From a642b2e11537c1f2f58376469a9d1e63f81272e8 Mon Sep 17 00:00:00 2001 From: pakjiddat Date: Wed, 22 Dec 2021 18:10:01 +0500 Subject: [PATCH] Disable caching in rmarkdown files - Update R version in DESCRIPTION file. - Caching needs to be disabled in README.rmd, features.Rmd and Overview.Rmd files. - Print statement in env-manager.R needs to be commented out. - Build website using pkgdown. - Update NEWS.md and cran-comments.md files. - Update link in codecov badge. --- .Rbuildignore | 1 + CRAN-RELEASE | 2 - CRAN-SUBMISSION | 3 + DESCRIPTION | 4 +- NEWS.md | 6 + R/env-manager.R | 2 +- README.Rmd | 12 +- README.md | 26 +- _pkgdown.yml | 2 + cran-comments.md | 10 +- docs/404.html | 192 +- docs/LICENSE-text.html | 179 +- docs/LICENSE.html | 179 +- docs/articles/features.html | 334 +- docs/articles/index.html | 182 +- docs/articles/man/figures/coverage.png | Bin 56053 -> 53316 bytes docs/articles/man/figures/top_features.png | Bin 64494 -> 62981 bytes docs/articles/overview.html | 295 +- 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.../figures/VIGNETTES-tokenization-3-1.png | Bin vignettes/reference/figures/coverage.png | Bin 0 -> 53316 bytes vignettes/reference/figures/top_features.png | Bin 0 -> 62981 bytes 65 files changed, 15035 insertions(+), 5387 deletions(-) delete mode 100644 CRAN-RELEASE create mode 100644 CRAN-SUBMISSION create mode 100644 docs/articles/reference/figures/coverage.png create mode 100644 docs/articles/reference/figures/top_features.png create mode 100644 docs/deps/bootstrap-5.1.0/bootstrap.bundle.min.js create mode 100644 docs/deps/bootstrap-5.1.0/bootstrap.bundle.min.js.map create mode 100644 docs/deps/bootstrap-5.1.0/bootstrap.min.css create mode 100644 docs/deps/data-deps.txt create mode 100644 docs/deps/jquery-3.6.0/jquery-3.6.0.js create mode 100644 docs/deps/jquery-3.6.0/jquery-3.6.0.min.js create mode 100644 docs/deps/jquery-3.6.0/jquery-3.6.0.min.map create mode 100644 docs/search.json delete mode 100644 vignettes/man/figures/coverage.png delete mode 100644 vignettes/man/figures/top_features.png rename vignettes/{man => reference}/figures/README-tokenization-2-1.png (100%) rename vignettes/{man => reference}/figures/README-tokenization-3-1.png (100%) rename vignettes/{man => reference}/figures/VIGNETTES-tokenization-2-1.png (100%) rename vignettes/{man => reference}/figures/VIGNETTES-tokenization-3-1.png (100%) create mode 100644 vignettes/reference/figures/coverage.png create mode 100644 vignettes/reference/figures/top_features.png diff --git a/.Rbuildignore b/.Rbuildignore index 5c20faf..2754745 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -13,3 +13,4 @@ ^vignettes/overview_cache$ ^CRAN-RELEASE$ ^codecov\.yml$ +^CRAN-SUBMISSION$ diff --git a/CRAN-RELEASE b/CRAN-RELEASE deleted file mode 100644 index 660d888..0000000 --- a/CRAN-RELEASE +++ /dev/null @@ -1,2 +0,0 @@ -This package was submitted to CRAN on 2021-06-18. -Once it is accepted, delete this file and tag the release (commit 021eb41). diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION new file mode 100644 index 0000000..8b5188e --- /dev/null +++ b/CRAN-SUBMISSION @@ -0,0 +1,3 @@ +Version: 0.0.3 +Date: 2022-01-03 10:44:59 UTC +SHA: e08f755a59570f644a442d01647e672e6a1a73f1 diff --git a/DESCRIPTION b/DESCRIPTION index 9bad7a4..18e8f8b 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: wordpredictor Title: Develop Text Prediction Models Based on N-Grams -Version: 0.0.2 +Version: 0.0.3 URL: https://github.com/pakjiddat/word-predictor, https://pakjiddat.github.io/word-predictor/ BugReports: https://github.com/pakjiddat/word-predictor/issues Authors@R: @@ -20,7 +20,7 @@ Description: A framework for developing n-gram models for text prediction. License: MIT + file LICENSE Encoding: UTF-8 Roxygen: list(markdown = TRUE) -RoxygenNote: 7.1.1 +RoxygenNote: 7.1.2 Imports: digest, ggplot2, patchwork, stringr, dplyr, SnowballC Suggests: testthat, diff --git a/NEWS.md b/NEWS.md index c1281d8..a67b5c4 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,9 @@ +# wordpredictor 0.0.3 + +## Bug fixes + + * Disabled caching in R Markdown files, because it was causing problems with CRAN checks. + # wordpredictor 0.0.2 ## Bug fixes diff --git a/R/env-manager.R b/R/env-manager.R index fd762bd..73e6f1a 100644 --- a/R/env-manager.R +++ b/R/env-manager.R @@ -184,7 +184,7 @@ EnvManager <- R6::R6Class( wp$ed <- ed # The wordpredictor options are updated options("wordpredictor" = wp) - print(list.files(system.file(package = "wordpredictor"))) + # print(list.files(system.file(package = "wordpredictor"))) # Each file is copied from extdata to the given folder for (fn in fns) { # The source file path diff --git a/README.Rmd b/README.Rmd index dcae618..883d533 100644 --- a/README.Rmd +++ b/README.Rmd @@ -47,7 +47,7 @@ clean_up <- function(ve) { [![R-CMD-check](https://github.com/pakjiddat/word-predictor/workflows/R-CMD-check/badge.svg)](https://github.com/pakjiddat/word-predictor/actions) [![lint](https://github.com/pakjiddat/word-predictor/workflows/lint/badge.svg)](https://github.com/pakjiddat/word-predictor/actions) [![test-coverage](https://github.com/pakjiddat/word-predictor/workflows/test-coverage/badge.svg)](https://github.com/pakjiddat/word-predictor/actions) -[![Codecov test coverage](https://codecov.io/gh/pakjiddat/word-predictor/branch/master/graph/badge.svg)](https://codecov.io/gh/pakjiddat/word-predictor?branch=master) +[![Codecov test coverage](https://codecov.io/gh/pakjiddat/word-predictor/branch/master/graph/badge.svg)](https://app.codecov.io/gh/pakjiddat/word-predictor?branch=master) [![CRAN version](https://www.r-pkg.org/badges/version/wordpredictor)](https://cran.r-project.org/package=wordpredictor) @@ -91,7 +91,7 @@ Information about the package can be obtained using the command line or the pack The following example shows how to generate a n-gram model. -```{r generate-model, cache=TRUE, results='hide'} +```{r generate-model, cache=FALSE, results='hide'} # The required files rf <- c("input.txt") # The test environment is setup @@ -131,7 +131,7 @@ The above code generates the file **def-model.RDS**. This file represents the n- The following example shows how to predict the next word given a set of words: -```{r predict-word, cache=TRUE} +```{r predict-word, cache=FALSE} # The required files rf <- c("def-model.RDS") # The test environment is setup @@ -183,7 +183,7 @@ clean_up(ve) The following example plots the n-gram frequency coverage. It shows the percentage of n-grams with frequency 1, 2 ... 10. -```{r analyze-ngrams-2, cache=TRUE, fig.width=6, fig.height=4} +```{r analyze-ngrams-2, cache=FALSE, fig.width=6, fig.height=4} # The required files rf <- c("n2.RDS") # The test environment is setup @@ -208,7 +208,7 @@ clean_up(ve) The following example shows how to get the list of bi-grams starting with **"great_"** along with their frequencies. It also shows how to get the frequency of the bi-gram **"great_deal"**. -```{r analyze-n-grams-3, cache=TRUE} +```{r analyze-n-grams-3, cache=FALSE} # The required files rf <- c("n2.RDS") # The test environment is setup @@ -294,7 +294,7 @@ Extrinsic evaluation measures the accuracy score for the sentences in a validati The following example shows how to evaluate the performance of a model: -```{r evaluate-performance-1, cache=TRUE, results='hide'} +```{r evaluate-performance-1, cache=FALSE, results='hide'} # The required files rf <- c("def-model.RDS", "validate.txt") # The test environment is setup diff --git a/README.md b/README.md index 31a854d..070ab54 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ [![lint](https://github.com/pakjiddat/word-predictor/workflows/lint/badge.svg)](https://github.com/pakjiddat/word-predictor/actions) [![test-coverage](https://github.com/pakjiddat/word-predictor/workflows/test-coverage/badge.svg)](https://github.com/pakjiddat/word-predictor/actions) [![Codecov test -coverage](https://codecov.io/gh/pakjiddat/word-predictor/branch/master/graph/badge.svg)](https://codecov.io/gh/pakjiddat/word-predictor?branch=master) +coverage](https://codecov.io/gh/pakjiddat/word-predictor/branch/master/graph/badge.svg)](https://app.codecov.io/gh/pakjiddat/word-predictor?branch=master) [![CRAN version](https://www.r-pkg.org/badges/version/wordpredictor)](https://cran.r-project.org/package=wordpredictor) @@ -106,7 +106,7 @@ mg <- ModelGenerator$new( # Generates n-gram model. The output is the file def-model.RDS mg$generate_model() -# The test envionment is cleaned up +# The test environment is cleaned up clean_up(ve) ``` @@ -133,7 +133,7 @@ mp <- ModelPredictor$new(mf = mfn) # next words are returned along with their respective probabilities. res <- mp$predict_word(words = "how are", 3) -# The test envionment is cleaned up +# The test environment is cleaned up clean_up(ve) ``` @@ -176,8 +176,7 @@ df <- da$plot_n_gram_stats(opts = list( ![](man/figures/README-analyze-ngrams-1-1.png) ``` r - -# The test envionment is cleaned up +# The test environment is cleaned up clean_up(ve) ``` @@ -207,14 +206,13 @@ df <- da$plot_n_gram_stats(opts = list( ![](man/figures/README-analyze-ngrams-2-1.png) ``` r - -# The test envionment is cleaned up +# The test environment is cleaned up clean_up(ve) ``` The following example shows how to get the list of bi-grams starting with **“great\_”** along with their frequencies. It also shows how to -get the frequency of the bi-gram **“great\_deal”**. +get the frequency of the bi-gram **“great_deal”**. ``` r # The required files @@ -234,18 +232,17 @@ df <- df[order(df$freq, decreasing = T),] # The frequency of the bi-gram "great_deal" f <- as.numeric(df[df$pre == "great_deal", "freq"]) -# The test envionment is cleaned up +# The test environment is cleaned up clean_up(ve) ``` ## Customizing the n-gram model -The **dc\_opts** parameter to the **ModelGenerator** class specifies the +The **dc_opts** parameter to the **ModelGenerator** class specifies the data cleaning options. The following code shows the data cleaning options and their default values: ``` r - # @field dc_opts The options for the data cleaner object. # min_words -> The minimum number of words per sentence. # line_count -> The number of lines to read and clean at a time. @@ -280,12 +277,11 @@ dc_opts = list( ) ``` -The **tg\_opts** parameter to the **ModelGenerator** class specifies the +The **tg_opts** parameter to the **ModelGenerator** class specifies the token generation options. The following code shows the token generation options and their default values: ``` r - # @field tg_opts The options for the token generator obj. # min_freq -> All ngrams with frequency less than min_freq are # ignored. @@ -331,7 +327,7 @@ me <- ModelEvaluator$new(mf = mfn, ve = 2) # a data frame and also saved within the model file itself. stats <- me$evaluate_performance(lc = 20, fn = vfn) -# The test envionment is cleaned up +# The test environment is cleaned up clean_up(ve) ``` @@ -388,7 +384,7 @@ biological sequence analysis, data compression and more. This will require further performance optimization. The source code is organized using R6 classes. It is easy to extend. -Contributions are welcome \!. +Contributions are welcome !. ## Acknowledgments diff --git a/_pkgdown.yml b/_pkgdown.yml index 27c3e94..ffc182b 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -2,3 +2,5 @@ url: https://pakjiddat.github.io/word-predictor title: Word Predictor development: mode: release +template: + bootstrap: 5 diff --git a/cran-comments.md b/cran-comments.md index 8fe83bb..d47f76f 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,14 +1,8 @@ ## Test environments - - local, ubuntu 20.04.2, R 3.6.3 - - github actions, macos-latest, R release - - github actions, windows-latest, R release - - github actions, ubuntu 20.04, R devel - - github actions, ubuntu 20.04, R release - - rhub, debian-clang-devel, R devel + - local, ubuntu 20.04.2, R 4.1.2 + - rhub, ubuntu 20.04.2, R release - rhub, fedora-clang-devel, R devel - - rhub, solaris-x86-patched, R release - - rhub, macos-highsierra-release, R release - winbuilder, windows, R devel ## R CMD check results diff --git a/docs/404.html b/docs/404.html index 2a68018..e948a8c 100644 --- a/docs/404.html +++ b/docs/404.html @@ -1,163 +1,97 @@ - - - - + + + + - - + Page not found (404) • Word Predictor - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + - - - - - - -
-
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+
+ + +
-
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-

Page not found (404)

+
+

Page not found (404)

+
Content not found. Please use links in the navbar. -
- - - +
- -
-
-

Developed by Nadir Latif.

+
+

+

Developed by Nadir Latif.

-
-

Site built with pkgdown 1.6.1.

+
+

+

Site built with pkgdown 2.0.1.

-
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+
+
+ - - diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index 4b2233b..d647ff2 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -1,165 +1,76 @@ - - - - - - - -License • Word Predictor - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +License • Word Predictor + Skip to contents + +
-
-
-

License

+
+

License

+ 
YEAR: 2021
 COPYRIGHT HOLDER: Nadir Latif
-
- - - -
- +
-
-
-

Developed by Nadir Latif.

+
+

Developed by Nadir Latif.

-
-

Site built with pkgdown 1.6.1.

+
+

Site built with pkgdown 2.0.1.

-
-
+
+ - - - + diff --git a/docs/LICENSE.html b/docs/LICENSE.html index 16d34a7..6d65212 100644 --- a/docs/LICENSE.html +++ b/docs/LICENSE.html @@ -1,131 +1,54 @@ - - - - - - - -MIT License • Word Predictor - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +MIT License • Word Predictor + Skip to contents + +
-
-
-

MIT License

+
+

MIT License

+ Source: LICENSE.md
@@ -136,34 +59,22 @@

MIT License

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

-
+
- +
+

Developed by Nadir Latif.

- - -
-
-

Developed by Nadir Latif.

+
+

Site built with pkgdown 2.0.1.

-
-

Site built with pkgdown 1.6.1.

-
- -
-
+
+ - - - + diff --git a/docs/articles/features.html b/docs/articles/features.html index 13658dd..922e5ea 100644 --- a/docs/articles/features.html +++ b/docs/articles/features.html @@ -4,115 +4,98 @@ - + + Features • Word Predictor - - - + + + - - + - -
-
-
-
-

Features

+
+
+

Features

- Source: vignettes/features.Rmd - - + Source: vignettes/features.Rmd +
features.Rmd
-
-

-Introduction

+
+

Introduction +

This document describes all the features provided by the wordpredictor package. It first describes how to generate n-gram models. Next it describes how to evaluate the performance of the n-gram models. Finally it describes how to make word predictions using the n-gram model.

-
-

-Model Generation

+
+

Model Generation +

The wordpredictor package provides several classes that can be used to generate n-gram models. These classes may be used to generate n-gram models step by step. An alternative is to use the ModelGenerator class which combines all the steps and provides a single method for generating n-gram models.

The following steps are involved in generating n-gram models:

-
-

-Data Exploration

+
+

Data Exploration +

The first step in generating a n-gram model is data exploration. This involves determining the type of textual content and various text related statistics. The type of text may be news content, blog posts, Twitter feeds, product reviews, customer chat history etc. Example of text related statistics are line count, word count, average line length and input file size.

It is also important to determine the unwanted words and symbols in the data such as vulgar words, punctuation symbols, non-alphabetical symbols etc. The wordpredictor package provides the DataAnalyzer class which can be used to find out statistics about the input data. The following example shows how to get statistics on all text files within a folder:

 # The required files
-rf <- c(
+rf <- c(
   "test.txt",
   "validate.txt",
   "validate-clean.txt",
@@ -120,22 +103,19 @@ 

 )
 # The test environment is setup
 ed <- setup_env(rf, ve)
-#>  [1] "CITATION"    "demo"        "DESCRIPTION" "examples"    "extdata"    
-#>  [6] "help"        "html"        "INDEX"       "LICENSE"     "Meta"       
-#> [11] "NAMESPACE"   "NEWS.md"     "R"
 
 # The DataAnalyzer object is created
 da <- DataAnalyzer$new(ve = ve)
 # Information on all text files in the ed folder is returned
 fi <- da$get_file_info(ed)
 # The file information is printed
-print(fi)
+print(fi)
 #> $file_stats
 #>                                   fn total_lc max_ll min_ll mean_ll   size
-#> 1     /tmp/RtmpmqAHFH/test-clean.txt       73     50     25      39 2.8 Kb
-#> 2           /tmp/RtmpmqAHFH/test.txt       73     51     28      41   3 Kb
-#> 3 /tmp/RtmpmqAHFH/validate-clean.txt       75     48     10      37 2.8 Kb
-#> 4       /tmp/RtmpmqAHFH/validate.txt       73     50     31      40 2.9 Kb
+#> 1     /tmp/RtmpoZ8ia5/test-clean.txt       73     50     25      39 2.8 Kb
+#> 2           /tmp/RtmpoZ8ia5/test.txt       73     51     28      41   3 Kb
+#> 3 /tmp/RtmpoZ8ia5/validate-clean.txt       75     48     10      37 2.8 Kb
+#> 4       /tmp/RtmpoZ8ia5/validate.txt       73     50     31      40 2.9 Kb
 #> 
 #> $overall_stats
 #>   total_lc max_ll min_ll mean_ll total_s
@@ -145,27 +125,24 @@ 

 clean_up(ve)

The word count of a text file can be fetched using the command: cat file-name | wc -w. This command should work on all Unix based systems.

-
-

-Data Sampling

+
+

Data Sampling +

The next step is to generate training, testing and validation samples from the input text file. If there are many input text files, then they can be combined to a single file using the command: cat file-1 file-2 file3 > output-file. The contents of the combined text file may need to be randomized.

The wordpredictor package provides the DataSampler class which can be used to generate a random sample containing given number of lines. The following example shows how to generate a random sample of size 10 Mb from an input text file:

 # The required files
-rf <- c("input.txt")
+rf <- c("input.txt")
 # The test environment is setup
 ed <- setup_env(rf, ve)
-#>  [1] "CITATION"    "demo"        "DESCRIPTION" "examples"    "extdata"    
-#>  [6] "help"        "html"        "INDEX"       "LICENSE"     "Meta"       
-#> [11] "NAMESPACE"   "NEWS.md"     "R"
 
 # The sample size as a proportion of the input.txt file
 ssize <- 0.1
 # The data file path
-dfp <- paste0(ed, "/input.txt")
+dfp <- paste0(ed, "/input.txt")
 
 # The object size is formatted
-obj_size <- file.size(dfp)/10^6
+obj_size <- file.size(dfp)/10^6
 # The proportion of data to sample
 prop <- (ssize/obj_size)
 # An object of class DataSampler is created
@@ -187,19 +164,16 @@ 

 
The data in the validation file must be different from the data in the train file. Otherwise it can result in over-fitting of the model. When a model is over-fitted, the model evaluation results will be exaggerated, overly optimistic and unreliable. So care should be taken to ensure that the data in the validation and train files is different.

 
 # The required files
-rf <- c("input.txt")
+rf <- c("input.txt")
 # The test environment is setup
 ed <- setup_env(rf, ve)
-#>  [1] "CITATION"    "demo"        "DESCRIPTION" "examples"    "extdata"    
-#>  [6] "help"        "html"        "INDEX"       "LICENSE"     "Meta"       
-#> [11] "NAMESPACE"   "NEWS.md"     "R"
 
 # An object of class DataSampler is created
 ds <- DataSampler$new(dir = ed, ve = ve)
 # The train, test and validation files are generated
 ds$generate_data(
     fn =  "input.txt",
-    percs = list(
+    percs = list(
         "train" = 0.8,
         "test" = 0.1,
         "validate" = 0.1
@@ -210,26 +184,23 @@ 

 clean_up(ve)

 
In the above example, dir parameter is the directory containing the input.txt file and the generated test, validation and train data files.
-
-

-Data Cleaning

+
+

Data Cleaning +

The next step is to remove unwanted symbols and words from the input text file. This reduces the memory requirement of the n-gram model and makes it more efficient. Example of unwanted words are vulgar words, words that are not part of the vocabulary, punctuation, numbers, non-printable characters and extra spaces.

The wordpredictor package provides the DataCleaner class which can be used to remove unwanted words and symbols from text files. The following example shows how to clean a given text file:

 # The required files
-rf <- c("input.txt")
+rf <- c("input.txt")
 # The test environment is setup
 ed <- setup_env(rf, ve)
-#>  [1] "CITATION"    "demo"        "DESCRIPTION" "examples"    "extdata"    
-#>  [6] "help"        "html"        "INDEX"       "LICENSE"     "Meta"       
-#> [11] "NAMESPACE"   "NEWS.md"     "R"
 
 # The data file path
-fn <- paste0(ed, "/input.txt")
+fn <- paste0(ed, "/input.txt")
 # The clean file path
-cfn <- paste0(ed, "/input-clean.txt")
+cfn <- paste0(ed, "/input-clean.txt")
 # The data cleaning options
-dc_opts = list(
+dc_opts = list(
     "min_words" = 2,
     "to_lower" = T,
     "remove_stop" = F,
@@ -249,25 +220,22 @@ 

 clean_up(ve)

The clean_file method reads a certain number of lines at a time, cleans the lines of text and saves them to an output text file. It can be used for cleaning large text files.

-
-

-Tokenization

+
+

Tokenization +

The next step is to generate n-gram tokens from the cleaned text file. The TokenGenerator class allows generating n-gram tokens of given size from a given input text file. The following example shows how to generate n-grams tokens of size 1,2,3 and 4:

 # The required files
-rf <- c("test-clean.txt")
+rf <- c("test-clean.txt")
 # The test environment is setup
 ed <- setup_env(rf, ve)
-#>  [1] "CITATION"    "demo"        "DESCRIPTION" "examples"    "extdata"    
-#>  [6] "help"        "html"        "INDEX"       "LICENSE"     "Meta"       
-#> [11] "NAMESPACE"   "NEWS.md"     "R"
 
 # The test file path
-fn <- paste0(ed, "/test-clean.txt")
+fn <- paste0(ed, "/test-clean.txt")
 # The n-grams are generated
 for (n in 1:4) {
   # The ngram number is set
-  tg_opts = list("n" = n, "save_ngrams" = T, dir = ed)
+  tg_opts = list("n" = n, "save_ngrams" = T, dir = ed)
   # The TokenGenerator object is created
   tg <- TokenGenerator$new(fn, tg_opts, ve = ve)
   # The ngram tokens are generated
@@ -282,29 +250,26 @@ 

 
The n-gram token frequencies may be analyzed using the DataAnalyzer class. The following example displays the top most occurring 2-gram tokens:

 
 # The required files
-rf <- c("n2.RDS")
+rf <- c("n2.RDS")
 # The test environment is setup
 ed <- setup_env(rf, ve)
-#>  [1] "CITATION"    "demo"        "DESCRIPTION" "examples"    "extdata"    
-#>  [6] "help"        "html"        "INDEX"       "LICENSE"     "Meta"       
-#> [11] "NAMESPACE"   "NEWS.md"     "R"
 
 # The ngram file name
-fn <- paste0(ed, "/n2.RDS")
+fn <- paste0(ed, "/n2.RDS")
 # The DataAnalyzer object is created
 da <- DataAnalyzer$new(fn, ve = ve)
 # The top features plot is checked
-df <- da$plot_n_gram_stats(opts = list(
+df <- da$plot_n_gram_stats(opts = list(
     "type" = "top_features",
     "n" = 10,
     "save_to" = "png",
-    "dir" = "./man/figures"
+    "dir" = "./reference/figures"
 ))
 
 # The output file path
-fn <- paste0("./man/figures/top_features.png")
-knitr::include_graphics(fn)

-

+fn <- paste0("./reference/figures/top_features.png")
+knitr::include_graphics(fn)
+

 
 # The test environment is cleaned up
@@ -312,29 +277,26 @@ 

 
The following example shows the distribution of word frequencies:

 
 # The required files
-rf <- c("n2.RDS")
+rf <- c("n2.RDS")
 # The test environment is setup
 ed <- setup_env(rf, ve)
-#>  [1] "CITATION"    "demo"        "DESCRIPTION" "examples"    "extdata"    
-#>  [6] "help"        "html"        "INDEX"       "LICENSE"     "Meta"       
-#> [11] "NAMESPACE"   "NEWS.md"     "R"
 
 # The ngram file name
-fn <- paste0(ed, "/n2.RDS")
+fn <- paste0(ed, "/n2.RDS")
 # The DataAnalyzer object is created
 da <- DataAnalyzer$new(fn, ve = ve)
 # The top features plot is checked
-df <- da$plot_n_gram_stats(opts = list(
+df <- da$plot_n_gram_stats(opts = list(
     "type" = "coverage",
     "n" = 10,
     "save_to" = "png",
-    "dir" = "./man/figures"
+    "dir" = "./reference/figures"
 ))
 
 # The output file path
-fn <- paste0("./man/figures/coverage.png")
-knitr::include_graphics(fn)

-

+fn <- paste0("./reference/figures/coverage.png")
+knitr::include_graphics(fn)
+

 
 # The test environment is cleaned up
@@ -342,23 +304,20 @@ 

 
The following example returns top 10 2-gram tokens that start with and_:

 
 # The required files
-rf <- c("n2.RDS")
+rf <- c("n2.RDS")
 # The test environment is setup
 ed <- setup_env(rf, ve)
-#>  [1] "CITATION"    "demo"        "DESCRIPTION" "examples"    "extdata"    
-#>  [6] "help"        "html"        "INDEX"       "LICENSE"     "Meta"       
-#> [11] "NAMESPACE"   "NEWS.md"     "R"
 
 # The ngram file name
-fn <- paste0(ed, "/n2.RDS")
+fn <- paste0(ed, "/n2.RDS")
 # The DataAnalyzer object is created
 da <- DataAnalyzer$new(ve = ve)
 # Bi-grams starting with "and_" are returned
 df <- da$get_ngrams(fn = fn, c = 10, pre = "^and_*")
 # The data frame is sorted by frequency
-df <- df[order(df$freq, decreasing = T),]
+df <- df[order(df$freq, decreasing = T),]
 # The first 10 rows of the data frame are printed
-knitr::kable(df[1:10,], col.names = c("Prefix", "Frequency"))

+knitr::kable(df[1:10,], col.names = c("Prefix", "Frequency"))
@@ -412,22 +371,19 @@

# The test environment is cleaned up clean_up(ve) -
-

-Transition Probabilities

+
+

Transition Probabilities +

The next step in generating the n-gram model is to generate transition probabilities (tp) from the n-gram files. The TPGenerator class is used to generate the tps. For each n-gram token file a corresponding tp file is generated.

The tp files are then combined into a single file containing tp data for n-grams of size 1, 2, 3, 4 etc.

The following example shows how to generate combined tps for n-grams of size 1, 2, 3 and 4:

 # The required files
-rf <- c("n1.RDS", "n2.RDS", "n3.RDS", "n4.RDS")
+rf <- c("n1.RDS", "n2.RDS", "n3.RDS", "n4.RDS")
 # The test environment is setup
 ed <- setup_env(rf, ve)
-#>  [1] "CITATION"    "demo"        "DESCRIPTION" "examples"    "extdata"    
-#>  [6] "help"        "html"        "INDEX"       "LICENSE"     "Meta"       
-#> [11] "NAMESPACE"   "NEWS.md"     "R"
 # The TPGenerator object is created
-tp <- TPGenerator$new(opts = list(n = 4, dir = ed), ve = ve)
+tp <- TPGenerator$new(opts = list(n = 4, dir = ed), ve = ve)
 # The combined transition probabilities are generated
 tp$generate_tp()
 
@@ -435,18 +391,18 @@ 

 clean_up(ve)

The above code produces the file model-4.RDS.

-
-

-The model file

+
+

The model file +

The final step is to generate a n-gram model file from the files generated in the previous steps. The Model class contains the method load_model, which reads the combined tps files and other files that are used by the model. An instance of the Model class represents the n-gram model.

-
-

-Generating the model in one step

+
+

Generating the model in one step +

All the previous steps may be combined into a single step. The ModelGenerator class allows generating the final n-gram model using a single method call. The following example generates a n-gram model using default data cleaning and tokenization options:

 # The required files
-rf <- c("input.txt")
+rf <- c("input.txt")
 # The test environment is setup
 ed <- setup_env(rf, ve)
 
@@ -464,8 +420,8 @@ 

     n = 4,
     ssize = 0.1,
     dir = ed,
-    dc_opts = list(),
-    tg_opts = list(),
+    dc_opts = list(),
+    tg_opts = list(),
     ve = ve
 )
 
@@ -476,24 +432,21 @@ 

 clean_up(ve)
-
-

-Evaluating the model performance

+
+

Evaluating the model performance +

The wordpredictor package provides the ModelEvaluator class for evaluating the performance of the generated n-gram model. Intrinsic and Extrinsic evaluation are supported. Also the performance of several n-gram models may be compared.

The following example performs Intrinsic evaluation. It measures the Perplexity score for each sentence in the validation.txt file, that was generated in the data sampling step. It returns the minimum, mean and maximum Perplexity score for each line.

 # The required files
-rf <- c("def-model.RDS", "validate-clean.txt")
+rf <- c("def-model.RDS", "validate-clean.txt")
 # The test environment is setup
 ed <- setup_env(rf, ve)
-#>  [1] "CITATION"    "demo"        "DESCRIPTION" "examples"    "extdata"    
-#>  [6] "help"        "html"        "INDEX"       "LICENSE"     "Meta"       
-#> [11] "NAMESPACE"   "NEWS.md"     "R"
 
 # The model file name
-mfn <- paste0(ed, "/def-model.RDS")
+mfn <- paste0(ed, "/def-model.RDS")
 # The path to the cleaned validation file
-vfn <- paste0(ed, "/validate-clean.txt")
+vfn <- paste0(ed, "/validate-clean.txt")
 # ModelEvaluator class object is created
 me <- ModelEvaluator$new(mf = mfn, ve = ve)
 # The intrinsic evaluation is performed on first 20 lines
@@ -504,17 +457,14 @@ 

 
The following example performs Extrinsic evaluation. It measures the accuracy score for each sentence in validation.txt file. For each sentence the model is used to predict the last word in the sentence given the previous words. If the last word was correctly predicted, then the prediction is considered to be accurate.

 
 # The required files
-rf <- c("def-model.RDS", "validate-clean.txt")
+rf <- c("def-model.RDS", "validate-clean.txt")
 # The test environment is setup
 ed <- setup_env(rf, ve)
-#>  [1] "CITATION"    "demo"        "DESCRIPTION" "examples"    "extdata"    
-#>  [6] "help"        "html"        "INDEX"       "LICENSE"     "Meta"       
-#> [11] "NAMESPACE"   "NEWS.md"     "R"
 
 # The model file name
-mfn <- paste0(ed, "/def-model.RDS")
+mfn <- paste0(ed, "/def-model.RDS")
 # The path to the cleaned validation file
-vfn <- paste0(ed, "/validate-clean.txt")
+vfn <- paste0(ed, "/validate-clean.txt")
 # ModelEvaluator class object is created
 me <- ModelEvaluator$new(mf = mfn, ve = ve)
 # The intrinsic evaluation is performed on first 100 lines
@@ -523,21 +473,18 @@ 

 # The test environment is cleaned up
 clean_up(ve)
-
-

-Making word predictions

+
+

Making word predictions +

The n-gram model generated in the previous step can be used to predict the next word given a set of words. The following example shows how to predict the next word. It returns the 3 possible next words along with their probabilities.

 # The required files
-rf <- c("def-model.RDS")
+rf <- c("def-model.RDS")
 # The test environment is setup
 ed <- setup_env(rf, ve)
-#>  [1] "CITATION"    "demo"        "DESCRIPTION" "examples"    "extdata"    
-#>  [6] "help"        "html"        "INDEX"       "LICENSE"     "Meta"       
-#> [11] "NAMESPACE"   "NEWS.md"     "R"
 
 # The model file name
-mfn <- paste0(ed, "/def-model.RDS")
+mfn <- paste0(ed, "/def-model.RDS")
 # An object of class ModelPredictor is created. The mf parameter is the name of
 # the model file that was generated in the previous example.
 mp <- ModelPredictor$new(mf = mfn, ve = ve)
@@ -548,31 +495,28 @@ 

 # The test environment is cleaned up
 clean_up(ve)
-
- - - +
-
-

Developed by Nadir Latif.

+
+

+

Developed by Nadir Latif.

-
-

Site built with pkgdown 1.6.1.

+
+

+

Site built with pkgdown 2.0.1.

-
+
+ diff --git a/docs/articles/index.html b/docs/articles/index.html index f0d566b..ed1b94c 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -1,164 +1,80 @@ - - - - - - - -Articles • Word Predictor - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Articles • Word Predictor + Skip to contents + +
-
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-

Articles

+
+

Articles

All vignettes

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Features
-
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Overview
-
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+
Features
+
+
Overview
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+
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-
-

Developed by Nadir Latif.

+
+

Developed by Nadir Latif.

-
-

Site built with pkgdown 1.6.1.

+
+

Site built with pkgdown 2.0.1.

-
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zZz(iqGdEn1++2aG7hiM7h?@*hHEA^Pz0|bq<^%n=A+R~?K{L>+*t4y#_}!j={qBOT zTemXByldAjQdhWnqAaD(ONfpA?dx@MIod;Z-5il^$6vYpHCvIWo^h(M-6;O+N4K8| zgZNh0J5$GsKgHLLkmi#?@*iG(e=_g?$ACPYE=c_dlGvaD@h6Bq{51TMKag~>_E%N; QZ;<~yUHx3vIVCg!0QK-OVgLXD diff --git a/docs/articles/overview.html b/docs/articles/overview.html index 7965272..f741cf6 100644 --- a/docs/articles/overview.html +++ b/docs/articles/overview.html @@ -4,148 +4,131 @@ - + + Overview • Word Predictor - - - + + + - - + - -
-
-
-
-

Overview

+
+
+

Overview

- Source: vignettes/overview.Rmd - - + Source: vignettes/overview.Rmd +
overview.Rmd
-
-

-Introduction

+
+

Introduction +

This document describes the theory behind the n-gram models generated by the wordpredictor package. It also provides code examples that describe how to use the package.

-

The goal of the wordpredictor package is to provide a flexible and easy to use framework for generating n-gram models for word prediction.

-

The package allows generating n-gram models. It also allows exploring n-gram frequencies using plots. Additionally it provides methods for measuring n-gram model performance using Perplexity and accuracy.

+

The goal of the wordpredictor package is to provide a flexible and easy to use framework for generating n-gram models for word prediction.

+

The package allows generating n-gram models. It also allows exploring n-gram frequencies using plots. Additionally it provides methods for measuring n-gram model performance using Perplexity and accuracy.

The n-gram model may be customized using several options such as n-gram size, data cleaning options and options for converting text to tokens.

-
-

-How the model works

-

The n-gram model generated by the wordpredictor package uses the Markov model for approximating the language model. It means that the probability of a word depends only on the probability of the n-1 previous words.

+
+

How the model works +

+

The n-gram model generated by the wordpredictor package uses the Markov model for approximating the language model. It means that the probability of a word depends only on the probability of the n-1 previous words.

Maximum Likelihood Estimation (MLE) is used to calculate the probability of a word. The probability of a word is calculated by regarding the word as the last component of a n-gram. The total number of occurrences of the n-gram is divided by the total number of occurrences of the (n-1)-gram. This gives the probability for the word.

The n-gram model is generated in steps. In the first step, the input data is cleaned. Unwanted symbols and words are removed from the input data.

In the next step, the cleaned data file is read. N-grams are extracted from the file, starting from 1-grams up to the configured n-gram size. The 1-gram, 2-gram, 3-gram etc tokens are saved in separate files along with the frequency. So the 3-gram file contains all extracted 3-grams and their respective frequencies.

The next step is to generate transition probability tables for each n-gram file. For the 1-gram file the transition probability table is simply the list of unique words along with the word frequencies. For the other n-gram files, the transition probability table is a data frame with 3 columns. The hash of n-gram prefixes, the next word id and the next word probability.

-

The n-gram prefix is the set of n-1 components before the last component. The n-1 components are combined using "_" and converted to a numeric hash value using the digest2Int method of the digest package.

+

The n-gram prefix is the set of n-1 components before the last component. The n-1 components are combined using “_” and converted to a numeric hash value using the digest2Int method of the digest package.

The next word id is the numeric index of the next word in the list of 1-grams. The next word probability is the probability of the next word given the previous n-1 words. It is calculated using Maximum Likelihood Estimation (MLE) as described above.

Instead of storing the n-gram prefix strings, a single number is saved. Also instead of storing the next word, the numeric index of the next word is saved. This saves a lot of memory and allows more data to be stored, which improves the n-gram model’s efficiency.

In R, a number requires a fixed amount of storage, which about 56 bytes. In contrast the memory required to store a string increases with the number of characters in the string.

The data frames that represent each transition probability table are combined into a single data frame. The combined transition probability table is used to make word predictions.

-
-

-Using the model to predict words

+
+

Using the model to predict words +

To predict a word, the word along with the n-1 previous words are used as input. The model computes the hash of the previous words and looks up the hash in the combined transition probabilities table. If the hash was found, then the model extracts the top 3 next word ids that have the highest probabilities.

The model looks up the next word text that corresponds to the next word ids. The result is the top 3 most likely next words along with their probabilities.

If the hash was not found, then the hash of the n-2 previous words is calculated and looked up in the combined transition probabilities table.

This process is repeated until there are no previous words. When this happens, the model returns a “word not found” message.

This method of checking the transition probabilities of lower level n-grams is called back-off. An alternate method of predicting a word is to use interpolation. This involves weighing and summing the probabilities for each n-gram size.

-
-

-Predicting the model performance

+
+

Predicting the model performance +

The wordpredictor package provides methods for performing intrinsic and extrinsic evaluation of the n-gram model.

The wordpredictor package performs intrinsic evaluation by calculating the mean Perplexity score for all sentences in a validation text file. The Perplexity for a sentence is calculated by taking the N-th root of the inverse of the product of probabilities of all words in a sentence. N is the number of words in the sentence.

The probability of a word is calculated by considering all n-1 words before that word. If the word was not found in the transition probabilities table, then the n-2 words are looked up. This process is repeated until there are no previous words.

If the word was found in the 1-gram list, then the probability of the word is calculated by simply dividing the number of times the word occurs by the total number words.

-

If the word was not found in the 1-gram list, then the model uses a default probability as the probability of the word. The default probability is calculated using Laplace Smoothing.

+

If the word was not found in the 1-gram list, then the model uses a default probability as the probability of the word. The default probability is calculated using Laplace Smoothing.

Laplace Smoothing involves adding 1 to the frequency count of each word in the vocabulary. Essentially this means that the total number of words in the data set are increased by vc, where vc is the number of words in the vocabulary.

In Laplace Smoothing 1 is added to the word count. Since an unknown word occurs zero times, after Laplace Smoothing it will have a count of 1. So the default probability is calculated as: P(unk) = 1/(N+VC), where N is the total number of words in the data set and VC is the number of words in the vocabulary. This default probability is assigned to unknown words. Alternative methods to Laplace Smoothing are Add-k smoothing, Kneser-Ney smoothing and Good-Turing Smoothing.

The wordpredictor package uses the file /usr/share/dict/cracklib-small as the dictionary file. This file is pre-installed in most Linux distributions.

Extrinsic evaluation involves calculating the accuracy score. The model tries to predict the last word of a sentence. If the actual last word was one of the 3 words predicted by the model, then the prediction is considered to be accurate. The accuracy score is the number of sentences that were correctly predicted.

-
-

-Generating the model

+
+

Generating the model +

The ModelGenerator class allows generating the final n-gram model using a single method call. The following example generates a n-gram model using default data cleaning and tokenization options:

 # The required files
-rf <- c("input.txt")
+rf <- c("input.txt")
 # The test environment is setup
 ed <- setup_env(rf, ve)
 
@@ -164,8 +147,8 @@ 

     n = 4,
     ssize = 0.1,
     dir = ed,
-    dc_opts = list(),
-    tg_opts = list(),
+    dc_opts = list(),
+    tg_opts = list(),
     ve = ve
 )
 
@@ -176,24 +159,21 @@ 

 # The test environment is cleaned up
 clean_up(ve)
-
-

-Evaluating the model performance

+
+

Evaluating the model performance +

The wordpredictor package provides the ModelEvaluator class for evaluating the performance of the generated n-gram model.

The following example performs intrinsic evaluation. It measures the Perplexity score for each sentence in the validation.txt file. It returns the minimum, mean and maximum Perplexity score for each line.

 # The required files
-rf <- c("def-model.RDS", "validate-clean.txt")
+rf <- c("def-model.RDS", "validate-clean.txt")
 # The test environment is setup
 ed <- setup_env(rf, ve)
-#>  [1] "CITATION"    "demo"        "DESCRIPTION" "examples"    "extdata"    
-#>  [6] "help"        "html"        "INDEX"       "LICENSE"     "Meta"       
-#> [11] "NAMESPACE"   "NEWS.md"     "R"
 
 # The model file name
-mfn <- paste0(ed, "/def-model.RDS")
+mfn <- paste0(ed, "/def-model.RDS")
 # The path to the cleaned validation file
-vfn <- paste0(ed, "/validate-clean.txt")
+vfn <- paste0(ed, "/validate-clean.txt")
 # ModelEvaluator class object is created
 me <- ModelEvaluator$new(mf = mfn, ve = ve)
 # The intrinsic evaluation is performed on first 20 lines
@@ -204,17 +184,14 @@ 

 
The following example performs extrinsic evaluation. It measures the accuracy score for each sentence in validation.txt file. For each sentence the model is used to predict the last word in the sentence given the previous words. If the last word was correctly predicted, then the prediction is considered to be accurate. The extrinsic evaluation returns the number of correct and incorrect predictions.

 
 # The required files
-rf <- c("def-model.RDS", "validate-clean.txt")
+rf <- c("def-model.RDS", "validate-clean.txt")
 # The test environment is setup
 ed <- setup_env(rf, ve)
-#>  [1] "CITATION"    "demo"        "DESCRIPTION" "examples"    "extdata"    
-#>  [6] "help"        "html"        "INDEX"       "LICENSE"     "Meta"       
-#> [11] "NAMESPACE"   "NEWS.md"     "R"
 
 # The model file name
-mfn <- paste0(ed, "/def-model.RDS")
+mfn <- paste0(ed, "/def-model.RDS")
 # The path to the cleaned validation file
-vfn <- paste0(ed, "/validate-clean.txt")
+vfn <- paste0(ed, "/validate-clean.txt")
 # ModelEvaluator class object is created
 me <- ModelEvaluator$new(mf = mfn, ve = ve)
 # The intrinsic evaluation is performed on first 100 lines
@@ -223,21 +200,18 @@ 

 # The test environment is cleaned up
 clean_up(ve)
-
-

-How to predict a word

+
+

How to predict a word +

The following example shows how to predict the next word. It returns the 3 possible next words along with their probabilities.

 # The required files
-rf <- c("def-model.RDS", "validate-clean.txt")
+rf <- c("def-model.RDS", "validate-clean.txt")
 # The test environment is setup
 ed <- setup_env(rf, ve)
-#>  [1] "CITATION"    "demo"        "DESCRIPTION" "examples"    "extdata"    
-#>  [6] "help"        "html"        "INDEX"       "LICENSE"     "Meta"       
-#> [11] "NAMESPACE"   "NEWS.md"     "R"
 
 # The model file name
-mfn <- paste0(ed, "/def-model.RDS")
+mfn <- paste0(ed, "/def-model.RDS")
 # An object of class ModelPredictor is created. The mf parameter is the name of
 # the model file that was generated in the previous example.
 mp <- ModelPredictor$new(mf = mfn, ve = ve)
@@ -247,87 +221,84 @@ 

 # The test environment is cleaned up
 clean_up(ve)
-
-

-Demo

-

The wordpredictor package includes a demo called “word-predictor”. The demo is a Shiny application that displays the ten most likely words for a given set of words. To access the demo, run the following command from the R shell:

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demo("word-predictor", package = "wordpredictor", ask = F).

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Demo +

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The wordpredictor package includes a demo called “word-predictor.” The demo is a Shiny application that displays the ten most likely words for a given set of words. To access the demo, run the following command from the R shell:

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demo("word-predictor", package = "wordpredictor", ask = F).

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-Package dependencies

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The wordpredictor package uses the following packages: digest, dply, ggplot2, R6, testthat and stingr

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The following packages were useful during package development: quanteda, tm and hash lintr styler pkgdown pryr,

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Package dependencies +

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The wordpredictor package uses the following packages: digest, dply, ggplot2, R6, testthat and stingr

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The following packages were useful during package development: quanteda, tm and hash lintr styler pkgdown pryr,

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Developed by Nadir Latif.

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Developed by Nadir Latif.

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Site built with pkgdown 1.6.1.

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Site built with pkgdown 2.0.1.

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literal 0 HcmV?d00001 diff --git a/docs/authors.html b/docs/authors.html index 6808b4b..823885d 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -1,179 +1,97 @@ - - - - - - - -Citation and Authors • Word Predictor - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Authors and Citation • Word Predictor + Skip to contents + +
-
-
-

Citation

- Source: inst/CITATION +
+

Authors and Citation

-

Latif N (2021). +

+

Authors

+ +
  • +

    Nadir Latif. Author, maintainer. +

    +
    • +
+ +
+

Citation

+

Source: inst/CITATION

+ +

Latif N (2021). Develop Text Prediction Models Based on N-Grams. -R package version 1.0.0, https://github.com/pakjiddat/word-predictor. +R package version 1.0.0, https://github.com/pakjiddat/word-predictor.

- 
@Manual{,
+      
@Manual{,
   title = {Develop Text Prediction Models Based on N-Grams},
   author = {Nadir Latif},
   year = {2021},
   note = {R package version 1.0.0},
   url = {https://github.com/pakjiddat/word-predictor},
 }

-
-    

-      
Authors

     

+
-
    -
  • -

    Nadir Latif. Author, maintainer. -

    -
    • -
- -
+
+

Developed by Nadir Latif.

- - -
-
-

Developed by Nadir Latif.

-
- -
-

Site built with pkgdown 1.6.1.

+
+

Site built with pkgdown 2.0.1.

-
-
+
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clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=U.find('[data-bs-toggle="dropdown"]');for(let i=0,n=e.length;ie+t),this._setElementAttributes(".fixed-top, .fixed-bottom, .is-fixed, .sticky-top","paddingRight",e=>e+t),this._setElementAttributes(".sticky-top","marginRight",e=>e-t)}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=i(Number.parseFloat(s))+"px"})}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(".fixed-top, .fixed-bottom, .is-fixed, .sticky-top","paddingRight"),this._resetElementAttributes(".sticky-top","marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,t=>{const i=F.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(F.removeDataAttribute(t,e),t.style[e]=i)})}_applyManipulationCallback(t,e){s(t)?e(t):U.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const Ce={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},ke={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"};class Le{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&d(this._getElement()),this._getElement().classList.add("show"),this._emulateAnimation(()=>{g(t)})):g(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove("show"),this._emulateAnimation(()=>{this.dispose(),g(t)})):g(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...Ce,..."object"==typeof t?t:{}}).rootElement=o(t.rootElement),r("backdrop",t,ke),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),P.on(this._getElement(),"mousedown.bs.backdrop",()=>{g(this._config.clickCallback)}),this._isAppended=!0)}dispose(){this._isAppended&&(P.off(this._element,"mousedown.bs.backdrop"),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const xe={trapElement:null,autofocus:!0},De={trapElement:"element",autofocus:"boolean"};class Se{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),P.off(document,".bs.focustrap"),P.on(document,"focusin.bs.focustrap",t=>this._handleFocusin(t)),P.on(document,"keydown.tab.bs.focustrap",t=>this._handleKeydown(t)),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,P.off(document,".bs.focustrap"))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=U.focusableChildren(i);0===n.length?i.focus():"backward"===this._lastTabNavDirection?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?"backward":"forward")}_getConfig(t){return t={...xe,..."object"==typeof t?t:{}},r("focustrap",t,De),t}}const Ne={backdrop:!0,keyboard:!0,focus:!0},Ie={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"};class Pe extends H{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=U.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new Oe}static get Default(){return Ne}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||P.trigger(this._element,"show.bs.modal",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add("modal-open"),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),P.on(this._dialog,"mousedown.dismiss.bs.modal",()=>{P.one(this._element,"mouseup.dismiss.bs.modal",t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)})}),this._showBackdrop(()=>this._showElement(t)))}hide(){if(!this._isShown||this._isTransitioning)return;if(P.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove("show"),P.off(this._element,"click.dismiss.bs.modal"),P.off(this._dialog,"mousedown.dismiss.bs.modal"),this._queueCallback(()=>this._hideModal(),this._element,t)}dispose(){[window,this._dialog].forEach(t=>P.off(t,".bs.modal")),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Le({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Se({trapElement:this._element})}_getConfig(t){return t={...Ne,...F.getDataAttributes(this._element),..."object"==typeof t?t:{}},r("modal",t,Ie),t}_showElement(t){const e=this._isAnimated(),i=U.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&d(this._element),this._element.classList.add("show"),this._queueCallback(()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,P.trigger(this._element,"shown.bs.modal",{relatedTarget:t})},this._dialog,e)}_setEscapeEvent(){this._isShown?P.on(this._element,"keydown.dismiss.bs.modal",t=>{this._config.keyboard&&"Escape"===t.key?(t.preventDefault(),this.hide()):this._config.keyboard||"Escape"!==t.key||this._triggerBackdropTransition()}):P.off(this._element,"keydown.dismiss.bs.modal")}_setResizeEvent(){this._isShown?P.on(window,"resize.bs.modal",()=>this._adjustDialog()):P.off(window,"resize.bs.modal")}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide(()=>{document.body.classList.remove("modal-open"),this._resetAdjustments(),this._scrollBar.reset(),P.trigger(this._element,"hidden.bs.modal")})}_showBackdrop(t){P.on(this._element,"click.dismiss.bs.modal",t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())}),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(P.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains("modal-static")||(n||(i.overflowY="hidden"),t.add("modal-static"),this._queueCallback(()=>{t.remove("modal-static"),n||this._queueCallback(()=>{i.overflowY=""},this._dialog)},this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!p()||i&&!t&&p())&&(this._element.style.paddingLeft=e+"px"),(i&&!t&&!p()||!i&&t&&p())&&(this._element.style.paddingRight=e+"px")}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Pe.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}P.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=i(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),P.one(e,"show.bs.modal",t=>{t.defaultPrevented||P.one(e,"hidden.bs.modal",()=>{a(this)&&this.focus()})}),Pe.getOrCreateInstance(e).toggle(this)})),B(Pe),m(Pe);const je={backdrop:!0,keyboard:!0,scroll:!1},Me={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"};class He extends H{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return"offcanvas"}static get Default(){return je}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||P.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new Oe).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add("show"),this._queueCallback(()=>{this._config.scroll||this._focustrap.activate(),P.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})},this._element,!0))}hide(){this._isShown&&(P.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove("show"),this._backdrop.hide(),this._queueCallback(()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new Oe).reset(),P.trigger(this._element,"hidden.bs.offcanvas")},this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...je,...F.getDataAttributes(this._element),..."object"==typeof t?t:{}},r("offcanvas",t,Me),t}_initializeBackDrop(){return new Le({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Se({trapElement:this._element})}_addEventListeners(){P.on(this._element,"keydown.dismiss.bs.offcanvas",t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()})}static jQueryInterface(t){return this.each((function(){const e=He.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}P.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=i(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;P.one(e,"hidden.bs.offcanvas",()=>{a(this)&&this.focus()});const n=U.findOne(".offcanvas.show");n&&n!==e&&He.getInstance(n).hide(),He.getOrCreateInstance(e).toggle(this)})),P.on(window,"load.bs.offcanvas.data-api",()=>U.find(".offcanvas.show").forEach(t=>He.getOrCreateInstance(t).show())),B(He),m(He);const Be=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Re=/^(?:(?:https?|mailto|ftp|tel|file):|[^#&/:?]*(?:[#/?]|$))/i,We=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,ze=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Be.has(i)||Boolean(Re.test(t.nodeValue)||We.test(t.nodeValue));const n=e.filter(t=>t instanceof RegExp);for(let t=0,e=n.length;t{ze(t,a)||i.removeAttribute(t.nodeName)})}return n.body.innerHTML}const Fe=new Set(["sanitize","allowList","sanitizeFn"]),Ue={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},$e={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},Ve={animation:!0,template:'',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},Ke={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"};class Xe extends H{constructor(t,e){if(void 0===pe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return Ve}static get NAME(){return"tooltip"}static get Event(){return Ke}static get DefaultType(){return Ue}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains("show"))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),P.off(this._element.closest(".modal"),"hide.bs.modal",this._hideModalHandler),this.tip&&this.tip.remove(),this._popper&&this._popper.destroy(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=P.trigger(this._element,this.constructor.Event.SHOW),e=c(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add("fade");const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;M.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),P.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=fe(this._element,n,this._getPopperConfig(r)),n.classList.add("show");const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach(t=>{P.on(t,"mouseover",h)});const d=this.tip.classList.contains("fade");this._queueCallback(()=>{const t=this._hoverState;this._hoverState=null,P.trigger(this._element,this.constructor.Event.SHOWN),"out"===t&&this._leave(null,this)},this.tip,d)}hide(){if(!this._popper)return;const t=this.getTipElement();if(P.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove("show"),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach(t=>P.off(t,"mouseover",h)),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains("fade");this._queueCallback(()=>{this._isWithActiveTrigger()||("show"!==this._hoverState&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),P.trigger(this._element,this.constructor.Event.HIDDEN),this._popper&&(this._popper.destroy(),this._popper=null))},this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove("fade","show"),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".tooltip-inner")}_sanitizeAndSetContent(t,e,i){const n=U.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return s(e)?(e=o(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=qe(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map(t=>Number.parseInt(t,10)):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return $e[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach(t=>{if("click"===t)P.on(this._element,this.constructor.Event.CLICK,this._config.selector,t=>this.toggle(t));else if("manual"!==t){const e="hover"===t?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i="hover"===t?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;P.on(this._element,e,this._config.selector,t=>this._enter(t)),P.on(this._element,i,this._config.selector,t=>this._leave(t))}}),this._hideModalHandler=()=>{this._element&&this.hide()},P.on(this._element.closest(".modal"),"hide.bs.modal",this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?"focus":"hover"]=!0),e.getTipElement().classList.contains("show")||"show"===e._hoverState?e._hoverState="show":(clearTimeout(e._timeout),e._hoverState="show",e._config.delay&&e._config.delay.show?e._timeout=setTimeout(()=>{"show"===e._hoverState&&e.show()},e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?"focus":"hover"]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState="out",e._config.delay&&e._config.delay.hide?e._timeout=setTimeout(()=>{"out"===e._hoverState&&e.hide()},e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=F.getDataAttributes(this._element);return Object.keys(e).forEach(t=>{Fe.has(t)&&delete e[t]}),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:o(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),r("tooltip",t,this.constructor.DefaultType),t.sanitize&&(t.template=qe(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new 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element.getAttribute('data-bs-target')\n\n if (!selector || selector === '#') {\n let hrefAttr = element.getAttribute('href')\n\n // The only valid content that could double as a selector are IDs or classes,\n // so everything starting with `#` or `.`. 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'element' : toType(value)\n\n if (!new RegExp(expectedTypes).test(valueType)) {\n throw new TypeError(\n `${componentName.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`\n )\n }\n })\n}\n\nconst isVisible = element => {\n if (!isElement(element) || element.getClientRects().length === 0) {\n return false\n }\n\n return getComputedStyle(element).getPropertyValue('visibility') === 'visible'\n}\n\nconst isDisabled = element => {\n if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n return true\n }\n\n if (element.classList.contains('disabled')) {\n return true\n }\n\n if (typeof element.disabled !== 'undefined') {\n return element.disabled\n }\n\n return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false'\n}\n\nconst findShadowRoot = element => {\n if (!document.documentElement.attachShadow) {\n return null\n }\n\n // Can find the shadow root otherwise it'll return the document\n if (typeof element.getRootNode === 'function') {\n const root = element.getRootNode()\n return root instanceof ShadowRoot ? root : null\n }\n\n if (element instanceof ShadowRoot) {\n return element\n }\n\n // when we don't find a shadow root\n if (!element.parentNode) {\n return null\n }\n\n return findShadowRoot(element.parentNode)\n}\n\nconst noop = () => {}\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n // eslint-disable-next-line no-unused-expressions\n element.offsetHeight\n}\n\nconst getjQuery = () => {\n const { jQuery } = window\n\n if (jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n return jQuery\n }\n\n return null\n}\n\nconst DOMContentLoadedCallbacks = []\n\nconst onDOMContentLoaded = callback => {\n if (document.readyState === 'loading') {\n // add listener on the first call when the document is in loading state\n if (!DOMContentLoadedCallbacks.length) {\n document.addEventListener('DOMContentLoaded', () => {\n DOMContentLoadedCallbacks.forEach(callback => callback())\n })\n }\n\n DOMContentLoadedCallbacks.push(callback)\n } else {\n callback()\n }\n}\n\nconst isRTL = () => document.documentElement.dir === 'rtl'\n\nconst defineJQueryPlugin = plugin => {\n onDOMContentLoaded(() => {\n const $ = getjQuery()\n /* istanbul ignore if */\n if ($) {\n const name = plugin.NAME\n const JQUERY_NO_CONFLICT = $.fn[name]\n $.fn[name] = plugin.jQueryInterface\n $.fn[name].Constructor = plugin\n $.fn[name].noConflict = () => {\n $.fn[name] = JQUERY_NO_CONFLICT\n return plugin.jQueryInterface\n }\n }\n })\n}\n\nconst execute = callback => {\n if (typeof callback === 'function') {\n callback()\n }\n}\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n if (!waitForTransition) {\n execute(callback)\n return\n }\n\n const durationPadding = 5\n const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding\n\n let called = false\n\n const handler = ({ target }) => {\n if (target !== transitionElement) {\n return\n }\n\n called = true\n transitionElement.removeEventListener(TRANSITION_END, handler)\n execute(callback)\n }\n\n transitionElement.addEventListener(TRANSITION_END, handler)\n setTimeout(() => {\n if (!called) {\n triggerTransitionEnd(transitionElement)\n }\n }, emulatedDuration)\n}\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list The list of elements\n * @param activeElement The active element\n * @param shouldGetNext Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n let index = list.indexOf(activeElement)\n\n // if the element does not exist in the list return an element depending on the direction and if cycle is allowed\n if (index === -1) {\n return list[!shouldGetNext && isCycleAllowed ? list.length - 1 : 0]\n }\n\n const listLength = list.length\n\n index += shouldGetNext ? 1 : -1\n\n if (isCycleAllowed) {\n index = (index + listLength) % listLength\n }\n\n return list[Math.max(0, Math.min(index, listLength - 1))]\n}\n\nexport {\n getElement,\n getUID,\n getSelectorFromElement,\n getElementFromSelector,\n getTransitionDurationFromElement,\n triggerTransitionEnd,\n isElement,\n typeCheckConfig,\n isVisible,\n isDisabled,\n findShadowRoot,\n noop,\n getNextActiveElement,\n reflow,\n getjQuery,\n onDOMContentLoaded,\n isRTL,\n defineJQueryPlugin,\n execute,\n executeAfterTransition\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { getjQuery } from '../util/index'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/\nconst stripNameRegex = /\\..*/\nconst stripUidRegex = /::\\d+$/\nconst eventRegistry = {} // Events storage\nlet uidEvent = 1\nconst customEvents = {\n mouseenter: 'mouseover',\n mouseleave: 'mouseout'\n}\nconst customEventsRegex = /^(mouseenter|mouseleave)/i\nconst nativeEvents = new Set([\n 'click',\n 'dblclick',\n 'mouseup',\n 'mousedown',\n 'contextmenu',\n 'mousewheel',\n 'DOMMouseScroll',\n 'mouseover',\n 'mouseout',\n 'mousemove',\n 'selectstart',\n 'selectend',\n 'keydown',\n 'keypress',\n 'keyup',\n 'orientationchange',\n 'touchstart',\n 'touchmove',\n 'touchend',\n 'touchcancel',\n 'pointerdown',\n 'pointermove',\n 'pointerup',\n 'pointerleave',\n 'pointercancel',\n 'gesturestart',\n 'gesturechange',\n 'gestureend',\n 'focus',\n 'blur',\n 'change',\n 'reset',\n 'select',\n 'submit',\n 'focusin',\n 'focusout',\n 'load',\n 'unload',\n 'beforeunload',\n 'resize',\n 'move',\n 'DOMContentLoaded',\n 'readystatechange',\n 'error',\n 'abort',\n 'scroll'\n])\n\n/**\n * ------------------------------------------------------------------------\n * Private methods\n * ------------------------------------------------------------------------\n */\n\nfunction getUidEvent(element, uid) {\n return (uid && `${uid}::${uidEvent++}`) || element.uidEvent || uidEvent++\n}\n\nfunction getEvent(element) {\n const uid = getUidEvent(element)\n\n element.uidEvent = uid\n eventRegistry[uid] = eventRegistry[uid] || {}\n\n return eventRegistry[uid]\n}\n\nfunction bootstrapHandler(element, fn) {\n return function handler(event) {\n event.delegateTarget = element\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, fn)\n }\n\n return fn.apply(element, [event])\n }\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n return function handler(event) {\n const domElements = element.querySelectorAll(selector)\n\n for (let { target } = event; target && target !== this; target = target.parentNode) {\n for (let i = domElements.length; i--;) {\n if (domElements[i] === target) {\n event.delegateTarget = target\n\n if (handler.oneOff) {\n // eslint-disable-next-line unicorn/consistent-destructuring\n EventHandler.off(element, event.type, selector, fn)\n }\n\n return fn.apply(target, [event])\n }\n }\n }\n\n // To please ESLint\n return null\n }\n}\n\nfunction findHandler(events, handler, delegationSelector = null) {\n const uidEventList = Object.keys(events)\n\n for (let i = 0, len = uidEventList.length; i < len; i++) {\n const event = events[uidEventList[i]]\n\n if (event.originalHandler === handler && event.delegationSelector === delegationSelector) {\n return event\n }\n }\n\n return null\n}\n\nfunction normalizeParams(originalTypeEvent, handler, delegationFn) {\n const delegation = typeof handler === 'string'\n const originalHandler = delegation ? delegationFn : handler\n\n let typeEvent = getTypeEvent(originalTypeEvent)\n const isNative = nativeEvents.has(typeEvent)\n\n if (!isNative) {\n typeEvent = originalTypeEvent\n }\n\n return [delegation, originalHandler, typeEvent]\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFn, oneOff) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return\n }\n\n if (!handler) {\n handler = delegationFn\n delegationFn = null\n }\n\n // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n if (customEventsRegex.test(originalTypeEvent)) {\n const wrapFn = fn => {\n return function (event) {\n if (!event.relatedTarget || (event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget))) {\n return fn.call(this, event)\n }\n }\n }\n\n if (delegationFn) {\n delegationFn = wrapFn(delegationFn)\n } else {\n handler = wrapFn(handler)\n }\n }\n\n const [delegation, originalHandler, typeEvent] = normalizeParams(originalTypeEvent, handler, delegationFn)\n const events = getEvent(element)\n const handlers = events[typeEvent] || (events[typeEvent] = {})\n const previousFn = findHandler(handlers, originalHandler, delegation ? handler : null)\n\n if (previousFn) {\n previousFn.oneOff = previousFn.oneOff && oneOff\n\n return\n }\n\n const uid = getUidEvent(originalHandler, originalTypeEvent.replace(namespaceRegex, ''))\n const fn = delegation ?\n bootstrapDelegationHandler(element, handler, delegationFn) :\n bootstrapHandler(element, handler)\n\n fn.delegationSelector = delegation ? handler : null\n fn.originalHandler = originalHandler\n fn.oneOff = oneOff\n fn.uidEvent = uid\n handlers[uid] = fn\n\n element.addEventListener(typeEvent, fn, delegation)\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n const fn = findHandler(events[typeEvent], handler, delegationSelector)\n\n if (!fn) {\n return\n }\n\n element.removeEventListener(typeEvent, fn, Boolean(delegationSelector))\n delete events[typeEvent][fn.uidEvent]\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n const storeElementEvent = events[typeEvent] || {}\n\n Object.keys(storeElementEvent).forEach(handlerKey => {\n if (handlerKey.includes(namespace)) {\n const event = storeElementEvent[handlerKey]\n\n removeHandler(element, events, typeEvent, event.originalHandler, event.delegationSelector)\n }\n })\n}\n\nfunction getTypeEvent(event) {\n // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n event = event.replace(stripNameRegex, '')\n return customEvents[event] || event\n}\n\nconst EventHandler = {\n on(element, event, handler, delegationFn) {\n addHandler(element, event, handler, delegationFn, false)\n },\n\n one(element, event, handler, delegationFn) {\n addHandler(element, event, handler, delegationFn, true)\n },\n\n off(element, originalTypeEvent, handler, delegationFn) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return\n }\n\n const [delegation, originalHandler, typeEvent] = normalizeParams(originalTypeEvent, handler, delegationFn)\n const inNamespace = typeEvent !== originalTypeEvent\n const events = getEvent(element)\n const isNamespace = originalTypeEvent.startsWith('.')\n\n if (typeof originalHandler !== 'undefined') {\n // Simplest case: handler is passed, remove that listener ONLY.\n if (!events || !events[typeEvent]) {\n return\n }\n\n removeHandler(element, events, typeEvent, originalHandler, delegation ? handler : null)\n return\n }\n\n if (isNamespace) {\n Object.keys(events).forEach(elementEvent => {\n removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1))\n })\n }\n\n const storeElementEvent = events[typeEvent] || {}\n Object.keys(storeElementEvent).forEach(keyHandlers => {\n const handlerKey = keyHandlers.replace(stripUidRegex, '')\n\n if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n const event = storeElementEvent[keyHandlers]\n\n removeHandler(element, events, typeEvent, event.originalHandler, event.delegationSelector)\n }\n })\n },\n\n trigger(element, event, args) {\n if (typeof event !== 'string' || !element) {\n return null\n }\n\n const $ = getjQuery()\n const typeEvent = getTypeEvent(event)\n const inNamespace = event !== typeEvent\n const isNative = nativeEvents.has(typeEvent)\n\n let jQueryEvent\n let bubbles = true\n let nativeDispatch = true\n let defaultPrevented = false\n let evt = null\n\n if (inNamespace && $) {\n jQueryEvent = $.Event(event, args)\n\n $(element).trigger(jQueryEvent)\n bubbles = !jQueryEvent.isPropagationStopped()\n nativeDispatch = !jQueryEvent.isImmediatePropagationStopped()\n defaultPrevented = jQueryEvent.isDefaultPrevented()\n }\n\n if (isNative) {\n evt = document.createEvent('HTMLEvents')\n evt.initEvent(typeEvent, bubbles, true)\n } else {\n evt = new CustomEvent(event, {\n bubbles,\n cancelable: true\n })\n }\n\n // merge custom information in our event\n if (typeof args !== 'undefined') {\n Object.keys(args).forEach(key => {\n Object.defineProperty(evt, key, {\n get() {\n return args[key]\n }\n })\n })\n }\n\n if (defaultPrevented) {\n evt.preventDefault()\n }\n\n if (nativeDispatch) {\n element.dispatchEvent(evt)\n }\n\n if (evt.defaultPrevented && typeof jQueryEvent !== 'undefined') {\n jQueryEvent.preventDefault()\n }\n\n return evt\n }\n}\n\nexport default EventHandler\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst elementMap = new Map()\n\nexport default {\n set(element, key, instance) {\n if (!elementMap.has(element)) {\n elementMap.set(element, new Map())\n }\n\n const instanceMap = elementMap.get(element)\n\n // make it clear we only want one instance per element\n // can be removed later when multiple key/instances are fine to be used\n if (!instanceMap.has(key) && instanceMap.size !== 0) {\n // eslint-disable-next-line no-console\n console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`)\n return\n }\n\n instanceMap.set(key, instance)\n },\n\n get(element, key) {\n if (elementMap.has(element)) {\n return elementMap.get(element).get(key) || null\n }\n\n return null\n },\n\n remove(element, key) {\n if (!elementMap.has(element)) {\n return\n }\n\n const instanceMap = elementMap.get(element)\n\n instanceMap.delete(key)\n\n // free up element references if there are no instances left for an element\n if (instanceMap.size === 0) {\n elementMap.delete(element)\n }\n }\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Data from './dom/data'\nimport {\n executeAfterTransition,\n getElement\n} from './util/index'\nimport EventHandler from './dom/event-handler'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst VERSION = '5.1.0'\n\nclass BaseComponent {\n constructor(element) {\n element = getElement(element)\n\n if (!element) {\n return\n }\n\n this._element = element\n Data.set(this._element, this.constructor.DATA_KEY, this)\n }\n\n dispose() {\n Data.remove(this._element, this.constructor.DATA_KEY)\n EventHandler.off(this._element, this.constructor.EVENT_KEY)\n\n Object.getOwnPropertyNames(this).forEach(propertyName => {\n this[propertyName] = null\n })\n }\n\n _queueCallback(callback, element, isAnimated = true) {\n executeAfterTransition(callback, element, isAnimated)\n }\n\n /** Static */\n\n static getInstance(element) {\n return Data.get(getElement(element), this.DATA_KEY)\n }\n\n static getOrCreateInstance(element, config = {}) {\n return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null)\n }\n\n static get VERSION() {\n return VERSION\n }\n\n static get NAME() {\n throw new Error('You have to implement the static method \"NAME\", for each component!')\n }\n\n static get DATA_KEY() {\n return `bs.${this.NAME}`\n }\n\n static get EVENT_KEY() {\n return `.${this.DATA_KEY}`\n }\n}\n\nexport default BaseComponent\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler'\nimport { getElementFromSelector, isDisabled } from './index'\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n const clickEvent = `click.dismiss${component.EVENT_KEY}`\n const name = component.NAME\n\n EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault()\n }\n\n if (isDisabled(this)) {\n return\n }\n\n const target = getElementFromSelector(this) || this.closest(`.${name}`)\n const instance = component.getOrCreateInstance(target)\n\n // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n instance[method]()\n })\n}\n\nexport {\n enableDismissTrigger\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { defineJQueryPlugin } from './util/index'\nimport EventHandler from './dom/event-handler'\nimport BaseComponent from './base-component'\nimport { enableDismissTrigger } from './util/component-functions'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'alert'\nconst DATA_KEY = 'bs.alert'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_CLOSE = `close${EVENT_KEY}`\nconst EVENT_CLOSED = `closed${EVENT_KEY}`\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass Alert extends BaseComponent {\n // Getters\n\n static get NAME() {\n return NAME\n }\n\n // Public\n\n close() {\n const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE)\n\n if (closeEvent.defaultPrevented) {\n return\n }\n\n this._element.classList.remove(CLASS_NAME_SHOW)\n\n const isAnimated = this._element.classList.contains(CLASS_NAME_FADE)\n this._queueCallback(() => this._destroyElement(), this._element, isAnimated)\n }\n\n // Private\n _destroyElement() {\n this._element.remove()\n EventHandler.trigger(this._element, EVENT_CLOSED)\n this.dispose()\n }\n\n // Static\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Alert.getOrCreateInstance(this)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config](this)\n })\n }\n}\n\n/**\n * ------------------------------------------------------------------------\n * Data Api implementation\n * ------------------------------------------------------------------------\n */\n\nenableDismissTrigger(Alert, 'close')\n/**\n * ------------------------------------------------------------------------\n * jQuery\n * ------------------------------------------------------------------------\n * add .Alert to jQuery only if jQuery is present\n */\n\ndefineJQueryPlugin(Alert)\n\nexport default Alert\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { defineJQueryPlugin } from './util/index'\nimport EventHandler from './dom/event-handler'\nimport BaseComponent from './base-component'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'button'\nconst DATA_KEY = 'bs.button'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst CLASS_NAME_ACTIVE = 'active'\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"button\"]'\n\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass Button extends BaseComponent {\n // Getters\n\n static get NAME() {\n return NAME\n }\n\n // Public\n\n toggle() {\n // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE))\n }\n\n // Static\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Button.getOrCreateInstance(this)\n\n if (config === 'toggle') {\n data[config]()\n }\n })\n }\n}\n\n/**\n * ------------------------------------------------------------------------\n * Data Api implementation\n * ------------------------------------------------------------------------\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, event => {\n event.preventDefault()\n\n const button = event.target.closest(SELECTOR_DATA_TOGGLE)\n const data = Button.getOrCreateInstance(button)\n\n data.toggle()\n})\n\n/**\n * ------------------------------------------------------------------------\n * jQuery\n * ------------------------------------------------------------------------\n * add .Button to jQuery only if jQuery is present\n */\n\ndefineJQueryPlugin(Button)\n\nexport default Button\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(val) {\n if (val === 'true') {\n return true\n }\n\n if (val === 'false') {\n return false\n }\n\n if (val === Number(val).toString()) {\n return Number(val)\n }\n\n if (val === '' || val === 'null') {\n return null\n }\n\n return val\n}\n\nfunction normalizeDataKey(key) {\n return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`)\n}\n\nconst Manipulator = {\n setDataAttribute(element, key, value) {\n element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value)\n },\n\n removeDataAttribute(element, key) {\n element.removeAttribute(`data-bs-${normalizeDataKey(key)}`)\n },\n\n getDataAttributes(element) {\n if (!element) {\n return {}\n }\n\n const attributes = {}\n\n Object.keys(element.dataset)\n .filter(key => key.startsWith('bs'))\n .forEach(key => {\n let pureKey = key.replace(/^bs/, '')\n pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length)\n attributes[pureKey] = normalizeData(element.dataset[key])\n })\n\n return attributes\n },\n\n getDataAttribute(element, key) {\n return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`))\n },\n\n offset(element) {\n const rect = element.getBoundingClientRect()\n\n return {\n top: rect.top + window.pageYOffset,\n left: rect.left + window.pageXOffset\n }\n },\n\n position(element) {\n return {\n top: element.offsetTop,\n left: element.offsetLeft\n }\n }\n}\n\nexport default Manipulator\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nimport { isDisabled, isVisible } from '../util/index'\n\nconst NODE_TEXT = 3\n\nconst SelectorEngine = {\n find(selector, element = document.documentElement) {\n return [].concat(...Element.prototype.querySelectorAll.call(element, selector))\n },\n\n findOne(selector, element = document.documentElement) {\n return Element.prototype.querySelector.call(element, selector)\n },\n\n children(element, selector) {\n return [].concat(...element.children)\n .filter(child => child.matches(selector))\n },\n\n parents(element, selector) {\n const parents = []\n\n let ancestor = element.parentNode\n\n while (ancestor && ancestor.nodeType === Node.ELEMENT_NODE && ancestor.nodeType !== NODE_TEXT) {\n if (ancestor.matches(selector)) {\n parents.push(ancestor)\n }\n\n ancestor = ancestor.parentNode\n }\n\n return parents\n },\n\n prev(element, selector) {\n let previous = element.previousElementSibling\n\n while (previous) {\n if (previous.matches(selector)) {\n return [previous]\n }\n\n previous = previous.previousElementSibling\n }\n\n return []\n },\n\n next(element, selector) {\n let next = element.nextElementSibling\n\n while (next) {\n if (next.matches(selector)) {\n return [next]\n }\n\n next = next.nextElementSibling\n }\n\n return []\n },\n\n focusableChildren(element) {\n const focusables = [\n 'a',\n 'button',\n 'input',\n 'textarea',\n 'select',\n 'details',\n '[tabindex]',\n '[contenteditable=\"true\"]'\n ].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(', ')\n\n return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el))\n }\n}\n\nexport default SelectorEngine\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport {\n defineJQueryPlugin,\n getElementFromSelector,\n isRTL,\n isVisible,\n getNextActiveElement,\n reflow,\n triggerTransitionEnd,\n typeCheckConfig\n} from './util/index'\nimport EventHandler from './dom/event-handler'\nimport Manipulator from './dom/manipulator'\nimport SelectorEngine from './dom/selector-engine'\nimport BaseComponent from './base-component'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'carousel'\nconst DATA_KEY = 'bs.carousel'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ARROW_LEFT_KEY = 'ArrowLeft'\nconst ARROW_RIGHT_KEY = 'ArrowRight'\nconst TOUCHEVENT_COMPAT_WAIT = 500 // Time for mouse compat events to fire after touch\nconst SWIPE_THRESHOLD = 40\n\nconst Default = {\n interval: 5000,\n keyboard: true,\n slide: false,\n pause: 'hover',\n wrap: true,\n touch: true\n}\n\nconst DefaultType = {\n interval: '(number|boolean)',\n keyboard: 'boolean',\n slide: '(boolean|string)',\n pause: '(string|boolean)',\n wrap: 'boolean',\n touch: 'boolean'\n}\n\nconst ORDER_NEXT = 'next'\nconst ORDER_PREV = 'prev'\nconst DIRECTION_LEFT = 'left'\nconst DIRECTION_RIGHT = 'right'\n\nconst KEY_TO_DIRECTION = {\n [ARROW_LEFT_KEY]: DIRECTION_RIGHT,\n [ARROW_RIGHT_KEY]: DIRECTION_LEFT\n}\n\nconst EVENT_SLIDE = `slide${EVENT_KEY}`\nconst EVENT_SLID = `slid${EVENT_KEY}`\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY}`\nconst EVENT_MOUSEENTER = `mouseenter${EVENT_KEY}`\nconst EVENT_MOUSELEAVE = `mouseleave${EVENT_KEY}`\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY}`\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY}`\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY}`\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY}`\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY}`\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_CAROUSEL = 'carousel'\nconst CLASS_NAME_ACTIVE = 'active'\nconst CLASS_NAME_SLIDE = 'slide'\nconst CLASS_NAME_END = 'carousel-item-end'\nconst CLASS_NAME_START = 'carousel-item-start'\nconst CLASS_NAME_NEXT = 'carousel-item-next'\nconst CLASS_NAME_PREV = 'carousel-item-prev'\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event'\n\nconst SELECTOR_ACTIVE = '.active'\nconst SELECTOR_ACTIVE_ITEM = '.active.carousel-item'\nconst SELECTOR_ITEM = '.carousel-item'\nconst SELECTOR_ITEM_IMG = '.carousel-item img'\nconst SELECTOR_NEXT_PREV = '.carousel-item-next, .carousel-item-prev'\nconst SELECTOR_INDICATORS = '.carousel-indicators'\nconst SELECTOR_INDICATOR = '[data-bs-target]'\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]'\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]'\n\nconst POINTER_TYPE_TOUCH = 'touch'\nconst POINTER_TYPE_PEN = 'pen'\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\nclass Carousel extends BaseComponent {\n constructor(element, config) {\n super(element)\n\n this._items = null\n this._interval = null\n this._activeElement = null\n this._isPaused = false\n this._isSliding = false\n this.touchTimeout = null\n this.touchStartX = 0\n this.touchDeltaX = 0\n\n this._config = this._getConfig(config)\n this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element)\n this._touchSupported = 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0\n this._pointerEvent = Boolean(window.PointerEvent)\n\n this._addEventListeners()\n }\n\n // Getters\n\n static get Default() {\n return Default\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n\n next() {\n this._slide(ORDER_NEXT)\n }\n\n nextWhenVisible() {\n // Don't call next when the page isn't visible\n // or the carousel or its parent isn't visible\n if (!document.hidden && isVisible(this._element)) {\n this.next()\n }\n }\n\n prev() {\n this._slide(ORDER_PREV)\n }\n\n pause(event) {\n if (!event) {\n this._isPaused = true\n }\n\n if (SelectorEngine.findOne(SELECTOR_NEXT_PREV, this._element)) {\n triggerTransitionEnd(this._element)\n this.cycle(true)\n }\n\n clearInterval(this._interval)\n this._interval = null\n }\n\n cycle(event) {\n if (!event) {\n this._isPaused = false\n }\n\n if (this._interval) {\n clearInterval(this._interval)\n this._interval = null\n }\n\n if (this._config && this._config.interval && !this._isPaused) {\n this._updateInterval()\n\n this._interval = setInterval(\n (document.visibilityState ? this.nextWhenVisible : this.next).bind(this),\n this._config.interval\n )\n }\n }\n\n to(index) {\n this._activeElement = SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element)\n const activeIndex = this._getItemIndex(this._activeElement)\n\n if (index > this._items.length - 1 || index < 0) {\n return\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.to(index))\n return\n }\n\n if (activeIndex === index) {\n this.pause()\n this.cycle()\n return\n }\n\n const order = index > activeIndex ?\n ORDER_NEXT :\n ORDER_PREV\n\n this._slide(order, this._items[index])\n }\n\n // Private\n\n _getConfig(config) {\n config = {\n ...Default,\n ...Manipulator.getDataAttributes(this._element),\n ...(typeof config === 'object' ? config : {})\n }\n typeCheckConfig(NAME, config, DefaultType)\n return config\n }\n\n _handleSwipe() {\n const absDeltax = Math.abs(this.touchDeltaX)\n\n if (absDeltax <= SWIPE_THRESHOLD) {\n return\n }\n\n const direction = absDeltax / this.touchDeltaX\n\n this.touchDeltaX = 0\n\n if (!direction) {\n return\n }\n\n this._slide(direction > 0 ? DIRECTION_RIGHT : DIRECTION_LEFT)\n }\n\n _addEventListeners() {\n if (this._config.keyboard) {\n EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event))\n }\n\n if (this._config.pause === 'hover') {\n EventHandler.on(this._element, EVENT_MOUSEENTER, event => this.pause(event))\n EventHandler.on(this._element, EVENT_MOUSELEAVE, event => this.cycle(event))\n }\n\n if (this._config.touch && this._touchSupported) {\n this._addTouchEventListeners()\n }\n }\n\n _addTouchEventListeners() {\n const start = event => {\n if (this._pointerEvent && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH)) {\n this.touchStartX = event.clientX\n } else if (!this._pointerEvent) {\n this.touchStartX = event.touches[0].clientX\n }\n }\n\n const move = event => {\n // ensure swiping with one touch and not pinching\n this.touchDeltaX = event.touches && event.touches.length > 1 ?\n 0 :\n event.touches[0].clientX - this.touchStartX\n }\n\n const end = event => {\n if (this._pointerEvent && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH)) {\n this.touchDeltaX = event.clientX - this.touchStartX\n }\n\n this._handleSwipe()\n if (this._config.pause === 'hover') {\n // If it's a touch-enabled device, mouseenter/leave are fired as\n // part of the mouse compatibility events on first tap - the carousel\n // would stop cycling until user tapped out of it;\n // here, we listen for touchend, explicitly pause the carousel\n // (as if it's the second time we tap on it, mouseenter compat event\n // is NOT fired) and after a timeout (to allow for mouse compatibility\n // events to fire) we explicitly restart cycling\n\n this.pause()\n if (this.touchTimeout) {\n clearTimeout(this.touchTimeout)\n }\n\n this.touchTimeout = setTimeout(event => this.cycle(event), TOUCHEVENT_COMPAT_WAIT + this._config.interval)\n }\n }\n\n SelectorEngine.find(SELECTOR_ITEM_IMG, this._element).forEach(itemImg => {\n EventHandler.on(itemImg, EVENT_DRAG_START, e => e.preventDefault())\n })\n\n if (this._pointerEvent) {\n EventHandler.on(this._element, EVENT_POINTERDOWN, event => start(event))\n EventHandler.on(this._element, EVENT_POINTERUP, event => end(event))\n\n this._element.classList.add(CLASS_NAME_POINTER_EVENT)\n } else {\n EventHandler.on(this._element, EVENT_TOUCHSTART, event => start(event))\n EventHandler.on(this._element, EVENT_TOUCHMOVE, event => move(event))\n EventHandler.on(this._element, EVENT_TOUCHEND, event => end(event))\n }\n }\n\n _keydown(event) {\n if (/input|textarea/i.test(event.target.tagName)) {\n return\n }\n\n const direction = KEY_TO_DIRECTION[event.key]\n if (direction) {\n event.preventDefault()\n this._slide(direction)\n }\n }\n\n _getItemIndex(element) {\n this._items = element && element.parentNode ?\n SelectorEngine.find(SELECTOR_ITEM, element.parentNode) :\n []\n\n return this._items.indexOf(element)\n }\n\n _getItemByOrder(order, activeElement) {\n const isNext = order === ORDER_NEXT\n return getNextActiveElement(this._items, activeElement, isNext, this._config.wrap)\n }\n\n _triggerSlideEvent(relatedTarget, eventDirectionName) {\n const targetIndex = this._getItemIndex(relatedTarget)\n const fromIndex = this._getItemIndex(SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element))\n\n return EventHandler.trigger(this._element, EVENT_SLIDE, {\n relatedTarget,\n direction: eventDirectionName,\n from: fromIndex,\n to: targetIndex\n })\n }\n\n _setActiveIndicatorElement(element) {\n if (this._indicatorsElement) {\n const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement)\n\n activeIndicator.classList.remove(CLASS_NAME_ACTIVE)\n activeIndicator.removeAttribute('aria-current')\n\n const indicators = SelectorEngine.find(SELECTOR_INDICATOR, this._indicatorsElement)\n\n for (let i = 0; i < indicators.length; i++) {\n if (Number.parseInt(indicators[i].getAttribute('data-bs-slide-to'), 10) === this._getItemIndex(element)) {\n indicators[i].classList.add(CLASS_NAME_ACTIVE)\n indicators[i].setAttribute('aria-current', 'true')\n break\n }\n }\n }\n }\n\n _updateInterval() {\n const element = this._activeElement || SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element)\n\n if (!element) {\n return\n }\n\n const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10)\n\n if (elementInterval) {\n this._config.defaultInterval = this._config.defaultInterval || this._config.interval\n this._config.interval = elementInterval\n } else {\n this._config.interval = this._config.defaultInterval || this._config.interval\n }\n }\n\n _slide(directionOrOrder, element) {\n const order = this._directionToOrder(directionOrOrder)\n const activeElement = SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element)\n const activeElementIndex = this._getItemIndex(activeElement)\n const nextElement = element || this._getItemByOrder(order, activeElement)\n\n const nextElementIndex = this._getItemIndex(nextElement)\n const isCycling = Boolean(this._interval)\n\n const isNext = order === ORDER_NEXT\n const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END\n const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV\n const eventDirectionName = this._orderToDirection(order)\n\n if (nextElement && nextElement.classList.contains(CLASS_NAME_ACTIVE)) {\n this._isSliding = false\n return\n }\n\n if (this._isSliding) {\n return\n }\n\n const slideEvent = this._triggerSlideEvent(nextElement, eventDirectionName)\n if (slideEvent.defaultPrevented) {\n return\n }\n\n if (!activeElement || !nextElement) {\n // Some weirdness is happening, so we bail\n return\n }\n\n this._isSliding = true\n\n if (isCycling) {\n this.pause()\n }\n\n this._setActiveIndicatorElement(nextElement)\n this._activeElement = nextElement\n\n const triggerSlidEvent = () => {\n EventHandler.trigger(this._element, EVENT_SLID, {\n relatedTarget: nextElement,\n direction: eventDirectionName,\n from: activeElementIndex,\n to: nextElementIndex\n })\n }\n\n if (this._element.classList.contains(CLASS_NAME_SLIDE)) {\n nextElement.classList.add(orderClassName)\n\n reflow(nextElement)\n\n activeElement.classList.add(directionalClassName)\n nextElement.classList.add(directionalClassName)\n\n const completeCallBack = () => {\n nextElement.classList.remove(directionalClassName, orderClassName)\n nextElement.classList.add(CLASS_NAME_ACTIVE)\n\n activeElement.classList.remove(CLASS_NAME_ACTIVE, orderClassName, directionalClassName)\n\n this._isSliding = false\n\n setTimeout(triggerSlidEvent, 0)\n }\n\n this._queueCallback(completeCallBack, activeElement, true)\n } else {\n activeElement.classList.remove(CLASS_NAME_ACTIVE)\n nextElement.classList.add(CLASS_NAME_ACTIVE)\n\n this._isSliding = false\n triggerSlidEvent()\n }\n\n if (isCycling) {\n this.cycle()\n }\n }\n\n _directionToOrder(direction) {\n if (![DIRECTION_RIGHT, DIRECTION_LEFT].includes(direction)) {\n return direction\n }\n\n if (isRTL()) {\n return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT\n }\n\n return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV\n }\n\n _orderToDirection(order) {\n if (![ORDER_NEXT, ORDER_PREV].includes(order)) {\n return order\n }\n\n if (isRTL()) {\n return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT\n }\n\n return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT\n }\n\n // Static\n\n static carouselInterface(element, config) {\n const data = Carousel.getOrCreateInstance(element, config)\n\n let { _config } = data\n if (typeof config === 'object') {\n _config = {\n ..._config,\n ...config\n }\n }\n\n const action = typeof config === 'string' ? config : _config.slide\n\n if (typeof config === 'number') {\n data.to(config)\n } else if (typeof action === 'string') {\n if (typeof data[action] === 'undefined') {\n throw new TypeError(`No method named \"${action}\"`)\n }\n\n data[action]()\n } else if (_config.interval && _config.ride) {\n data.pause()\n data.cycle()\n }\n }\n\n static jQueryInterface(config) {\n return this.each(function () {\n Carousel.carouselInterface(this, config)\n })\n }\n\n static dataApiClickHandler(event) {\n const target = getElementFromSelector(this)\n\n if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n return\n }\n\n const config = {\n ...Manipulator.getDataAttributes(target),\n ...Manipulator.getDataAttributes(this)\n }\n const slideIndex = this.getAttribute('data-bs-slide-to')\n\n if (slideIndex) {\n config.interval = false\n }\n\n Carousel.carouselInterface(target, config)\n\n if (slideIndex) {\n Carousel.getInstance(target).to(slideIndex)\n }\n\n event.preventDefault()\n }\n}\n\n/**\n * ------------------------------------------------------------------------\n * Data Api implementation\n * ------------------------------------------------------------------------\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_SLIDE, Carousel.dataApiClickHandler)\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE)\n\n for (let i = 0, len = carousels.length; i < len; i++) {\n Carousel.carouselInterface(carousels[i], Carousel.getInstance(carousels[i]))\n }\n})\n\n/**\n * ------------------------------------------------------------------------\n * jQuery\n * ------------------------------------------------------------------------\n * add .Carousel to jQuery only if jQuery is present\n */\n\ndefineJQueryPlugin(Carousel)\n\nexport default Carousel\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport {\n defineJQueryPlugin,\n getElement,\n getSelectorFromElement,\n getElementFromSelector,\n reflow,\n typeCheckConfig\n} from './util/index'\nimport Data from './dom/data'\nimport EventHandler from './dom/event-handler'\nimport Manipulator from './dom/manipulator'\nimport SelectorEngine from './dom/selector-engine'\nimport BaseComponent from './base-component'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'collapse'\nconst DATA_KEY = 'bs.collapse'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst Default = {\n toggle: true,\n parent: null\n}\n\nconst DefaultType = {\n toggle: 'boolean',\n parent: '(null|element)'\n}\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_COLLAPSE = 'collapse'\nconst CLASS_NAME_COLLAPSING = 'collapsing'\nconst CLASS_NAME_COLLAPSED = 'collapsed'\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal'\n\nconst WIDTH = 'width'\nconst HEIGHT = 'height'\n\nconst SELECTOR_ACTIVES = '.show, .collapsing'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"collapse\"]'\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass Collapse extends BaseComponent {\n constructor(element, config) {\n super(element)\n\n this._isTransitioning = false\n this._config = this._getConfig(config)\n this._triggerArray = []\n\n const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE)\n\n for (let i = 0, len = toggleList.length; i < len; i++) {\n const elem = toggleList[i]\n const selector = getSelectorFromElement(elem)\n const filterElement = SelectorEngine.find(selector)\n .filter(foundElem => foundElem === this._element)\n\n if (selector !== null && filterElement.length) {\n this._selector = selector\n this._triggerArray.push(elem)\n }\n }\n\n this._initializeChildren()\n\n if (!this._config.parent) {\n this._addAriaAndCollapsedClass(this._triggerArray, this._isShown())\n }\n\n if (this._config.toggle) {\n this.toggle()\n }\n }\n\n // Getters\n\n static get Default() {\n return Default\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n\n toggle() {\n if (this._isShown()) {\n this.hide()\n } else {\n this.show()\n }\n }\n\n show() {\n if (this._isTransitioning || this._isShown()) {\n return\n }\n\n let actives = []\n let activesData\n\n if (this._config.parent) {\n const children = SelectorEngine.find(`.${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`, this._config.parent)\n actives = SelectorEngine.find(SELECTOR_ACTIVES, this._config.parent).filter(elem => !children.includes(elem)) // remove children if greater depth\n }\n\n const container = SelectorEngine.findOne(this._selector)\n if (actives.length) {\n const tempActiveData = actives.find(elem => container !== elem)\n activesData = tempActiveData ? Collapse.getInstance(tempActiveData) : null\n\n if (activesData && activesData._isTransitioning) {\n return\n }\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_SHOW)\n if (startEvent.defaultPrevented) {\n return\n }\n\n actives.forEach(elemActive => {\n if (container !== elemActive) {\n Collapse.getOrCreateInstance(elemActive, { toggle: false }).hide()\n }\n\n if (!activesData) {\n Data.set(elemActive, DATA_KEY, null)\n }\n })\n\n const dimension = this._getDimension()\n\n this._element.classList.remove(CLASS_NAME_COLLAPSE)\n this._element.classList.add(CLASS_NAME_COLLAPSING)\n\n this._element.style[dimension] = 0\n\n this._addAriaAndCollapsedClass(this._triggerArray, true)\n this._isTransitioning = true\n\n const complete = () => {\n this._isTransitioning = false\n\n this._element.classList.remove(CLASS_NAME_COLLAPSING)\n this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n this._element.style[dimension] = ''\n\n EventHandler.trigger(this._element, EVENT_SHOWN)\n }\n\n const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1)\n const scrollSize = `scroll${capitalizedDimension}`\n\n this._queueCallback(complete, this._element, true)\n this._element.style[dimension] = `${this._element[scrollSize]}px`\n }\n\n hide() {\n if (this._isTransitioning || !this._isShown()) {\n return\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n if (startEvent.defaultPrevented) {\n return\n }\n\n const dimension = this._getDimension()\n\n this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`\n\n reflow(this._element)\n\n this._element.classList.add(CLASS_NAME_COLLAPSING)\n this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n const triggerArrayLength = this._triggerArray.length\n for (let i = 0; i < triggerArrayLength; i++) {\n const trigger = this._triggerArray[i]\n const elem = getElementFromSelector(trigger)\n\n if (elem && !this._isShown(elem)) {\n this._addAriaAndCollapsedClass([trigger], false)\n }\n }\n\n this._isTransitioning = true\n\n const complete = () => {\n this._isTransitioning = false\n this._element.classList.remove(CLASS_NAME_COLLAPSING)\n this._element.classList.add(CLASS_NAME_COLLAPSE)\n EventHandler.trigger(this._element, EVENT_HIDDEN)\n }\n\n this._element.style[dimension] = ''\n\n this._queueCallback(complete, this._element, true)\n }\n\n _isShown(element = this._element) {\n return element.classList.contains(CLASS_NAME_SHOW)\n }\n\n // Private\n\n _getConfig(config) {\n config = {\n ...Default,\n ...Manipulator.getDataAttributes(this._element),\n ...config\n }\n config.toggle = Boolean(config.toggle) // Coerce string values\n config.parent = getElement(config.parent)\n typeCheckConfig(NAME, config, DefaultType)\n return config\n }\n\n _getDimension() {\n return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT\n }\n\n _initializeChildren() {\n if (!this._config.parent) {\n return\n }\n\n const children = SelectorEngine.find(`.${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`, this._config.parent)\n SelectorEngine.find(SELECTOR_DATA_TOGGLE, this._config.parent).filter(elem => !children.includes(elem))\n .forEach(element => {\n const selected = getElementFromSelector(element)\n\n if (selected) {\n this._addAriaAndCollapsedClass([element], this._isShown(selected))\n }\n })\n }\n\n _addAriaAndCollapsedClass(triggerArray, isOpen) {\n if (!triggerArray.length) {\n return\n }\n\n triggerArray.forEach(elem => {\n if (isOpen) {\n elem.classList.remove(CLASS_NAME_COLLAPSED)\n } else {\n elem.classList.add(CLASS_NAME_COLLAPSED)\n }\n\n elem.setAttribute('aria-expanded', isOpen)\n })\n }\n\n // Static\n\n static jQueryInterface(config) {\n return this.each(function () {\n const _config = {}\n if (typeof config === 'string' && /show|hide/.test(config)) {\n _config.toggle = false\n }\n\n const data = Collapse.getOrCreateInstance(this, _config)\n\n if (typeof config === 'string') {\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n }\n })\n }\n}\n\n/**\n * ------------------------------------------------------------------------\n * Data Api implementation\n * ------------------------------------------------------------------------\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n // preventDefault only for elements (which change the URL) not inside the collapsible element\n if (event.target.tagName === 'A' || (event.delegateTarget && event.delegateTarget.tagName === 'A')) {\n event.preventDefault()\n }\n\n const selector = getSelectorFromElement(this)\n const selectorElements = SelectorEngine.find(selector)\n\n selectorElements.forEach(element => {\n Collapse.getOrCreateInstance(element, { toggle: false }).toggle()\n })\n})\n\n/**\n * ------------------------------------------------------------------------\n * jQuery\n * ------------------------------------------------------------------------\n * add .Collapse to jQuery only if jQuery is present\n */\n\ndefineJQueryPlugin(Collapse)\n\nexport default Collapse\n","export var top = 'top';\nexport var bottom = 'bottom';\nexport var right = 'right';\nexport var left = 'left';\nexport var auto = 'auto';\nexport var basePlacements = [top, bottom, right, left];\nexport var start = 'start';\nexport var end = 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n if (node == null) {\n return window;\n }\n\n if (node.toString() !== '[object Window]') {\n var ownerDocument = node.ownerDocument;\n return ownerDocument ? ownerDocument.defaultView || window : window;\n }\n\n return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n var OwnElement = getWindow(node).Element;\n return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n var OwnElement = getWindow(node).HTMLElement;\n return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n // IE 11 has no ShadowRoot\n if (typeof ShadowRoot === 'undefined') {\n return false;\n }\n\n var OwnElement = getWindow(node).ShadowRoot;\n return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n var state = _ref.state;\n Object.keys(state.elements).forEach(function (name) {\n var style = state.styles[name] || {};\n var attributes = state.attributes[name] || {};\n var element = state.elements[name]; // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n } // Flow doesn't support to extend this property, but it's the most\n // effective way to apply styles to an HTMLElement\n // $FlowFixMe[cannot-write]\n\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (name) {\n var value = attributes[name];\n\n if (value === false) {\n element.removeAttribute(name);\n } else {\n element.setAttribute(name, value === true ? '' : value);\n }\n });\n });\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state;\n var initialStyles = {\n popper: {\n position: state.options.strategy,\n left: '0',\n top: '0',\n margin: '0'\n },\n arrow: {\n position: 'absolute'\n },\n reference: {}\n };\n Object.assign(state.elements.popper.style, initialStyles.popper);\n state.styles = initialStyles;\n\n if (state.elements.arrow) {\n Object.assign(state.elements.arrow.style, initialStyles.arrow);\n }\n\n return function () {\n Object.keys(state.elements).forEach(function (name) {\n var element = state.elements[name];\n var attributes = state.attributes[name] || {};\n var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n var style = styleProperties.reduce(function (style, property) {\n style[property] = '';\n return style;\n }, {}); // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n }\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (attribute) {\n element.removeAttribute(attribute);\n });\n });\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'applyStyles',\n enabled: true,\n phase: 'write',\n fn: applyStyles,\n effect: effect,\n requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n return placement.split('-')[0];\n}","import { isHTMLElement } from \"./instanceOf.js\";\nvar round = Math.round;\nexport default function getBoundingClientRect(element, includeScale) {\n if (includeScale === void 0) {\n includeScale = false;\n }\n\n var rect = element.getBoundingClientRect();\n var scaleX = 1;\n var scaleY = 1;\n\n if (isHTMLElement(element) && includeScale) {\n // Fallback to 1 in case both values are `0`\n scaleX = rect.width / element.offsetWidth || 1;\n scaleY = rect.height / element.offsetHeight || 1;\n }\n\n return {\n width: round(rect.width / scaleX),\n height: round(rect.height / scaleY),\n top: round(rect.top / scaleY),\n right: round(rect.right / scaleX),\n bottom: round(rect.bottom / scaleY),\n left: round(rect.left / scaleX),\n x: round(rect.left / scaleX),\n y: round(rect.top / scaleY)\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n var width = element.offsetWidth;\n var height = element.offsetHeight;\n\n if (Math.abs(clientRect.width - width) <= 1) {\n width = clientRect.width;\n }\n\n if (Math.abs(clientRect.height - height) <= 1) {\n height = clientRect.height;\n }\n\n return {\n x: element.offsetLeft,\n y: element.offsetTop,\n width: width,\n height: height\n };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n if (parent.contains(child)) {\n return true;\n } // then fallback to custom implementation with Shadow DOM support\n else if (rootNode && isShadowRoot(rootNode)) {\n var next = child;\n\n do {\n if (next && parent.isSameNode(next)) {\n return true;\n } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n next = next.parentNode || next.host;\n } while (next);\n } // Give up, the result is false\n\n\n return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n if (getNodeName(element) === 'html') {\n return element;\n }\n\n return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n // $FlowFixMe[incompatible-return]\n // $FlowFixMe[prop-missing]\n element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n element.parentNode || ( // DOM Element detected\n isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n getDocumentElement(element) // fallback\n\n );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\n\nfunction getTrueOffsetParent(element) {\n if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n getComputedStyle(element).position === 'fixed') {\n return null;\n }\n\n return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') !== -1;\n var isIE = navigator.userAgent.indexOf('Trident') !== -1;\n\n if (isIE && isHTMLElement(element)) {\n // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n var elementCss = getComputedStyle(element);\n\n if (elementCss.position === 'fixed') {\n return null;\n }\n }\n\n var currentNode = getParentNode(element);\n\n while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n // create a containing block.\n // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n return currentNode;\n } else {\n currentNode = currentNode.parentNode;\n }\n }\n\n return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n var window = getWindow(element);\n var offsetParent = getTrueOffsetParent(element);\n\n while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n offsetParent = getTrueOffsetParent(offsetParent);\n }\n\n if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n return window;\n }\n\n return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport default function within(min, value, max) {\n return mathMax(min, mathMin(value, max));\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n return {\n top: 0,\n right: 0,\n bottom: 0,\n left: 0\n };\n}","export default function expandToHashMap(value, keys) {\n return keys.reduce(function (hashMap, key) {\n hashMap[key] = value;\n return hashMap;\n }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport within from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n placement: state.placement\n })) : padding;\n return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n var _state$modifiersData$;\n\n var state = _ref.state,\n name = _ref.name,\n options = _ref.options;\n var arrowElement = state.elements.arrow;\n var popperOffsets = state.modifiersData.popperOffsets;\n var basePlacement = getBasePlacement(state.placement);\n var axis = getMainAxisFromPlacement(basePlacement);\n var isVertical = [left, right].indexOf(basePlacement) >= 0;\n var len = isVertical ? 'height' : 'width';\n\n if (!arrowElement || !popperOffsets) {\n return;\n }\n\n var paddingObject = toPaddingObject(options.padding, state);\n var arrowRect = getLayoutRect(arrowElement);\n var minProp = axis === 'y' ? top : left;\n var maxProp = axis === 'y' ? bottom : right;\n var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n var arrowOffsetParent = getOffsetParent(arrowElement);\n var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n // outside of the popper bounds\n\n var min = paddingObject[minProp];\n var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n var axisProp = axis;\n state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state,\n options = _ref2.options;\n var _options$element = options.element,\n arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n if (arrowElement == null) {\n return;\n } // CSS selector\n\n\n if (typeof arrowElement === 'string') {\n arrowElement = state.elements.popper.querySelector(arrowElement);\n\n if (!arrowElement) {\n return;\n }\n }\n\n if (process.env.NODE_ENV !== \"production\") {\n if (!isHTMLElement(arrowElement)) {\n console.error(['Popper: \"arrow\" element must be an HTMLElement (not an SVGElement).', 'To use an SVG arrow, wrap it in an HTMLElement that will be used as', 'the arrow.'].join(' '));\n }\n }\n\n if (!contains(state.elements.popper, arrowElement)) {\n if (process.env.NODE_ENV !== \"production\") {\n console.error(['Popper: \"arrow\" modifier\\'s `element` must be a child of the popper', 'element.'].join(' '));\n }\n\n return;\n }\n\n state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'arrow',\n enabled: true,\n phase: 'main',\n fn: arrow,\n effect: effect,\n requires: ['popperOffsets'],\n requiresIfExists: ['preventOverflow']\n};","import { top, left, right, bottom } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n top: 'auto',\n right: 'auto',\n bottom: 'auto',\n left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref) {\n var x = _ref.x,\n y = _ref.y;\n var win = window;\n var dpr = win.devicePixelRatio || 1;\n return {\n x: round(round(x * dpr) / dpr) || 0,\n y: round(round(y * dpr) / dpr) || 0\n };\n}\n\nexport function mapToStyles(_ref2) {\n var _Object$assign2;\n\n var popper = _ref2.popper,\n popperRect = _ref2.popperRect,\n placement = _ref2.placement,\n offsets = _ref2.offsets,\n position = _ref2.position,\n gpuAcceleration = _ref2.gpuAcceleration,\n adaptive = _ref2.adaptive,\n roundOffsets = _ref2.roundOffsets;\n\n var _ref3 = roundOffsets === true ? roundOffsetsByDPR(offsets) : typeof roundOffsets === 'function' ? roundOffsets(offsets) : offsets,\n _ref3$x = _ref3.x,\n x = _ref3$x === void 0 ? 0 : _ref3$x,\n _ref3$y = _ref3.y,\n y = _ref3$y === void 0 ? 0 : _ref3$y;\n\n var hasX = offsets.hasOwnProperty('x');\n var hasY = offsets.hasOwnProperty('y');\n var sideX = left;\n var sideY = top;\n var win = window;\n\n if (adaptive) {\n var offsetParent = getOffsetParent(popper);\n var heightProp = 'clientHeight';\n var widthProp = 'clientWidth';\n\n if (offsetParent === getWindow(popper)) {\n offsetParent = getDocumentElement(popper);\n\n if (getComputedStyle(offsetParent).position !== 'static') {\n heightProp = 'scrollHeight';\n widthProp = 'scrollWidth';\n }\n } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n offsetParent = offsetParent;\n\n if (placement === top) {\n sideY = bottom; // $FlowFixMe[prop-missing]\n\n y -= offsetParent[heightProp] - popperRect.height;\n y *= gpuAcceleration ? 1 : -1;\n }\n\n if (placement === left) {\n sideX = right; // $FlowFixMe[prop-missing]\n\n x -= offsetParent[widthProp] - popperRect.width;\n x *= gpuAcceleration ? 1 : -1;\n }\n }\n\n var commonStyles = Object.assign({\n position: position\n }, adaptive && unsetSides);\n\n if (gpuAcceleration) {\n var _Object$assign;\n\n return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) < 2 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n }\n\n return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref4) {\n var state = _ref4.state,\n options = _ref4.options;\n var _options$gpuAccelerat = options.gpuAcceleration,\n gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n _options$adaptive = options.adaptive,\n adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n _options$roundOffsets = options.roundOffsets,\n roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n\n if (process.env.NODE_ENV !== \"production\") {\n var transitionProperty = getComputedStyle(state.elements.popper).transitionProperty || '';\n\n if (adaptive && ['transform', 'top', 'right', 'bottom', 'left'].some(function (property) {\n return transitionProperty.indexOf(property) >= 0;\n })) {\n console.warn(['Popper: Detected CSS transitions on at least one of the following', 'CSS properties: \"transform\", \"top\", \"right\", \"bottom\", \"left\".', '\\n\\n', 'Disable the \"computeStyles\" modifier\\'s `adaptive` option to allow', 'for smooth transitions, or remove these properties from the CSS', 'transition declaration on the popper element if only transitioning', 'opacity or background-color for example.', '\\n\\n', 'We recommend using the popper element as a wrapper around an inner', 'element that can have any CSS property transitioned for animations.'].join(' '));\n }\n }\n\n var commonStyles = {\n placement: getBasePlacement(state.placement),\n popper: state.elements.popper,\n popperRect: state.rects.popper,\n gpuAcceleration: gpuAcceleration\n };\n\n if (state.modifiersData.popperOffsets != null) {\n state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.popperOffsets,\n position: state.options.strategy,\n adaptive: adaptive,\n roundOffsets: roundOffsets\n })));\n }\n\n if (state.modifiersData.arrow != null) {\n state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.arrow,\n position: 'absolute',\n adaptive: false,\n roundOffsets: roundOffsets\n })));\n }\n\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-placement': state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'computeStyles',\n enabled: true,\n phase: 'beforeWrite',\n fn: computeStyles,\n data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n passive: true\n};\n\nfunction effect(_ref) {\n var state = _ref.state,\n instance = _ref.instance,\n options = _ref.options;\n var _options$scroll = options.scroll,\n scroll = _options$scroll === void 0 ? true : _options$scroll,\n _options$resize = options.resize,\n resize = _options$resize === void 0 ? true : _options$resize;\n var window = getWindow(state.elements.popper);\n var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.addEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.addEventListener('resize', instance.update, passive);\n }\n\n return function () {\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.removeEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.removeEventListener('resize', instance.update, passive);\n }\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'eventListeners',\n enabled: true,\n phase: 'write',\n fn: function fn() {},\n effect: effect,\n data: {}\n};","var hash = {\n left: 'right',\n right: 'left',\n bottom: 'top',\n top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n return placement.replace(/left|right|bottom|top/g, function (matched) {\n return hash[matched];\n });\n}","var hash = {\n start: 'end',\n end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n return placement.replace(/start|end/g, function (matched) {\n return hash[matched];\n });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n var win = getWindow(node);\n var scrollLeft = win.pageXOffset;\n var scrollTop = win.pageYOffset;\n return {\n scrollLeft: scrollLeft,\n scrollTop: scrollTop\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n // If has a CSS width greater than the viewport, then this will be\n // incorrect for RTL.\n // Popper 1 is broken in this case and never had a bug report so let's assume\n // it's not an issue. I don't think anyone ever specifies width on \n // anyway.\n // Browsers where the left scrollbar doesn't cause an issue report `0` for\n // this (e.g. Edge 2019, IE11, Safari)\n return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n // Firefox wants us to check `-x` and `-y` variations as well\n var _getComputedStyle = getComputedStyle(element),\n overflow = _getComputedStyle.overflow,\n overflowX = _getComputedStyle.overflowX,\n overflowY = _getComputedStyle.overflowY;\n\n return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n var _element$ownerDocumen;\n\n if (list === void 0) {\n list = [];\n }\n\n var scrollParent = getScrollParent(element);\n var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n var win = getWindow(scrollParent);\n var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n var updatedList = list.concat(target);\n return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n updatedList.concat(listScrollParents(getParentNode(target)));\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return node.ownerDocument.body;\n }\n\n if (isHTMLElement(node) && isScrollParent(node)) {\n return node;\n }\n\n return getScrollParent(getParentNode(node));\n}","export default function rectToClientRect(rect) {\n return Object.assign({}, rect, {\n left: rect.x,\n top: rect.y,\n right: rect.x + rect.width,\n bottom: rect.y + rect.height\n });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element) {\n var rect = getBoundingClientRect(element);\n rect.top = rect.top + element.clientTop;\n rect.left = rect.left + element.clientLeft;\n rect.bottom = rect.top + element.clientHeight;\n rect.right = rect.left + element.clientWidth;\n rect.width = element.clientWidth;\n rect.height = element.clientHeight;\n rect.x = rect.left;\n rect.y = rect.top;\n return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent) {\n return clippingParent === viewport ? rectToClientRect(getViewportRect(element)) : isHTMLElement(clippingParent) ? getInnerBoundingClientRect(clippingParent) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n var clippingParents = listScrollParents(getParentNode(element));\n var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n if (!isElement(clipperElement)) {\n return [];\n } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n return clippingParents.filter(function (clippingParent) {\n return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary) {\n var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n var firstClippingParent = clippingParents[0];\n var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n var rect = getClientRectFromMixedType(element, clippingParent);\n accRect.top = max(rect.top, accRect.top);\n accRect.right = min(rect.right, accRect.right);\n accRect.bottom = min(rect.bottom, accRect.bottom);\n accRect.left = max(rect.left, accRect.left);\n return accRect;\n }, getClientRectFromMixedType(element, firstClippingParent));\n clippingRect.width = clippingRect.right - clippingRect.left;\n clippingRect.height = clippingRect.bottom - clippingRect.top;\n clippingRect.x = clippingRect.left;\n clippingRect.y = clippingRect.top;\n return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nexport default function getViewportRect(element) {\n var win = getWindow(element);\n var html = getDocumentElement(element);\n var visualViewport = win.visualViewport;\n var width = html.clientWidth;\n var height = html.clientHeight;\n var x = 0;\n var y = 0; // NB: This isn't supported on iOS <= 12. If the keyboard is open, the popper\n // can be obscured underneath it.\n // Also, `html.clientHeight` adds the bottom bar height in Safari iOS, even\n // if it isn't open, so if this isn't available, the popper will be detected\n // to overflow the bottom of the screen too early.\n\n if (visualViewport) {\n width = visualViewport.width;\n height = visualViewport.height; // Uses Layout Viewport (like Chrome; Safari does not currently)\n // In Chrome, it returns a value very close to 0 (+/-) but contains rounding\n // errors due to floating point numbers, so we need to check precision.\n // Safari returns a number <= 0, usually < -1 when pinch-zoomed\n // Feature detection fails in mobile emulation mode in Chrome.\n // Math.abs(win.innerWidth / visualViewport.scale - visualViewport.width) <\n // 0.001\n // Fallback here: \"Not Safari\" userAgent\n\n if (!/^((?!chrome|android).)*safari/i.test(navigator.userAgent)) {\n x = visualViewport.offsetLeft;\n y = visualViewport.offsetTop;\n }\n }\n\n return {\n width: width,\n height: height,\n x: x + getWindowScrollBarX(element),\n y: y\n };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `` and `` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n var _element$ownerDocumen;\n\n var html = getDocumentElement(element);\n var winScroll = getWindowScroll(element);\n var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n var y = -winScroll.scrollTop;\n\n if (getComputedStyle(body || html).direction === 'rtl') {\n x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n }\n\n return {\n width: width,\n height: height,\n x: x,\n y: y\n };\n}","export default function getVariation(placement) {\n return placement.split('-')[1];\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n var reference = _ref.reference,\n element = _ref.element,\n placement = _ref.placement;\n var basePlacement = placement ? getBasePlacement(placement) : null;\n var variation = placement ? getVariation(placement) : null;\n var commonX = reference.x + reference.width / 2 - element.width / 2;\n var commonY = reference.y + reference.height / 2 - element.height / 2;\n var offsets;\n\n switch (basePlacement) {\n case top:\n offsets = {\n x: commonX,\n y: reference.y - element.height\n };\n break;\n\n case bottom:\n offsets = {\n x: commonX,\n y: reference.y + reference.height\n };\n break;\n\n case right:\n offsets = {\n x: reference.x + reference.width,\n y: commonY\n };\n break;\n\n case left:\n offsets = {\n x: reference.x - element.width,\n y: commonY\n };\n break;\n\n default:\n offsets = {\n x: reference.x,\n y: reference.y\n };\n }\n\n var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n if (mainAxis != null) {\n var len = mainAxis === 'y' ? 'height' : 'width';\n\n switch (variation) {\n case start:\n offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n break;\n\n case end:\n offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n break;\n\n default:\n }\n }\n\n return offsets;\n}","import getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n _options$placement = _options.placement,\n placement = _options$placement === void 0 ? state.placement : _options$placement,\n _options$boundary = _options.boundary,\n boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n _options$rootBoundary = _options.rootBoundary,\n rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n _options$elementConte = _options.elementContext,\n elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n _options$altBoundary = _options.altBoundary,\n altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n _options$padding = _options.padding,\n padding = _options$padding === void 0 ? 0 : _options$padding;\n var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n var altContext = elementContext === popper ? reference : popper;\n var referenceElement = state.elements.reference;\n var popperRect = state.rects.popper;\n var element = state.elements[altBoundary ? altContext : elementContext];\n var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary);\n var referenceClientRect = getBoundingClientRect(referenceElement);\n var popperOffsets = computeOffsets({\n reference: referenceClientRect,\n element: popperRect,\n strategy: 'absolute',\n placement: placement\n });\n var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n // 0 or negative = within the clipping rect\n\n var overflowOffsets = {\n top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n };\n var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n if (elementContext === popper && offsetData) {\n var offset = offsetData[placement];\n Object.keys(overflowOffsets).forEach(function (key) {\n var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n overflowOffsets[key] += offset[axis] * multiply;\n });\n }\n\n return overflowOffsets;\n}","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n placement = _options.placement,\n boundary = _options.boundary,\n rootBoundary = _options.rootBoundary,\n padding = _options.padding,\n flipVariations = _options.flipVariations,\n _options$allowedAutoP = _options.allowedAutoPlacements,\n allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n var variation = getVariation(placement);\n var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n return getVariation(placement) === variation;\n }) : basePlacements;\n var allowedPlacements = placements.filter(function (placement) {\n return allowedAutoPlacements.indexOf(placement) >= 0;\n });\n\n if (allowedPlacements.length === 0) {\n allowedPlacements = placements;\n\n if (process.env.NODE_ENV !== \"production\") {\n console.error(['Popper: The `allowedAutoPlacements` option did not allow any', 'placements. Ensure the `placement` option matches the variation', 'of the allowed placements.', 'For example, \"auto\" cannot be used to allow \"bottom-start\".', 'Use \"auto-start\" instead.'].join(' '));\n }\n } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n var overflows = allowedPlacements.reduce(function (acc, placement) {\n acc[placement] = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding\n })[getBasePlacement(placement)];\n return acc;\n }, {});\n return Object.keys(overflows).sort(function (a, b) {\n return overflows[a] - overflows[b];\n });\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n if (getBasePlacement(placement) === auto) {\n return [];\n }\n\n var oppositePlacement = getOppositePlacement(placement);\n return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n\n if (state.modifiersData[name]._skip) {\n return;\n }\n\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n specifiedFallbackPlacements = options.fallbackPlacements,\n padding = options.padding,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n _options$flipVariatio = options.flipVariations,\n flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n allowedAutoPlacements = options.allowedAutoPlacements;\n var preferredPlacement = state.options.placement;\n var basePlacement = getBasePlacement(preferredPlacement);\n var isBasePlacement = basePlacement === preferredPlacement;\n var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n flipVariations: flipVariations,\n allowedAutoPlacements: allowedAutoPlacements\n }) : placement);\n }, []);\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var checksMap = new Map();\n var makeFallbackChecks = true;\n var firstFittingPlacement = placements[0];\n\n for (var i = 0; i < placements.length; i++) {\n var placement = placements[i];\n\n var _basePlacement = getBasePlacement(placement);\n\n var isStartVariation = getVariation(placement) === start;\n var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n var len = isVertical ? 'width' : 'height';\n var overflow = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n altBoundary: altBoundary,\n padding: padding\n });\n var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n if (referenceRect[len] > popperRect[len]) {\n mainVariationSide = getOppositePlacement(mainVariationSide);\n }\n\n var altVariationSide = getOppositePlacement(mainVariationSide);\n var checks = [];\n\n if (checkMainAxis) {\n checks.push(overflow[_basePlacement] <= 0);\n }\n\n if (checkAltAxis) {\n checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n }\n\n if (checks.every(function (check) {\n return check;\n })) {\n firstFittingPlacement = placement;\n makeFallbackChecks = false;\n break;\n }\n\n checksMap.set(placement, checks);\n }\n\n if (makeFallbackChecks) {\n // `2` may be desired in some cases – research later\n var numberOfChecks = flipVariations ? 3 : 1;\n\n var _loop = function _loop(_i) {\n var fittingPlacement = placements.find(function (placement) {\n var checks = checksMap.get(placement);\n\n if (checks) {\n return checks.slice(0, _i).every(function (check) {\n return check;\n });\n }\n });\n\n if (fittingPlacement) {\n firstFittingPlacement = fittingPlacement;\n return \"break\";\n }\n };\n\n for (var _i = numberOfChecks; _i > 0; _i--) {\n var _ret = _loop(_i);\n\n if (_ret === \"break\") break;\n }\n }\n\n if (state.placement !== firstFittingPlacement) {\n state.modifiersData[name]._skip = true;\n state.placement = firstFittingPlacement;\n state.reset = true;\n }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'flip',\n enabled: true,\n phase: 'main',\n fn: flip,\n requiresIfExists: ['offset'],\n data: {\n _skip: false\n }\n};","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n if (preventedOffsets === void 0) {\n preventedOffsets = {\n x: 0,\n y: 0\n };\n }\n\n return {\n top: overflow.top - rect.height - preventedOffsets.y,\n right: overflow.right - rect.width + preventedOffsets.x,\n bottom: overflow.bottom - rect.height + preventedOffsets.y,\n left: overflow.left - rect.width - preventedOffsets.x\n };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n return [top, right, bottom, left].some(function (side) {\n return overflow[side] >= 0;\n });\n}\n\nfunction hide(_ref) {\n var state = _ref.state,\n name = _ref.name;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var preventedOffsets = state.modifiersData.preventOverflow;\n var referenceOverflow = detectOverflow(state, {\n elementContext: 'reference'\n });\n var popperAltOverflow = detectOverflow(state, {\n altBoundary: true\n });\n var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n state.modifiersData[name] = {\n referenceClippingOffsets: referenceClippingOffsets,\n popperEscapeOffsets: popperEscapeOffsets,\n isReferenceHidden: isReferenceHidden,\n hasPopperEscaped: hasPopperEscaped\n };\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-reference-hidden': isReferenceHidden,\n 'data-popper-escaped': hasPopperEscaped\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'hide',\n enabled: true,\n phase: 'main',\n requiresIfExists: ['preventOverflow'],\n fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\";\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n var basePlacement = getBasePlacement(placement);\n var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n placement: placement\n })) : offset,\n skidding = _ref[0],\n distance = _ref[1];\n\n skidding = skidding || 0;\n distance = (distance || 0) * invertDistance;\n return [left, right].indexOf(basePlacement) >= 0 ? {\n x: distance,\n y: skidding\n } : {\n x: skidding,\n y: distance\n };\n}\n\nfunction offset(_ref2) {\n var state = _ref2.state,\n options = _ref2.options,\n name = _ref2.name;\n var _options$offset = options.offset,\n offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n var data = placements.reduce(function (acc, placement) {\n acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n return acc;\n }, {});\n var _data$state$placement = data[state.placement],\n x = _data$state$placement.x,\n y = _data$state$placement.y;\n\n if (state.modifiersData.popperOffsets != null) {\n state.modifiersData.popperOffsets.x += x;\n state.modifiersData.popperOffsets.y += y;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'offset',\n enabled: true,\n phase: 'main',\n requires: ['popperOffsets'],\n fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n var state = _ref.state,\n name = _ref.name;\n // Offsets are the actual position the popper needs to have to be\n // properly positioned near its reference element\n // This is the most basic placement, and will be adjusted by\n // the modifiers in the next step\n state.modifiersData[name] = computeOffsets({\n reference: state.rects.reference,\n element: state.rects.popper,\n strategy: 'absolute',\n placement: state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'popperOffsets',\n enabled: true,\n phase: 'read',\n fn: popperOffsets,\n data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport within from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { max as mathMax, min as mathMin } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n padding = options.padding,\n _options$tether = options.tether,\n tether = _options$tether === void 0 ? true : _options$tether,\n _options$tetherOffset = options.tetherOffset,\n tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n var overflow = detectOverflow(state, {\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n altBoundary: altBoundary\n });\n var basePlacement = getBasePlacement(state.placement);\n var variation = getVariation(state.placement);\n var isBasePlacement = !variation;\n var mainAxis = getMainAxisFromPlacement(basePlacement);\n var altAxis = getAltAxis(mainAxis);\n var popperOffsets = state.modifiersData.popperOffsets;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n placement: state.placement\n })) : tetherOffset;\n var data = {\n x: 0,\n y: 0\n };\n\n if (!popperOffsets) {\n return;\n }\n\n if (checkMainAxis || checkAltAxis) {\n var mainSide = mainAxis === 'y' ? top : left;\n var altSide = mainAxis === 'y' ? bottom : right;\n var len = mainAxis === 'y' ? 'height' : 'width';\n var offset = popperOffsets[mainAxis];\n var min = popperOffsets[mainAxis] + overflow[mainSide];\n var max = popperOffsets[mainAxis] - overflow[altSide];\n var additive = tether ? -popperRect[len] / 2 : 0;\n var minLen = variation === start ? referenceRect[len] : popperRect[len];\n var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n // outside the reference bounds\n\n var arrowElement = state.elements.arrow;\n var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n width: 0,\n height: 0\n };\n var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n var arrowPaddingMin = arrowPaddingObject[mainSide];\n var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n // to include its full size in the calculation. If the reference is small\n // and near the edge of a boundary, the popper can overflow even if the\n // reference is not overflowing as well (e.g. virtual elements with no\n // width or height)\n\n var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - tetherOffsetValue : minLen - arrowLen - arrowPaddingMin - tetherOffsetValue;\n var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + tetherOffsetValue : maxLen + arrowLen + arrowPaddingMax + tetherOffsetValue;\n var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n var offsetModifierValue = state.modifiersData.offset ? state.modifiersData.offset[state.placement][mainAxis] : 0;\n var tetherMin = popperOffsets[mainAxis] + minOffset - offsetModifierValue - clientOffset;\n var tetherMax = popperOffsets[mainAxis] + maxOffset - offsetModifierValue;\n\n if (checkMainAxis) {\n var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n popperOffsets[mainAxis] = preventedOffset;\n data[mainAxis] = preventedOffset - offset;\n }\n\n if (checkAltAxis) {\n var _mainSide = mainAxis === 'x' ? top : left;\n\n var _altSide = mainAxis === 'x' ? bottom : right;\n\n var _offset = popperOffsets[altAxis];\n\n var _min = _offset + overflow[_mainSide];\n\n var _max = _offset - overflow[_altSide];\n\n var _preventedOffset = within(tether ? mathMin(_min, tetherMin) : _min, _offset, tether ? mathMax(_max, tetherMax) : _max);\n\n popperOffsets[altAxis] = _preventedOffset;\n data[altAxis] = _preventedOffset - _offset;\n }\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'preventOverflow',\n enabled: true,\n phase: 'main',\n fn: preventOverflow,\n requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n\nfunction isElementScaled(element) {\n var rect = element.getBoundingClientRect();\n var scaleX = rect.width / element.offsetWidth || 1;\n var scaleY = rect.height / element.offsetHeight || 1;\n return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n if (isFixed === void 0) {\n isFixed = false;\n }\n\n var isOffsetParentAnElement = isHTMLElement(offsetParent);\n var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n var documentElement = getDocumentElement(offsetParent);\n var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled);\n var scroll = {\n scrollLeft: 0,\n scrollTop: 0\n };\n var offsets = {\n x: 0,\n y: 0\n };\n\n if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n isScrollParent(documentElement)) {\n scroll = getNodeScroll(offsetParent);\n }\n\n if (isHTMLElement(offsetParent)) {\n offsets = getBoundingClientRect(offsetParent, true);\n offsets.x += offsetParent.clientLeft;\n offsets.y += offsetParent.clientTop;\n } else if (documentElement) {\n offsets.x = getWindowScrollBarX(documentElement);\n }\n }\n\n return {\n x: rect.left + scroll.scrollLeft - offsets.x,\n y: rect.top + scroll.scrollTop - offsets.y,\n width: rect.width,\n height: rect.height\n };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n if (node === getWindow(node) || !isHTMLElement(node)) {\n return getWindowScroll(node);\n } else {\n return getHTMLElementScroll(node);\n }\n}","export default function getHTMLElementScroll(element) {\n return {\n scrollLeft: element.scrollLeft,\n scrollTop: element.scrollTop\n };\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport getComputedStyle from \"./dom-utils/getComputedStyle.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport validateModifiers from \"./utils/validateModifiers.js\";\nimport uniqueBy from \"./utils/uniqueBy.js\";\nimport getBasePlacement from \"./utils/getBasePlacement.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nimport { auto } from \"./enums.js\";\nvar INVALID_ELEMENT_ERROR = 'Popper: Invalid reference or popper argument provided. They must be either a DOM element or virtual element.';\nvar INFINITE_LOOP_ERROR = 'Popper: An infinite loop in the modifiers cycle has been detected! The cycle has been interrupted to prevent a browser crash.';\nvar DEFAULT_OPTIONS = {\n placement: 'bottom',\n modifiers: [],\n strategy: 'absolute'\n};\n\nfunction areValidElements() {\n for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n args[_key] = arguments[_key];\n }\n\n return !args.some(function (element) {\n return !(element && typeof element.getBoundingClientRect === 'function');\n });\n}\n\nexport function popperGenerator(generatorOptions) {\n if (generatorOptions === void 0) {\n generatorOptions = {};\n }\n\n var _generatorOptions = generatorOptions,\n _generatorOptions$def = _generatorOptions.defaultModifiers,\n defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n _generatorOptions$def2 = _generatorOptions.defaultOptions,\n defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n return function createPopper(reference, popper, options) {\n if (options === void 0) {\n options = defaultOptions;\n }\n\n var state = {\n placement: 'bottom',\n orderedModifiers: [],\n options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n modifiersData: {},\n elements: {\n reference: reference,\n popper: popper\n },\n attributes: {},\n styles: {}\n };\n var effectCleanupFns = [];\n var isDestroyed = false;\n var instance = {\n state: state,\n setOptions: function setOptions(options) {\n cleanupModifierEffects();\n state.options = Object.assign({}, defaultOptions, state.options, options);\n state.scrollParents = {\n reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n popper: listScrollParents(popper)\n }; // Orders the modifiers based on their dependencies and `phase`\n // properties\n\n var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n state.orderedModifiers = orderedModifiers.filter(function (m) {\n return m.enabled;\n }); // Validate the provided modifiers so that the consumer will get warned\n // if one of the modifiers is invalid for any reason\n\n if (process.env.NODE_ENV !== \"production\") {\n var modifiers = uniqueBy([].concat(orderedModifiers, state.options.modifiers), function (_ref) {\n var name = _ref.name;\n return name;\n });\n validateModifiers(modifiers);\n\n if (getBasePlacement(state.options.placement) === auto) {\n var flipModifier = state.orderedModifiers.find(function (_ref2) {\n var name = _ref2.name;\n return name === 'flip';\n });\n\n if (!flipModifier) {\n console.error(['Popper: \"auto\" placements require the \"flip\" modifier be', 'present and enabled to work.'].join(' '));\n }\n }\n\n var _getComputedStyle = getComputedStyle(popper),\n marginTop = _getComputedStyle.marginTop,\n marginRight = _getComputedStyle.marginRight,\n marginBottom = _getComputedStyle.marginBottom,\n marginLeft = _getComputedStyle.marginLeft; // We no longer take into account `margins` on the popper, and it can\n // cause bugs with positioning, so we'll warn the consumer\n\n\n if ([marginTop, marginRight, marginBottom, marginLeft].some(function (margin) {\n return parseFloat(margin);\n })) {\n console.warn(['Popper: CSS \"margin\" styles cannot be used to apply padding', 'between the popper and its reference element or boundary.', 'To replicate margin, use the `offset` modifier, as well as', 'the `padding` option in the `preventOverflow` and `flip`', 'modifiers.'].join(' '));\n }\n }\n\n runModifierEffects();\n return instance.update();\n },\n // Sync update – it will always be executed, even if not necessary. This\n // is useful for low frequency updates where sync behavior simplifies the\n // logic.\n // For high frequency updates (e.g. `resize` and `scroll` events), always\n // prefer the async Popper#update method\n forceUpdate: function forceUpdate() {\n if (isDestroyed) {\n return;\n }\n\n var _state$elements = state.elements,\n reference = _state$elements.reference,\n popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n // anymore\n\n if (!areValidElements(reference, popper)) {\n if (process.env.NODE_ENV !== \"production\") {\n console.error(INVALID_ELEMENT_ERROR);\n }\n\n return;\n } // Store the reference and popper rects to be read by modifiers\n\n\n state.rects = {\n reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n popper: getLayoutRect(popper)\n }; // Modifiers have the ability to reset the current update cycle. The\n // most common use case for this is the `flip` modifier changing the\n // placement, which then needs to re-run all the modifiers, because the\n // logic was previously ran for the previous placement and is therefore\n // stale/incorrect\n\n state.reset = false;\n state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n // is filled with the initial data specified by the modifier. This means\n // it doesn't persist and is fresh on each update.\n // To ensure persistent data, use `${name}#persistent`\n\n state.orderedModifiers.forEach(function (modifier) {\n return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n });\n var __debug_loops__ = 0;\n\n for (var index = 0; index < state.orderedModifiers.length; index++) {\n if (process.env.NODE_ENV !== \"production\") {\n __debug_loops__ += 1;\n\n if (__debug_loops__ > 100) {\n console.error(INFINITE_LOOP_ERROR);\n break;\n }\n }\n\n if (state.reset === true) {\n state.reset = false;\n index = -1;\n continue;\n }\n\n var _state$orderedModifie = state.orderedModifiers[index],\n fn = _state$orderedModifie.fn,\n _state$orderedModifie2 = _state$orderedModifie.options,\n _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n name = _state$orderedModifie.name;\n\n if (typeof fn === 'function') {\n state = fn({\n state: state,\n options: _options,\n name: name,\n instance: instance\n }) || state;\n }\n }\n },\n // Async and optimistically optimized update – it will not be executed if\n // not necessary (debounced to run at most once-per-tick)\n update: debounce(function () {\n return new Promise(function (resolve) {\n instance.forceUpdate();\n resolve(state);\n });\n }),\n destroy: function destroy() {\n cleanupModifierEffects();\n isDestroyed = true;\n }\n };\n\n if (!areValidElements(reference, popper)) {\n if (process.env.NODE_ENV !== \"production\") {\n console.error(INVALID_ELEMENT_ERROR);\n }\n\n return instance;\n }\n\n instance.setOptions(options).then(function (state) {\n if (!isDestroyed && options.onFirstUpdate) {\n options.onFirstUpdate(state);\n }\n }); // Modifiers have the ability to execute arbitrary code before the first\n // update cycle runs. They will be executed in the same order as the update\n // cycle. This is useful when a modifier adds some persistent data that\n // other modifiers need to use, but the modifier is run after the dependent\n // one.\n\n function runModifierEffects() {\n state.orderedModifiers.forEach(function (_ref3) {\n var name = _ref3.name,\n _ref3$options = _ref3.options,\n options = _ref3$options === void 0 ? {} : _ref3$options,\n effect = _ref3.effect;\n\n if (typeof effect === 'function') {\n var cleanupFn = effect({\n state: state,\n name: name,\n instance: instance,\n options: options\n });\n\n var noopFn = function noopFn() {};\n\n effectCleanupFns.push(cleanupFn || noopFn);\n }\n });\n }\n\n function cleanupModifierEffects() {\n effectCleanupFns.forEach(function (fn) {\n return fn();\n });\n effectCleanupFns = [];\n }\n\n return instance;\n };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n var pending;\n return function () {\n if (!pending) {\n pending = new Promise(function (resolve) {\n Promise.resolve().then(function () {\n pending = undefined;\n resolve(fn());\n });\n });\n }\n\n return pending;\n };\n}","export default function mergeByName(modifiers) {\n var merged = modifiers.reduce(function (merged, current) {\n var existing = merged[current.name];\n merged[current.name] = existing ? Object.assign({}, existing, current, {\n options: Object.assign({}, existing.options, current.options),\n data: Object.assign({}, existing.data, current.data)\n }) : current;\n return merged;\n }, {}); // IE11 does not support Object.values\n\n return Object.keys(merged).map(function (key) {\n return merged[key];\n });\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n var map = new Map();\n var visited = new Set();\n var result = [];\n modifiers.forEach(function (modifier) {\n map.set(modifier.name, modifier);\n }); // On visiting object, check for its dependencies and visit them recursively\n\n function sort(modifier) {\n visited.add(modifier.name);\n var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n requires.forEach(function (dep) {\n if (!visited.has(dep)) {\n var depModifier = map.get(dep);\n\n if (depModifier) {\n sort(depModifier);\n }\n }\n });\n result.push(modifier);\n }\n\n modifiers.forEach(function (modifier) {\n if (!visited.has(modifier.name)) {\n // check for visited object\n sort(modifier);\n }\n });\n return result;\n}\n\nexport default function orderModifiers(modifiers) {\n // order based on dependencies\n var orderedModifiers = order(modifiers); // order based on phase\n\n return modifierPhases.reduce(function (acc, phase) {\n return acc.concat(orderedModifiers.filter(function (modifier) {\n return modifier.phase === phase;\n }));\n }, []);\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\n\nimport {\n defineJQueryPlugin,\n getElement,\n getElementFromSelector,\n getNextActiveElement,\n isDisabled,\n isElement,\n isRTL,\n isVisible,\n noop,\n typeCheckConfig\n} from './util/index'\nimport EventHandler from './dom/event-handler'\nimport Manipulator from './dom/manipulator'\nimport SelectorEngine from './dom/selector-engine'\nimport BaseComponent from './base-component'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'dropdown'\nconst DATA_KEY = 'bs.dropdown'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ESCAPE_KEY = 'Escape'\nconst SPACE_KEY = 'Space'\nconst TAB_KEY = 'Tab'\nconst ARROW_UP_KEY = 'ArrowUp'\nconst ARROW_DOWN_KEY = 'ArrowDown'\nconst RIGHT_MOUSE_BUTTON = 2 // MouseEvent.button value for the secondary button, usually the right button\n\nconst REGEXP_KEYDOWN = new RegExp(`${ARROW_UP_KEY}|${ARROW_DOWN_KEY}|${ESCAPE_KEY}`)\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_DROPUP = 'dropup'\nconst CLASS_NAME_DROPEND = 'dropend'\nconst CLASS_NAME_DROPSTART = 'dropstart'\nconst CLASS_NAME_NAVBAR = 'navbar'\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"dropdown\"]'\nconst SELECTOR_MENU = '.dropdown-menu'\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav'\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)'\n\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start'\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end'\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start'\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end'\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start'\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start'\n\nconst Default = {\n offset: [0, 2],\n boundary: 'clippingParents',\n reference: 'toggle',\n display: 'dynamic',\n popperConfig: null,\n autoClose: true\n}\n\nconst DefaultType = {\n offset: '(array|string|function)',\n boundary: '(string|element)',\n reference: '(string|element|object)',\n display: 'string',\n popperConfig: '(null|object|function)',\n autoClose: '(boolean|string)'\n}\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass Dropdown extends BaseComponent {\n constructor(element, config) {\n super(element)\n\n this._popper = null\n this._config = this._getConfig(config)\n this._menu = this._getMenuElement()\n this._inNavbar = this._detectNavbar()\n }\n\n // Getters\n\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n\n toggle() {\n return this._isShown() ? this.hide() : this.show()\n }\n\n show() {\n if (isDisabled(this._element) || this._isShown(this._menu)) {\n return\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, relatedTarget)\n\n if (showEvent.defaultPrevented) {\n return\n }\n\n const parent = Dropdown.getParentFromElement(this._element)\n // Totally disable Popper for Dropdowns in Navbar\n if (this._inNavbar) {\n Manipulator.setDataAttribute(this._menu, 'popper', 'none')\n } else {\n this._createPopper(parent)\n }\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement &&\n !parent.closest(SELECTOR_NAVBAR_NAV)) {\n [].concat(...document.body.children)\n .forEach(elem => EventHandler.on(elem, 'mouseover', noop))\n }\n\n this._element.focus()\n this._element.setAttribute('aria-expanded', true)\n\n this._menu.classList.add(CLASS_NAME_SHOW)\n this._element.classList.add(CLASS_NAME_SHOW)\n EventHandler.trigger(this._element, EVENT_SHOWN, relatedTarget)\n }\n\n hide() {\n if (isDisabled(this._element) || !this._isShown(this._menu)) {\n return\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n }\n\n this._completeHide(relatedTarget)\n }\n\n dispose() {\n if (this._popper) {\n this._popper.destroy()\n }\n\n super.dispose()\n }\n\n update() {\n this._inNavbar = this._detectNavbar()\n if (this._popper) {\n this._popper.update()\n }\n }\n\n // Private\n\n _completeHide(relatedTarget) {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE, relatedTarget)\n if (hideEvent.defaultPrevented) {\n return\n }\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n [].concat(...document.body.children)\n .forEach(elem => EventHandler.off(elem, 'mouseover', noop))\n }\n\n if (this._popper) {\n this._popper.destroy()\n }\n\n this._menu.classList.remove(CLASS_NAME_SHOW)\n this._element.classList.remove(CLASS_NAME_SHOW)\n this._element.setAttribute('aria-expanded', 'false')\n Manipulator.removeDataAttribute(this._menu, 'popper')\n EventHandler.trigger(this._element, EVENT_HIDDEN, relatedTarget)\n }\n\n _getConfig(config) {\n config = {\n ...this.constructor.Default,\n ...Manipulator.getDataAttributes(this._element),\n ...config\n }\n\n typeCheckConfig(NAME, config, this.constructor.DefaultType)\n\n if (typeof config.reference === 'object' && !isElement(config.reference) &&\n typeof config.reference.getBoundingClientRect !== 'function'\n ) {\n // Popper virtual elements require a getBoundingClientRect method\n throw new TypeError(`${NAME.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`)\n }\n\n return config\n }\n\n _createPopper(parent) {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)')\n }\n\n let referenceElement = this._element\n\n if (this._config.reference === 'parent') {\n referenceElement = parent\n } else if (isElement(this._config.reference)) {\n referenceElement = getElement(this._config.reference)\n } else if (typeof this._config.reference === 'object') {\n referenceElement = this._config.reference\n }\n\n const popperConfig = this._getPopperConfig()\n const isDisplayStatic = popperConfig.modifiers.find(modifier => modifier.name === 'applyStyles' && modifier.enabled === false)\n\n this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig)\n\n if (isDisplayStatic) {\n Manipulator.setDataAttribute(this._menu, 'popper', 'static')\n }\n }\n\n _isShown(element = this._element) {\n return element.classList.contains(CLASS_NAME_SHOW)\n }\n\n _getMenuElement() {\n return SelectorEngine.next(this._element, SELECTOR_MENU)[0]\n }\n\n _getPlacement() {\n const parentDropdown = this._element.parentNode\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n return PLACEMENT_RIGHT\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n return PLACEMENT_LEFT\n }\n\n // We need to trim the value because custom properties can also include spaces\n const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end'\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP\n }\n\n return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM\n }\n\n _detectNavbar() {\n return this._element.closest(`.${CLASS_NAME_NAVBAR}`) !== null\n }\n\n _getOffset() {\n const { offset } = this._config\n\n if (typeof offset === 'string') {\n return offset.split(',').map(val => Number.parseInt(val, 10))\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element)\n }\n\n return offset\n }\n\n _getPopperConfig() {\n const defaultBsPopperConfig = {\n placement: this._getPlacement(),\n modifiers: [{\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n },\n {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }]\n }\n\n // Disable Popper if we have a static display\n if (this._config.display === 'static') {\n defaultBsPopperConfig.modifiers = [{\n name: 'applyStyles',\n enabled: false\n }]\n }\n\n return {\n ...defaultBsPopperConfig,\n ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n }\n }\n\n _selectMenuItem({ key, target }) {\n const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(isVisible)\n\n if (!items.length) {\n return\n }\n\n // if target isn't included in items (e.g. when expanding the dropdown)\n // allow cycling to get the last item in case key equals ARROW_UP_KEY\n getNextActiveElement(items, target, key === ARROW_DOWN_KEY, !items.includes(target)).focus()\n }\n\n // Static\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Dropdown.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n })\n }\n\n static clearMenus(event) {\n if (event && (event.button === RIGHT_MOUSE_BUTTON || (event.type === 'keyup' && event.key !== TAB_KEY))) {\n return\n }\n\n const toggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE)\n\n for (let i = 0, len = toggles.length; i < len; i++) {\n const context = Dropdown.getInstance(toggles[i])\n if (!context || context._config.autoClose === false) {\n continue\n }\n\n if (!context._isShown()) {\n continue\n }\n\n const relatedTarget = {\n relatedTarget: context._element\n }\n\n if (event) {\n const composedPath = event.composedPath()\n const isMenuTarget = composedPath.includes(context._menu)\n if (\n composedPath.includes(context._element) ||\n (context._config.autoClose === 'inside' && !isMenuTarget) ||\n (context._config.autoClose === 'outside' && isMenuTarget)\n ) {\n continue\n }\n\n // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n if (context._menu.contains(event.target) && ((event.type === 'keyup' && event.key === TAB_KEY) || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n continue\n }\n\n if (event.type === 'click') {\n relatedTarget.clickEvent = event\n }\n }\n\n context._completeHide(relatedTarget)\n }\n }\n\n static getParentFromElement(element) {\n return getElementFromSelector(element) || element.parentNode\n }\n\n static dataApiKeydownHandler(event) {\n // If not input/textarea:\n // - And not a key in REGEXP_KEYDOWN => not a dropdown command\n // If input/textarea:\n // - If space key => not a dropdown command\n // - If key is other than escape\n // - If key is not up or down => not a dropdown command\n // - If trigger inside the menu => not a dropdown command\n if (/input|textarea/i.test(event.target.tagName) ?\n event.key === SPACE_KEY || (event.key !== ESCAPE_KEY &&\n ((event.key !== ARROW_DOWN_KEY && event.key !== ARROW_UP_KEY) ||\n event.target.closest(SELECTOR_MENU))) :\n !REGEXP_KEYDOWN.test(event.key)) {\n return\n }\n\n const isActive = this.classList.contains(CLASS_NAME_SHOW)\n\n if (!isActive && event.key === ESCAPE_KEY) {\n return\n }\n\n event.preventDefault()\n event.stopPropagation()\n\n if (isDisabled(this)) {\n return\n }\n\n const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE)[0]\n const instance = Dropdown.getOrCreateInstance(getToggleButton)\n\n if (event.key === ESCAPE_KEY) {\n instance.hide()\n return\n }\n\n if (event.key === ARROW_UP_KEY || event.key === ARROW_DOWN_KEY) {\n if (!isActive) {\n instance.show()\n }\n\n instance._selectMenuItem(event)\n return\n }\n\n if (!isActive || event.key === SPACE_KEY) {\n Dropdown.clearMenus()\n }\n }\n}\n\n/**\n * ------------------------------------------------------------------------\n * Data Api implementation\n * ------------------------------------------------------------------------\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_CLICK_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n event.preventDefault()\n Dropdown.getOrCreateInstance(this).toggle()\n})\n\n/**\n * ------------------------------------------------------------------------\n * jQuery\n * ------------------------------------------------------------------------\n * add .Dropdown to jQuery only if jQuery is present\n */\n\ndefineJQueryPlugin(Dropdown)\n\nexport default Dropdown\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport SelectorEngine from '../dom/selector-engine'\nimport Manipulator from '../dom/manipulator'\nimport { isElement } from './index'\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top'\nconst SELECTOR_STICKY_CONTENT = '.sticky-top'\n\nclass ScrollBarHelper {\n constructor() {\n this._element = document.body\n }\n\n getWidth() {\n // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n const documentWidth = document.documentElement.clientWidth\n return Math.abs(window.innerWidth - documentWidth)\n }\n\n hide() {\n const width = this.getWidth()\n this._disableOverFlow()\n // give padding to element to balance the hidden scrollbar width\n this._setElementAttributes(this._element, 'paddingRight', calculatedValue => calculatedValue + width)\n // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n this._setElementAttributes(SELECTOR_FIXED_CONTENT, 'paddingRight', calculatedValue => calculatedValue + width)\n this._setElementAttributes(SELECTOR_STICKY_CONTENT, 'marginRight', calculatedValue => calculatedValue - width)\n }\n\n _disableOverFlow() {\n this._saveInitialAttribute(this._element, 'overflow')\n this._element.style.overflow = 'hidden'\n }\n\n _setElementAttributes(selector, styleProp, callback) {\n const scrollbarWidth = this.getWidth()\n const manipulationCallBack = element => {\n if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n return\n }\n\n this._saveInitialAttribute(element, styleProp)\n const calculatedValue = window.getComputedStyle(element)[styleProp]\n element.style[styleProp] = `${callback(Number.parseFloat(calculatedValue))}px`\n }\n\n this._applyManipulationCallback(selector, manipulationCallBack)\n }\n\n reset() {\n this._resetElementAttributes(this._element, 'overflow')\n this._resetElementAttributes(this._element, 'paddingRight')\n this._resetElementAttributes(SELECTOR_FIXED_CONTENT, 'paddingRight')\n this._resetElementAttributes(SELECTOR_STICKY_CONTENT, 'marginRight')\n }\n\n _saveInitialAttribute(element, styleProp) {\n const actualValue = element.style[styleProp]\n if (actualValue) {\n Manipulator.setDataAttribute(element, styleProp, actualValue)\n }\n }\n\n _resetElementAttributes(selector, styleProp) {\n const manipulationCallBack = element => {\n const value = Manipulator.getDataAttribute(element, styleProp)\n if (typeof value === 'undefined') {\n element.style.removeProperty(styleProp)\n } else {\n Manipulator.removeDataAttribute(element, styleProp)\n element.style[styleProp] = value\n }\n }\n\n this._applyManipulationCallback(selector, manipulationCallBack)\n }\n\n _applyManipulationCallback(selector, callBack) {\n if (isElement(selector)) {\n callBack(selector)\n } else {\n SelectorEngine.find(selector, this._element).forEach(callBack)\n }\n }\n\n isOverflowing() {\n return this.getWidth() > 0\n }\n}\n\nexport default ScrollBarHelper\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler'\nimport { execute, executeAfterTransition, getElement, reflow, typeCheckConfig } from './index'\n\nconst Default = {\n className: 'modal-backdrop',\n isVisible: true, // if false, we use the backdrop helper without adding any element to the dom\n isAnimated: false,\n rootElement: 'body', // give the choice to place backdrop under different elements\n clickCallback: null\n}\n\nconst DefaultType = {\n className: 'string',\n isVisible: 'boolean',\n isAnimated: 'boolean',\n rootElement: '(element|string)',\n clickCallback: '(function|null)'\n}\nconst NAME = 'backdrop'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\n\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME}`\n\nclass Backdrop {\n constructor(config) {\n this._config = this._getConfig(config)\n this._isAppended = false\n this._element = null\n }\n\n show(callback) {\n if (!this._config.isVisible) {\n execute(callback)\n return\n }\n\n this._append()\n\n if (this._config.isAnimated) {\n reflow(this._getElement())\n }\n\n this._getElement().classList.add(CLASS_NAME_SHOW)\n\n this._emulateAnimation(() => {\n execute(callback)\n })\n }\n\n hide(callback) {\n if (!this._config.isVisible) {\n execute(callback)\n return\n }\n\n this._getElement().classList.remove(CLASS_NAME_SHOW)\n\n this._emulateAnimation(() => {\n this.dispose()\n execute(callback)\n })\n }\n\n // Private\n\n _getElement() {\n if (!this._element) {\n const backdrop = document.createElement('div')\n backdrop.className = this._config.className\n if (this._config.isAnimated) {\n backdrop.classList.add(CLASS_NAME_FADE)\n }\n\n this._element = backdrop\n }\n\n return this._element\n }\n\n _getConfig(config) {\n config = {\n ...Default,\n ...(typeof config === 'object' ? config : {})\n }\n\n // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n config.rootElement = getElement(config.rootElement)\n typeCheckConfig(NAME, config, DefaultType)\n return config\n }\n\n _append() {\n if (this._isAppended) {\n return\n }\n\n this._config.rootElement.append(this._getElement())\n\n EventHandler.on(this._getElement(), EVENT_MOUSEDOWN, () => {\n execute(this._config.clickCallback)\n })\n\n this._isAppended = true\n }\n\n dispose() {\n if (!this._isAppended) {\n return\n }\n\n EventHandler.off(this._element, EVENT_MOUSEDOWN)\n\n this._element.remove()\n this._isAppended = false\n }\n\n _emulateAnimation(callback) {\n executeAfterTransition(callback, this._getElement(), this._config.isAnimated)\n }\n}\n\nexport default Backdrop\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler'\nimport SelectorEngine from '../dom/selector-engine'\nimport { typeCheckConfig } from './index'\n\nconst Default = {\n trapElement: null, // The element to trap focus inside of\n autofocus: true\n}\n\nconst DefaultType = {\n trapElement: 'element',\n autofocus: 'boolean'\n}\n\nconst NAME = 'focustrap'\nconst DATA_KEY = 'bs.focustrap'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY}`\n\nconst TAB_KEY = 'Tab'\nconst TAB_NAV_FORWARD = 'forward'\nconst TAB_NAV_BACKWARD = 'backward'\n\nclass FocusTrap {\n constructor(config) {\n this._config = this._getConfig(config)\n this._isActive = false\n this._lastTabNavDirection = null\n }\n\n activate() {\n const { trapElement, autofocus } = this._config\n\n if (this._isActive) {\n return\n }\n\n if (autofocus) {\n trapElement.focus()\n }\n\n EventHandler.off(document, EVENT_KEY) // guard against infinite focus loop\n EventHandler.on(document, EVENT_FOCUSIN, event => this._handleFocusin(event))\n EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event))\n\n this._isActive = true\n }\n\n deactivate() {\n if (!this._isActive) {\n return\n }\n\n this._isActive = false\n EventHandler.off(document, EVENT_KEY)\n }\n\n // Private\n\n _handleFocusin(event) {\n const { target } = event\n const { trapElement } = this._config\n\n if (\n target === document ||\n target === trapElement ||\n trapElement.contains(target)\n ) {\n return\n }\n\n const elements = SelectorEngine.focusableChildren(trapElement)\n\n if (elements.length === 0) {\n trapElement.focus()\n } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n elements[elements.length - 1].focus()\n } else {\n elements[0].focus()\n }\n }\n\n _handleKeydown(event) {\n if (event.key !== TAB_KEY) {\n return\n }\n\n this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD\n }\n\n _getConfig(config) {\n config = {\n ...Default,\n ...(typeof config === 'object' ? config : {})\n }\n typeCheckConfig(NAME, config, DefaultType)\n return config\n }\n}\n\nexport default FocusTrap\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport {\n defineJQueryPlugin,\n getElementFromSelector,\n isRTL,\n isVisible,\n reflow,\n typeCheckConfig\n} from './util/index'\nimport EventHandler from './dom/event-handler'\nimport Manipulator from './dom/manipulator'\nimport SelectorEngine from './dom/selector-engine'\nimport ScrollBarHelper from './util/scrollbar'\nimport BaseComponent from './base-component'\nimport Backdrop from './util/backdrop'\nimport FocusTrap from './util/focustrap'\nimport { enableDismissTrigger } from './util/component-functions'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'modal'\nconst DATA_KEY = 'bs.modal'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst ESCAPE_KEY = 'Escape'\n\nconst Default = {\n backdrop: true,\n keyboard: true,\n focus: true\n}\n\nconst DefaultType = {\n backdrop: '(boolean|string)',\n keyboard: 'boolean',\n focus: 'boolean'\n}\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\nconst EVENT_MOUSEUP_DISMISS = `mouseup.dismiss${EVENT_KEY}`\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_OPEN = 'modal-open'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_STATIC = 'modal-static'\n\nconst SELECTOR_DIALOG = '.modal-dialog'\nconst SELECTOR_MODAL_BODY = '.modal-body'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"modal\"]'\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass Modal extends BaseComponent {\n constructor(element, config) {\n super(element)\n\n this._config = this._getConfig(config)\n this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element)\n this._backdrop = this._initializeBackDrop()\n this._focustrap = this._initializeFocusTrap()\n this._isShown = false\n this._ignoreBackdropClick = false\n this._isTransitioning = false\n this._scrollBar = new ScrollBarHelper()\n }\n\n // Getters\n\n static get Default() {\n return Default\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget)\n }\n\n show(relatedTarget) {\n if (this._isShown || this._isTransitioning) {\n return\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, {\n relatedTarget\n })\n\n if (showEvent.defaultPrevented) {\n return\n }\n\n this._isShown = true\n\n if (this._isAnimated()) {\n this._isTransitioning = true\n }\n\n this._scrollBar.hide()\n\n document.body.classList.add(CLASS_NAME_OPEN)\n\n this._adjustDialog()\n\n this._setEscapeEvent()\n this._setResizeEvent()\n\n EventHandler.on(this._dialog, EVENT_MOUSEDOWN_DISMISS, () => {\n EventHandler.one(this._element, EVENT_MOUSEUP_DISMISS, event => {\n if (event.target === this._element) {\n this._ignoreBackdropClick = true\n }\n })\n })\n\n this._showBackdrop(() => this._showElement(relatedTarget))\n }\n\n hide() {\n if (!this._isShown || this._isTransitioning) {\n return\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n if (hideEvent.defaultPrevented) {\n return\n }\n\n this._isShown = false\n const isAnimated = this._isAnimated()\n\n if (isAnimated) {\n this._isTransitioning = true\n }\n\n this._setEscapeEvent()\n this._setResizeEvent()\n\n this._focustrap.deactivate()\n\n this._element.classList.remove(CLASS_NAME_SHOW)\n\n EventHandler.off(this._element, EVENT_CLICK_DISMISS)\n EventHandler.off(this._dialog, EVENT_MOUSEDOWN_DISMISS)\n\n this._queueCallback(() => this._hideModal(), this._element, isAnimated)\n }\n\n dispose() {\n [window, this._dialog]\n .forEach(htmlElement => EventHandler.off(htmlElement, EVENT_KEY))\n\n this._backdrop.dispose()\n this._focustrap.deactivate()\n super.dispose()\n }\n\n handleUpdate() {\n this._adjustDialog()\n }\n\n // Private\n\n _initializeBackDrop() {\n return new Backdrop({\n isVisible: Boolean(this._config.backdrop), // 'static' option will be translated to true, and booleans will keep their value\n isAnimated: this._isAnimated()\n })\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n })\n }\n\n _getConfig(config) {\n config = {\n ...Default,\n ...Manipulator.getDataAttributes(this._element),\n ...(typeof config === 'object' ? config : {})\n }\n typeCheckConfig(NAME, config, DefaultType)\n return config\n }\n\n _showElement(relatedTarget) {\n const isAnimated = this._isAnimated()\n const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog)\n\n if (!this._element.parentNode || this._element.parentNode.nodeType !== Node.ELEMENT_NODE) {\n // Don't move modal's DOM position\n document.body.append(this._element)\n }\n\n this._element.style.display = 'block'\n this._element.removeAttribute('aria-hidden')\n this._element.setAttribute('aria-modal', true)\n this._element.setAttribute('role', 'dialog')\n this._element.scrollTop = 0\n\n if (modalBody) {\n modalBody.scrollTop = 0\n }\n\n if (isAnimated) {\n reflow(this._element)\n }\n\n this._element.classList.add(CLASS_NAME_SHOW)\n\n const transitionComplete = () => {\n if (this._config.focus) {\n this._focustrap.activate()\n }\n\n this._isTransitioning = false\n EventHandler.trigger(this._element, EVENT_SHOWN, {\n relatedTarget\n })\n }\n\n this._queueCallback(transitionComplete, this._dialog, isAnimated)\n }\n\n _setEscapeEvent() {\n if (this._isShown) {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (this._config.keyboard && event.key === ESCAPE_KEY) {\n event.preventDefault()\n this.hide()\n } else if (!this._config.keyboard && event.key === ESCAPE_KEY) {\n this._triggerBackdropTransition()\n }\n })\n } else {\n EventHandler.off(this._element, EVENT_KEYDOWN_DISMISS)\n }\n }\n\n _setResizeEvent() {\n if (this._isShown) {\n EventHandler.on(window, EVENT_RESIZE, () => this._adjustDialog())\n } else {\n EventHandler.off(window, EVENT_RESIZE)\n }\n }\n\n _hideModal() {\n this._element.style.display = 'none'\n this._element.setAttribute('aria-hidden', true)\n this._element.removeAttribute('aria-modal')\n this._element.removeAttribute('role')\n this._isTransitioning = false\n this._backdrop.hide(() => {\n document.body.classList.remove(CLASS_NAME_OPEN)\n this._resetAdjustments()\n this._scrollBar.reset()\n EventHandler.trigger(this._element, EVENT_HIDDEN)\n })\n }\n\n _showBackdrop(callback) {\n EventHandler.on(this._element, EVENT_CLICK_DISMISS, event => {\n if (this._ignoreBackdropClick) {\n this._ignoreBackdropClick = false\n return\n }\n\n if (event.target !== event.currentTarget) {\n return\n }\n\n if (this._config.backdrop === true) {\n this.hide()\n } else if (this._config.backdrop === 'static') {\n this._triggerBackdropTransition()\n }\n })\n\n this._backdrop.show(callback)\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_FADE)\n }\n\n _triggerBackdropTransition() {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n if (hideEvent.defaultPrevented) {\n return\n }\n\n const { classList, scrollHeight, style } = this._element\n const isModalOverflowing = scrollHeight > document.documentElement.clientHeight\n\n // return if the following background transition hasn't yet completed\n if ((!isModalOverflowing && style.overflowY === 'hidden') || classList.contains(CLASS_NAME_STATIC)) {\n return\n }\n\n if (!isModalOverflowing) {\n style.overflowY = 'hidden'\n }\n\n classList.add(CLASS_NAME_STATIC)\n this._queueCallback(() => {\n classList.remove(CLASS_NAME_STATIC)\n if (!isModalOverflowing) {\n this._queueCallback(() => {\n style.overflowY = ''\n }, this._dialog)\n }\n }, this._dialog)\n\n this._element.focus()\n }\n\n // ----------------------------------------------------------------------\n // the following methods are used to handle overflowing modals\n // ----------------------------------------------------------------------\n\n _adjustDialog() {\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n const scrollbarWidth = this._scrollBar.getWidth()\n const isBodyOverflowing = scrollbarWidth > 0\n\n if ((!isBodyOverflowing && isModalOverflowing && !isRTL()) || (isBodyOverflowing && !isModalOverflowing && isRTL())) {\n this._element.style.paddingLeft = `${scrollbarWidth}px`\n }\n\n if ((isBodyOverflowing && !isModalOverflowing && !isRTL()) || (!isBodyOverflowing && isModalOverflowing && isRTL())) {\n this._element.style.paddingRight = `${scrollbarWidth}px`\n }\n }\n\n _resetAdjustments() {\n this._element.style.paddingLeft = ''\n this._element.style.paddingRight = ''\n }\n\n // Static\n\n static jQueryInterface(config, relatedTarget) {\n return this.each(function () {\n const data = Modal.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config](relatedTarget)\n })\n }\n}\n\n/**\n * ------------------------------------------------------------------------\n * Data Api implementation\n * ------------------------------------------------------------------------\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n const target = getElementFromSelector(this)\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault()\n }\n\n EventHandler.one(target, EVENT_SHOW, showEvent => {\n if (showEvent.defaultPrevented) {\n // only register focus restorer if modal will actually get shown\n return\n }\n\n EventHandler.one(target, EVENT_HIDDEN, () => {\n if (isVisible(this)) {\n this.focus()\n }\n })\n })\n\n const data = Modal.getOrCreateInstance(target)\n\n data.toggle(this)\n})\n\nenableDismissTrigger(Modal)\n\n/**\n * ------------------------------------------------------------------------\n * jQuery\n * ------------------------------------------------------------------------\n * add .Modal to jQuery only if jQuery is present\n */\n\ndefineJQueryPlugin(Modal)\n\nexport default Modal\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport {\n defineJQueryPlugin,\n getElementFromSelector,\n isDisabled,\n isVisible,\n typeCheckConfig\n} from './util/index'\nimport ScrollBarHelper from './util/scrollbar'\nimport EventHandler from './dom/event-handler'\nimport BaseComponent from './base-component'\nimport SelectorEngine from './dom/selector-engine'\nimport Manipulator from './dom/manipulator'\nimport Backdrop from './util/backdrop'\nimport FocusTrap from './util/focustrap'\nimport { enableDismissTrigger } from './util/component-functions'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'offcanvas'\nconst DATA_KEY = 'bs.offcanvas'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst ESCAPE_KEY = 'Escape'\n\nconst Default = {\n backdrop: true,\n keyboard: true,\n scroll: false\n}\n\nconst DefaultType = {\n backdrop: 'boolean',\n keyboard: 'boolean',\n scroll: 'boolean'\n}\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop'\nconst OPEN_SELECTOR = '.offcanvas.show'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"offcanvas\"]'\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass Offcanvas extends BaseComponent {\n constructor(element, config) {\n super(element)\n\n this._config = this._getConfig(config)\n this._isShown = false\n this._backdrop = this._initializeBackDrop()\n this._focustrap = this._initializeFocusTrap()\n this._addEventListeners()\n }\n\n // Getters\n\n static get NAME() {\n return NAME\n }\n\n static get Default() {\n return Default\n }\n\n // Public\n\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget)\n }\n\n show(relatedTarget) {\n if (this._isShown) {\n return\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, { relatedTarget })\n\n if (showEvent.defaultPrevented) {\n return\n }\n\n this._isShown = true\n this._element.style.visibility = 'visible'\n\n this._backdrop.show()\n\n if (!this._config.scroll) {\n new ScrollBarHelper().hide()\n }\n\n this._element.removeAttribute('aria-hidden')\n this._element.setAttribute('aria-modal', true)\n this._element.setAttribute('role', 'dialog')\n this._element.classList.add(CLASS_NAME_SHOW)\n\n const completeCallBack = () => {\n if (!this._config.scroll) {\n this._focustrap.activate()\n }\n\n EventHandler.trigger(this._element, EVENT_SHOWN, { relatedTarget })\n }\n\n this._queueCallback(completeCallBack, this._element, true)\n }\n\n hide() {\n if (!this._isShown) {\n return\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n if (hideEvent.defaultPrevented) {\n return\n }\n\n this._focustrap.deactivate()\n this._element.blur()\n this._isShown = false\n this._element.classList.remove(CLASS_NAME_SHOW)\n this._backdrop.hide()\n\n const completeCallback = () => {\n this._element.setAttribute('aria-hidden', true)\n this._element.removeAttribute('aria-modal')\n this._element.removeAttribute('role')\n this._element.style.visibility = 'hidden'\n\n if (!this._config.scroll) {\n new ScrollBarHelper().reset()\n }\n\n EventHandler.trigger(this._element, EVENT_HIDDEN)\n }\n\n this._queueCallback(completeCallback, this._element, true)\n }\n\n dispose() {\n this._backdrop.dispose()\n this._focustrap.deactivate()\n super.dispose()\n }\n\n // Private\n\n _getConfig(config) {\n config = {\n ...Default,\n ...Manipulator.getDataAttributes(this._element),\n ...(typeof config === 'object' ? config : {})\n }\n typeCheckConfig(NAME, config, DefaultType)\n return config\n }\n\n _initializeBackDrop() {\n return new Backdrop({\n className: CLASS_NAME_BACKDROP,\n isVisible: this._config.backdrop,\n isAnimated: true,\n rootElement: this._element.parentNode,\n clickCallback: () => this.hide()\n })\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n })\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (this._config.keyboard && event.key === ESCAPE_KEY) {\n this.hide()\n }\n })\n }\n\n // Static\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Offcanvas.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config](this)\n })\n }\n}\n\n/**\n * ------------------------------------------------------------------------\n * Data Api implementation\n * ------------------------------------------------------------------------\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n const target = getElementFromSelector(this)\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault()\n }\n\n if (isDisabled(this)) {\n return\n }\n\n EventHandler.one(target, EVENT_HIDDEN, () => {\n // focus on trigger when it is closed\n if (isVisible(this)) {\n this.focus()\n }\n })\n\n // avoid conflict when clicking a toggler of an offcanvas, while another is open\n const allReadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n if (allReadyOpen && allReadyOpen !== target) {\n Offcanvas.getInstance(allReadyOpen).hide()\n }\n\n const data = Offcanvas.getOrCreateInstance(target)\n data.toggle(this)\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () =>\n SelectorEngine.find(OPEN_SELECTOR).forEach(el => Offcanvas.getOrCreateInstance(el).show())\n)\n\nenableDismissTrigger(Offcanvas)\n/**\n * ------------------------------------------------------------------------\n * jQuery\n * ------------------------------------------------------------------------\n */\n\ndefineJQueryPlugin(Offcanvas)\n\nexport default Offcanvas\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst uriAttrs = new Set([\n 'background',\n 'cite',\n 'href',\n 'itemtype',\n 'longdesc',\n 'poster',\n 'src',\n 'xlink:href'\n])\n\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i\n\n/**\n * A pattern that recognizes a commonly useful subset of URLs that are safe.\n *\n * Shoutout to Angular 7 https://github.com/angular/angular/blob/7.2.4/packages/core/src/sanitization/url_sanitizer.ts\n */\nconst SAFE_URL_PATTERN = /^(?:(?:https?|mailto|ftp|tel|file):|[^#&/:?]*(?:[#/?]|$))/i\n\n/**\n * A pattern that matches safe data URLs. Only matches image, video and audio types.\n *\n * Shoutout to Angular 7 https://github.com/angular/angular/blob/7.2.4/packages/core/src/sanitization/url_sanitizer.ts\n */\nconst DATA_URL_PATTERN = /^data:(?:image\\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\\/(?:mpeg|mp4|ogg|webm)|audio\\/(?:mp3|oga|ogg|opus));base64,[\\d+/a-z]+=*$/i\n\nconst allowedAttribute = (attr, allowedAttributeList) => {\n const attrName = attr.nodeName.toLowerCase()\n\n if (allowedAttributeList.includes(attrName)) {\n if (uriAttrs.has(attrName)) {\n return Boolean(SAFE_URL_PATTERN.test(attr.nodeValue) || DATA_URL_PATTERN.test(attr.nodeValue))\n }\n\n return true\n }\n\n const regExp = allowedAttributeList.filter(attrRegex => attrRegex instanceof RegExp)\n\n // Check if a regular expression validates the attribute.\n for (let i = 0, len = regExp.length; i < len; i++) {\n if (regExp[i].test(attrName)) {\n return true\n }\n }\n\n return false\n}\n\nexport const DefaultAllowlist = {\n // Global attributes allowed on any supplied element below.\n '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n a: ['target', 'href', 'title', 'rel'],\n area: [],\n b: [],\n br: [],\n col: [],\n code: [],\n div: [],\n em: [],\n hr: [],\n h1: [],\n h2: [],\n h3: [],\n h4: [],\n h5: [],\n h6: [],\n i: [],\n img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n li: [],\n ol: [],\n p: [],\n pre: [],\n s: [],\n small: [],\n span: [],\n sub: [],\n sup: [],\n strong: [],\n u: [],\n ul: []\n}\n\nexport function sanitizeHtml(unsafeHtml, allowList, sanitizeFn) {\n if (!unsafeHtml.length) {\n return unsafeHtml\n }\n\n if (sanitizeFn && typeof sanitizeFn === 'function') {\n return sanitizeFn(unsafeHtml)\n }\n\n const domParser = new window.DOMParser()\n const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html')\n const allowlistKeys = Object.keys(allowList)\n const elements = [].concat(...createdDocument.body.querySelectorAll('*'))\n\n for (let i = 0, len = elements.length; i < len; i++) {\n const el = elements[i]\n const elName = el.nodeName.toLowerCase()\n\n if (!allowlistKeys.includes(elName)) {\n el.remove()\n\n continue\n }\n\n const attributeList = [].concat(...el.attributes)\n const allowedAttributes = [].concat(allowList['*'] || [], allowList[elName] || [])\n\n attributeList.forEach(attr => {\n if (!allowedAttribute(attr, allowedAttributes)) {\n el.removeAttribute(attr.nodeName)\n }\n })\n }\n\n return createdDocument.body.innerHTML\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\n\nimport {\n defineJQueryPlugin,\n findShadowRoot,\n getElement,\n getUID,\n isElement,\n isRTL,\n noop,\n typeCheckConfig\n} from './util/index'\nimport { DefaultAllowlist, sanitizeHtml } from './util/sanitizer'\nimport Data from './dom/data'\nimport EventHandler from './dom/event-handler'\nimport Manipulator from './dom/manipulator'\nimport SelectorEngine from './dom/selector-engine'\nimport BaseComponent from './base-component'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'tooltip'\nconst DATA_KEY = 'bs.tooltip'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst CLASS_PREFIX = 'bs-tooltip'\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn'])\n\nconst DefaultType = {\n animation: 'boolean',\n template: 'string',\n title: '(string|element|function)',\n trigger: 'string',\n delay: '(number|object)',\n html: 'boolean',\n selector: '(string|boolean)',\n placement: '(string|function)',\n offset: '(array|string|function)',\n container: '(string|element|boolean)',\n fallbackPlacements: 'array',\n boundary: '(string|element)',\n customClass: '(string|function)',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n allowList: 'object',\n popperConfig: '(null|object|function)'\n}\n\nconst AttachmentMap = {\n AUTO: 'auto',\n TOP: 'top',\n RIGHT: isRTL() ? 'left' : 'right',\n BOTTOM: 'bottom',\n LEFT: isRTL() ? 'right' : 'left'\n}\n\nconst Default = {\n animation: true,\n template: '
' +\n '
' +\n '
' +\n '
',\n trigger: 'hover focus',\n title: '',\n delay: 0,\n html: false,\n selector: false,\n placement: 'top',\n offset: [0, 0],\n container: false,\n fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n boundary: 'clippingParents',\n customClass: '',\n sanitize: true,\n sanitizeFn: null,\n allowList: DefaultAllowlist,\n popperConfig: null\n}\n\nconst Event = {\n HIDE: `hide${EVENT_KEY}`,\n HIDDEN: `hidden${EVENT_KEY}`,\n SHOW: `show${EVENT_KEY}`,\n SHOWN: `shown${EVENT_KEY}`,\n INSERTED: `inserted${EVENT_KEY}`,\n CLICK: `click${EVENT_KEY}`,\n FOCUSIN: `focusin${EVENT_KEY}`,\n FOCUSOUT: `focusout${EVENT_KEY}`,\n MOUSEENTER: `mouseenter${EVENT_KEY}`,\n MOUSELEAVE: `mouseleave${EVENT_KEY}`\n}\n\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_MODAL = 'modal'\nconst CLASS_NAME_SHOW = 'show'\n\nconst HOVER_STATE_SHOW = 'show'\nconst HOVER_STATE_OUT = 'out'\n\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner'\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`\n\nconst EVENT_MODAL_HIDE = 'hide.bs.modal'\n\nconst TRIGGER_HOVER = 'hover'\nconst TRIGGER_FOCUS = 'focus'\nconst TRIGGER_CLICK = 'click'\nconst TRIGGER_MANUAL = 'manual'\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass Tooltip extends BaseComponent {\n constructor(element, config) {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)')\n }\n\n super(element)\n\n // private\n this._isEnabled = true\n this._timeout = 0\n this._hoverState = ''\n this._activeTrigger = {}\n this._popper = null\n\n // Protected\n this._config = this._getConfig(config)\n this.tip = null\n\n this._setListeners()\n }\n\n // Getters\n\n static get Default() {\n return Default\n }\n\n static get NAME() {\n return NAME\n }\n\n static get Event() {\n return Event\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n // Public\n\n enable() {\n this._isEnabled = true\n }\n\n disable() {\n this._isEnabled = false\n }\n\n toggleEnabled() {\n this._isEnabled = !this._isEnabled\n }\n\n toggle(event) {\n if (!this._isEnabled) {\n return\n }\n\n if (event) {\n const context = this._initializeOnDelegatedTarget(event)\n\n context._activeTrigger.click = !context._activeTrigger.click\n\n if (context._isWithActiveTrigger()) {\n context._enter(null, context)\n } else {\n context._leave(null, context)\n }\n } else {\n if (this.getTipElement().classList.contains(CLASS_NAME_SHOW)) {\n this._leave(null, this)\n return\n }\n\n this._enter(null, this)\n }\n }\n\n dispose() {\n clearTimeout(this._timeout)\n\n EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n\n if (this.tip) {\n this.tip.remove()\n }\n\n if (this._popper) {\n this._popper.destroy()\n }\n\n super.dispose()\n }\n\n show() {\n if (this._element.style.display === 'none') {\n throw new Error('Please use show on visible elements')\n }\n\n if (!(this.isWithContent() && this._isEnabled)) {\n return\n }\n\n const showEvent = EventHandler.trigger(this._element, this.constructor.Event.SHOW)\n const shadowRoot = findShadowRoot(this._element)\n const isInTheDom = shadowRoot === null ?\n this._element.ownerDocument.documentElement.contains(this._element) :\n shadowRoot.contains(this._element)\n\n if (showEvent.defaultPrevented || !isInTheDom) {\n return\n }\n\n const tip = this.getTipElement()\n const tipId = getUID(this.constructor.NAME)\n\n tip.setAttribute('id', tipId)\n this._element.setAttribute('aria-describedby', tipId)\n\n if (this._config.animation) {\n tip.classList.add(CLASS_NAME_FADE)\n }\n\n const placement = typeof this._config.placement === 'function' ?\n this._config.placement.call(this, tip, this._element) :\n this._config.placement\n\n const attachment = this._getAttachment(placement)\n this._addAttachmentClass(attachment)\n\n const { container } = this._config\n Data.set(tip, this.constructor.DATA_KEY, this)\n\n if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n container.append(tip)\n EventHandler.trigger(this._element, this.constructor.Event.INSERTED)\n }\n\n if (this._popper) {\n this._popper.update()\n } else {\n this._popper = Popper.createPopper(this._element, tip, this._getPopperConfig(attachment))\n }\n\n tip.classList.add(CLASS_NAME_SHOW)\n\n const customClass = this._resolvePossibleFunction(this._config.customClass)\n if (customClass) {\n tip.classList.add(...customClass.split(' '))\n }\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement) {\n [].concat(...document.body.children).forEach(element => {\n EventHandler.on(element, 'mouseover', noop)\n })\n }\n\n const complete = () => {\n const prevHoverState = this._hoverState\n\n this._hoverState = null\n EventHandler.trigger(this._element, this.constructor.Event.SHOWN)\n\n if (prevHoverState === HOVER_STATE_OUT) {\n this._leave(null, this)\n }\n }\n\n const isAnimated = this.tip.classList.contains(CLASS_NAME_FADE)\n this._queueCallback(complete, this.tip, isAnimated)\n }\n\n hide() {\n if (!this._popper) {\n return\n }\n\n const tip = this.getTipElement()\n const complete = () => {\n if (this._isWithActiveTrigger()) {\n return\n }\n\n if (this._hoverState !== HOVER_STATE_SHOW) {\n tip.remove()\n }\n\n this._cleanTipClass()\n this._element.removeAttribute('aria-describedby')\n EventHandler.trigger(this._element, this.constructor.Event.HIDDEN)\n\n if (this._popper) {\n this._popper.destroy()\n this._popper = null\n }\n }\n\n const hideEvent = EventHandler.trigger(this._element, this.constructor.Event.HIDE)\n if (hideEvent.defaultPrevented) {\n return\n }\n\n tip.classList.remove(CLASS_NAME_SHOW)\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n [].concat(...document.body.children)\n .forEach(element => EventHandler.off(element, 'mouseover', noop))\n }\n\n this._activeTrigger[TRIGGER_CLICK] = false\n this._activeTrigger[TRIGGER_FOCUS] = false\n this._activeTrigger[TRIGGER_HOVER] = false\n\n const isAnimated = this.tip.classList.contains(CLASS_NAME_FADE)\n this._queueCallback(complete, this.tip, isAnimated)\n this._hoverState = ''\n }\n\n update() {\n if (this._popper !== null) {\n this._popper.update()\n }\n }\n\n // Protected\n\n isWithContent() {\n return Boolean(this.getTitle())\n }\n\n getTipElement() {\n if (this.tip) {\n return this.tip\n }\n\n const element = document.createElement('div')\n element.innerHTML = this._config.template\n\n const tip = element.children[0]\n this.setContent(tip)\n tip.classList.remove(CLASS_NAME_FADE, CLASS_NAME_SHOW)\n\n this.tip = tip\n return this.tip\n }\n\n setContent(tip) {\n this._sanitizeAndSetContent(tip, this.getTitle(), SELECTOR_TOOLTIP_INNER)\n }\n\n _sanitizeAndSetContent(template, content, selector) {\n const templateElement = SelectorEngine.findOne(selector, template)\n\n if (!content && templateElement) {\n templateElement.remove()\n return\n }\n\n // we use append for html objects to maintain js events\n this.setElementContent(templateElement, content)\n }\n\n setElementContent(element, content) {\n if (element === null) {\n return\n }\n\n if (isElement(content)) {\n content = getElement(content)\n\n // content is a DOM node or a jQuery\n if (this._config.html) {\n if (content.parentNode !== element) {\n element.innerHTML = ''\n element.append(content)\n }\n } else {\n element.textContent = content.textContent\n }\n\n return\n }\n\n if (this._config.html) {\n if (this._config.sanitize) {\n content = sanitizeHtml(content, this._config.allowList, this._config.sanitizeFn)\n }\n\n element.innerHTML = content\n } else {\n element.textContent = content\n }\n }\n\n getTitle() {\n const title = this._element.getAttribute('data-bs-original-title') || this._config.title\n\n return this._resolvePossibleFunction(title)\n }\n\n updateAttachment(attachment) {\n if (attachment === 'right') {\n return 'end'\n }\n\n if (attachment === 'left') {\n return 'start'\n }\n\n return attachment\n }\n\n // Private\n\n _initializeOnDelegatedTarget(event, context) {\n return context || this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig())\n }\n\n _getOffset() {\n const { offset } = this._config\n\n if (typeof offset === 'string') {\n return offset.split(',').map(val => Number.parseInt(val, 10))\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element)\n }\n\n return offset\n }\n\n _resolvePossibleFunction(content) {\n return typeof content === 'function' ? content.call(this._element) : content\n }\n\n _getPopperConfig(attachment) {\n const defaultBsPopperConfig = {\n placement: attachment,\n modifiers: [\n {\n name: 'flip',\n options: {\n fallbackPlacements: this._config.fallbackPlacements\n }\n },\n {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n },\n {\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n },\n {\n name: 'arrow',\n options: {\n element: `.${this.constructor.NAME}-arrow`\n }\n },\n {\n name: 'onChange',\n enabled: true,\n phase: 'afterWrite',\n fn: data => this._handlePopperPlacementChange(data)\n }\n ],\n onFirstUpdate: data => {\n if (data.options.placement !== data.placement) {\n this._handlePopperPlacementChange(data)\n }\n }\n }\n\n return {\n ...defaultBsPopperConfig,\n ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n }\n }\n\n _addAttachmentClass(attachment) {\n this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(attachment)}`)\n }\n\n _getAttachment(placement) {\n return AttachmentMap[placement.toUpperCase()]\n }\n\n _setListeners() {\n const triggers = this._config.trigger.split(' ')\n\n triggers.forEach(trigger => {\n if (trigger === 'click') {\n EventHandler.on(this._element, this.constructor.Event.CLICK, this._config.selector, event => this.toggle(event))\n } else if (trigger !== TRIGGER_MANUAL) {\n const eventIn = trigger === TRIGGER_HOVER ?\n this.constructor.Event.MOUSEENTER :\n this.constructor.Event.FOCUSIN\n const eventOut = trigger === TRIGGER_HOVER ?\n this.constructor.Event.MOUSELEAVE :\n this.constructor.Event.FOCUSOUT\n\n EventHandler.on(this._element, eventIn, this._config.selector, event => this._enter(event))\n EventHandler.on(this._element, eventOut, this._config.selector, event => this._leave(event))\n }\n })\n\n this._hideModalHandler = () => {\n if (this._element) {\n this.hide()\n }\n }\n\n EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n\n if (this._config.selector) {\n this._config = {\n ...this._config,\n trigger: 'manual',\n selector: ''\n }\n } else {\n this._fixTitle()\n }\n }\n\n _fixTitle() {\n const title = this._element.getAttribute('title')\n const originalTitleType = typeof this._element.getAttribute('data-bs-original-title')\n\n if (title || originalTitleType !== 'string') {\n this._element.setAttribute('data-bs-original-title', title || '')\n if (title && !this._element.getAttribute('aria-label') && !this._element.textContent) {\n this._element.setAttribute('aria-label', title)\n }\n\n this._element.setAttribute('title', '')\n }\n }\n\n _enter(event, context) {\n context = this._initializeOnDelegatedTarget(event, context)\n\n if (event) {\n context._activeTrigger[\n event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER\n ] = true\n }\n\n if (context.getTipElement().classList.contains(CLASS_NAME_SHOW) || context._hoverState === HOVER_STATE_SHOW) {\n context._hoverState = HOVER_STATE_SHOW\n return\n }\n\n clearTimeout(context._timeout)\n\n context._hoverState = HOVER_STATE_SHOW\n\n if (!context._config.delay || !context._config.delay.show) {\n context.show()\n return\n }\n\n context._timeout = setTimeout(() => {\n if (context._hoverState === HOVER_STATE_SHOW) {\n context.show()\n }\n }, context._config.delay.show)\n }\n\n _leave(event, context) {\n context = this._initializeOnDelegatedTarget(event, context)\n\n if (event) {\n context._activeTrigger[\n event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER\n ] = context._element.contains(event.relatedTarget)\n }\n\n if (context._isWithActiveTrigger()) {\n return\n }\n\n clearTimeout(context._timeout)\n\n context._hoverState = HOVER_STATE_OUT\n\n if (!context._config.delay || !context._config.delay.hide) {\n context.hide()\n return\n }\n\n context._timeout = setTimeout(() => {\n if (context._hoverState === HOVER_STATE_OUT) {\n context.hide()\n }\n }, context._config.delay.hide)\n }\n\n _isWithActiveTrigger() {\n for (const trigger in this._activeTrigger) {\n if (this._activeTrigger[trigger]) {\n return true\n }\n }\n\n return false\n }\n\n _getConfig(config) {\n const dataAttributes = Manipulator.getDataAttributes(this._element)\n\n Object.keys(dataAttributes).forEach(dataAttr => {\n if (DISALLOWED_ATTRIBUTES.has(dataAttr)) {\n delete dataAttributes[dataAttr]\n }\n })\n\n config = {\n ...this.constructor.Default,\n ...dataAttributes,\n ...(typeof config === 'object' && config ? config : {})\n }\n\n config.container = config.container === false ? document.body : getElement(config.container)\n\n if (typeof config.delay === 'number') {\n config.delay = {\n show: config.delay,\n hide: config.delay\n }\n }\n\n if (typeof config.title === 'number') {\n config.title = config.title.toString()\n }\n\n if (typeof config.content === 'number') {\n config.content = config.content.toString()\n }\n\n typeCheckConfig(NAME, config, this.constructor.DefaultType)\n\n if (config.sanitize) {\n config.template = sanitizeHtml(config.template, config.allowList, config.sanitizeFn)\n }\n\n return config\n }\n\n _getDelegateConfig() {\n const config = {}\n\n for (const key in this._config) {\n if (this.constructor.Default[key] !== this._config[key]) {\n config[key] = this._config[key]\n }\n }\n\n // In the future can be replaced with:\n // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n // `Object.fromEntries(keysWithDifferentValues)`\n return config\n }\n\n _cleanTipClass() {\n const tip = this.getTipElement()\n const basicClassPrefixRegex = new RegExp(`(^|\\\\s)${this._getBasicClassPrefix()}\\\\S+`, 'g')\n const tabClass = tip.getAttribute('class').match(basicClassPrefixRegex)\n if (tabClass !== null && tabClass.length > 0) {\n tabClass.map(token => token.trim())\n .forEach(tClass => tip.classList.remove(tClass))\n }\n }\n\n _getBasicClassPrefix() {\n return CLASS_PREFIX\n }\n\n _handlePopperPlacementChange(popperData) {\n const { state } = popperData\n\n if (!state) {\n return\n }\n\n this.tip = state.elements.popper\n this._cleanTipClass()\n this._addAttachmentClass(this._getAttachment(state.placement))\n }\n\n // Static\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Tooltip.getOrCreateInstance(this, config)\n\n if (typeof config === 'string') {\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n }\n })\n }\n}\n\n/**\n * ------------------------------------------------------------------------\n * jQuery\n * ------------------------------------------------------------------------\n * add .Tooltip to jQuery only if jQuery is present\n */\n\ndefineJQueryPlugin(Tooltip)\n\nexport default Tooltip\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { defineJQueryPlugin } from './util/index'\nimport Tooltip from './tooltip'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'popover'\nconst DATA_KEY = 'bs.popover'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst CLASS_PREFIX = 'bs-popover'\n\nconst Default = {\n ...Tooltip.Default,\n placement: 'right',\n offset: [0, 8],\n trigger: 'click',\n content: '',\n template: '
' +\n '
' +\n '

' +\n '
' +\n '
'\n}\n\nconst DefaultType = {\n ...Tooltip.DefaultType,\n content: '(string|element|function)'\n}\n\nconst Event = {\n HIDE: `hide${EVENT_KEY}`,\n HIDDEN: `hidden${EVENT_KEY}`,\n SHOW: `show${EVENT_KEY}`,\n SHOWN: `shown${EVENT_KEY}`,\n INSERTED: `inserted${EVENT_KEY}`,\n CLICK: `click${EVENT_KEY}`,\n FOCUSIN: `focusin${EVENT_KEY}`,\n FOCUSOUT: `focusout${EVENT_KEY}`,\n MOUSEENTER: `mouseenter${EVENT_KEY}`,\n MOUSELEAVE: `mouseleave${EVENT_KEY}`\n}\n\nconst SELECTOR_TITLE = '.popover-header'\nconst SELECTOR_CONTENT = '.popover-body'\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass Popover extends Tooltip {\n // Getters\n\n static get Default() {\n return Default\n }\n\n static get NAME() {\n return NAME\n }\n\n static get Event() {\n return Event\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n // Overrides\n\n isWithContent() {\n return this.getTitle() || this._getContent()\n }\n\n setContent(tip) {\n this._sanitizeAndSetContent(tip, this.getTitle(), SELECTOR_TITLE)\n this._sanitizeAndSetContent(tip, this._getContent(), SELECTOR_CONTENT)\n }\n\n // Private\n\n _getContent() {\n return this._resolvePossibleFunction(this._config.content)\n }\n\n _getBasicClassPrefix() {\n return CLASS_PREFIX\n }\n\n // Static\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Popover.getOrCreateInstance(this, config)\n\n if (typeof config === 'string') {\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n }\n })\n }\n}\n\n/**\n * ------------------------------------------------------------------------\n * jQuery\n * ------------------------------------------------------------------------\n * add .Popover to jQuery only if jQuery is present\n */\n\ndefineJQueryPlugin(Popover)\n\nexport default Popover\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport {\n defineJQueryPlugin,\n getElement,\n getSelectorFromElement,\n typeCheckConfig\n} from './util/index'\nimport EventHandler from './dom/event-handler'\nimport Manipulator from './dom/manipulator'\nimport SelectorEngine from './dom/selector-engine'\nimport BaseComponent from './base-component'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'scrollspy'\nconst DATA_KEY = 'bs.scrollspy'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst Default = {\n offset: 10,\n method: 'auto',\n target: ''\n}\n\nconst DefaultType = {\n offset: 'number',\n method: 'string',\n target: '(string|element)'\n}\n\nconst EVENT_ACTIVATE = `activate${EVENT_KEY}`\nconst EVENT_SCROLL = `scroll${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item'\nconst CLASS_NAME_ACTIVE = 'active'\n\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]'\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group'\nconst SELECTOR_NAV_LINKS = '.nav-link'\nconst SELECTOR_NAV_ITEMS = '.nav-item'\nconst SELECTOR_LIST_ITEMS = '.list-group-item'\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}, .${CLASS_NAME_DROPDOWN_ITEM}`\nconst SELECTOR_DROPDOWN = '.dropdown'\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle'\n\nconst METHOD_OFFSET = 'offset'\nconst METHOD_POSITION = 'position'\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass ScrollSpy extends BaseComponent {\n constructor(element, config) {\n super(element)\n this._scrollElement = this._element.tagName === 'BODY' ? window : this._element\n this._config = this._getConfig(config)\n this._offsets = []\n this._targets = []\n this._activeTarget = null\n this._scrollHeight = 0\n\n EventHandler.on(this._scrollElement, EVENT_SCROLL, () => this._process())\n\n this.refresh()\n this._process()\n }\n\n // Getters\n\n static get Default() {\n return Default\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n\n refresh() {\n const autoMethod = this._scrollElement === this._scrollElement.window ?\n METHOD_OFFSET :\n METHOD_POSITION\n\n const offsetMethod = this._config.method === 'auto' ?\n autoMethod :\n this._config.method\n\n const offsetBase = offsetMethod === METHOD_POSITION ?\n this._getScrollTop() :\n 0\n\n this._offsets = []\n this._targets = []\n this._scrollHeight = this._getScrollHeight()\n\n const targets = SelectorEngine.find(SELECTOR_LINK_ITEMS, this._config.target)\n\n targets.map(element => {\n const targetSelector = getSelectorFromElement(element)\n const target = targetSelector ? SelectorEngine.findOne(targetSelector) : null\n\n if (target) {\n const targetBCR = target.getBoundingClientRect()\n if (targetBCR.width || targetBCR.height) {\n return [\n Manipulator[offsetMethod](target).top + offsetBase,\n targetSelector\n ]\n }\n }\n\n return null\n })\n .filter(item => item)\n .sort((a, b) => a[0] - b[0])\n .forEach(item => {\n this._offsets.push(item[0])\n this._targets.push(item[1])\n })\n }\n\n dispose() {\n EventHandler.off(this._scrollElement, EVENT_KEY)\n super.dispose()\n }\n\n // Private\n\n _getConfig(config) {\n config = {\n ...Default,\n ...Manipulator.getDataAttributes(this._element),\n ...(typeof config === 'object' && config ? config : {})\n }\n\n config.target = getElement(config.target) || document.documentElement\n\n typeCheckConfig(NAME, config, DefaultType)\n\n return config\n }\n\n _getScrollTop() {\n return this._scrollElement === window ?\n this._scrollElement.pageYOffset :\n this._scrollElement.scrollTop\n }\n\n _getScrollHeight() {\n return this._scrollElement.scrollHeight || Math.max(\n document.body.scrollHeight,\n document.documentElement.scrollHeight\n )\n }\n\n _getOffsetHeight() {\n return this._scrollElement === window ?\n window.innerHeight :\n this._scrollElement.getBoundingClientRect().height\n }\n\n _process() {\n const scrollTop = this._getScrollTop() + this._config.offset\n const scrollHeight = this._getScrollHeight()\n const maxScroll = this._config.offset + scrollHeight - this._getOffsetHeight()\n\n if (this._scrollHeight !== scrollHeight) {\n this.refresh()\n }\n\n if (scrollTop >= maxScroll) {\n const target = this._targets[this._targets.length - 1]\n\n if (this._activeTarget !== target) {\n this._activate(target)\n }\n\n return\n }\n\n if (this._activeTarget && scrollTop < this._offsets[0] && this._offsets[0] > 0) {\n this._activeTarget = null\n this._clear()\n return\n }\n\n for (let i = this._offsets.length; i--;) {\n const isActiveTarget = this._activeTarget !== this._targets[i] &&\n scrollTop >= this._offsets[i] &&\n (typeof this._offsets[i + 1] === 'undefined' || scrollTop < this._offsets[i + 1])\n\n if (isActiveTarget) {\n this._activate(this._targets[i])\n }\n }\n }\n\n _activate(target) {\n this._activeTarget = target\n\n this._clear()\n\n const queries = SELECTOR_LINK_ITEMS.split(',')\n .map(selector => `${selector}[data-bs-target=\"${target}\"],${selector}[href=\"${target}\"]`)\n\n const link = SelectorEngine.findOne(queries.join(','), this._config.target)\n\n link.classList.add(CLASS_NAME_ACTIVE)\n if (link.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE, link.closest(SELECTOR_DROPDOWN))\n .classList.add(CLASS_NAME_ACTIVE)\n } else {\n SelectorEngine.parents(link, SELECTOR_NAV_LIST_GROUP)\n .forEach(listGroup => {\n // Set triggered links parents as active\n // With both
    and

or other required elements. + thead: [ 1, "
Prefix
", "
" ], + col: [ 2, "", "
" ], + tr: [ 2, "", "
" ], + td: [ 3, "", "
" ], + + _default: [ 0, "", "" ] +}; + +wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; +wrapMap.th = wrapMap.td; + +// Support: IE <=9 only +if ( !support.option ) { + wrapMap.optgroup = wrapMap.option = [ 1, "" ]; +} + + +function getAll( context, tag ) { + + // Support: IE <=9 - 11 only + // Use typeof to avoid zero-argument method invocation on host objects (#15151) + var ret; + + if ( typeof context.getElementsByTagName !== "undefined" ) { + ret = context.getElementsByTagName( tag || "*" ); + + } else if ( typeof context.querySelectorAll !== "undefined" ) { + ret = context.querySelectorAll( tag || "*" ); + + } else { + ret = []; + } + + if ( tag === undefined || tag && nodeName( context, tag ) ) { + return jQuery.merge( [ context ], ret ); + } + + return ret; +} + + +// Mark scripts as having already been evaluated +function setGlobalEval( elems, refElements ) { + var i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + dataPriv.set( + elems[ i ], + "globalEval", + !refElements || dataPriv.get( refElements[ i ], "globalEval" ) + ); + } +} + + +var rhtml = /<|&#?\w+;/; + +function buildFragment( elems, context, scripts, selection, ignored ) { + var elem, tmp, tag, wrap, attached, j, + fragment = context.createDocumentFragment(), + nodes = [], + i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + elem = elems[ i ]; + + if ( elem || elem === 0 ) { + + // Add nodes directly + if ( toType( elem ) === "object" ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); + + // Convert non-html into a text node + } else if ( !rhtml.test( elem ) ) { + nodes.push( context.createTextNode( elem ) ); + + // Convert html into DOM nodes + } else { + tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); + + // Deserialize a standard representation + tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); + wrap = wrapMap[ tag ] || wrapMap._default; + tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; + + // Descend through wrappers to the right content + j = wrap[ 0 ]; + while ( j-- ) { + tmp = tmp.lastChild; + } + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, tmp.childNodes ); + + // Remember the top-level container + tmp = fragment.firstChild; + + // Ensure the created nodes are orphaned (#12392) + tmp.textContent = ""; + } + } + } + + // Remove wrapper from fragment + fragment.textContent = ""; + + i = 0; + while ( ( elem = nodes[ i++ ] ) ) { + + // Skip elements already in the context collection (trac-4087) + if ( selection && jQuery.inArray( elem, selection ) > -1 ) { + if ( ignored ) { + ignored.push( elem ); + } + continue; + } + + attached = isAttached( elem ); + + // Append to fragment + tmp = getAll( fragment.appendChild( elem ), "script" ); + + // Preserve script evaluation history + if ( attached ) { + setGlobalEval( tmp ); + } + + // Capture executables + if ( scripts ) { + j = 0; + while ( ( elem = tmp[ j++ ] ) ) { + if ( rscriptType.test( elem.type || "" ) ) { + scripts.push( elem ); + } + } + } + } + + return fragment; +} + + +var rtypenamespace = /^([^.]*)(?:\.(.+)|)/; + +function returnTrue() { + return true; +} + +function returnFalse() { + return false; +} + +// Support: IE <=9 - 11+ +// focus() and blur() are asynchronous, except when they are no-op. +// So expect focus to be synchronous when the element is already active, +// and blur to be synchronous when the element is not already active. +// (focus and blur are always synchronous in other supported browsers, +// this just defines when we can count on it). +function expectSync( elem, type ) { + return ( elem === safeActiveElement() ) === ( type === "focus" ); +} + +// Support: IE <=9 only +// Accessing document.activeElement can throw unexpectedly +// https://bugs.jquery.com/ticket/13393 +function safeActiveElement() { + try { + return document.activeElement; + } catch ( err ) { } +} + +function on( elem, types, selector, data, fn, one ) { + var origFn, type; + + // Types can be a map of types/handlers + if ( typeof types === "object" ) { + + // ( types-Object, selector, data ) + if ( typeof selector !== "string" ) { + + // ( types-Object, data ) + data = data || selector; + selector = undefined; + } + for ( type in types ) { + on( elem, type, selector, data, types[ type ], one ); + } + return elem; + } + + if ( data == null && fn == null ) { + + // ( types, fn ) + fn = selector; + data = selector = undefined; + } else if ( fn == null ) { + if ( typeof selector === "string" ) { + + // ( types, selector, fn ) + fn = data; + data = undefined; + } else { + + // ( types, data, fn ) + fn = data; + data = selector; + selector = undefined; + } + } + if ( fn === false ) { + fn = returnFalse; + } else if ( !fn ) { + return elem; + } + + if ( one === 1 ) { + origFn = fn; + fn = function( event ) { + + // Can use an empty set, since event contains the info + jQuery().off( event ); + return origFn.apply( this, arguments ); + }; + + // Use same guid so caller can remove using origFn + fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); + } + return elem.each( function() { + jQuery.event.add( this, types, fn, data, selector ); + } ); +} + +/* + * Helper functions for managing events -- not part of the public interface. + * Props to Dean Edwards' addEvent library for many of the ideas. + */ +jQuery.event = { + + global: {}, + + add: function( elem, types, handler, data, selector ) { + + var handleObjIn, eventHandle, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.get( elem ); + + // Only attach events to objects that accept data + if ( !acceptData( elem ) ) { + return; + } + + // Caller can pass in an object of custom data in lieu of the handler + if ( handler.handler ) { + handleObjIn = handler; + handler = handleObjIn.handler; + selector = handleObjIn.selector; + } + + // Ensure that invalid selectors throw exceptions at attach time + // Evaluate against documentElement in case elem is a non-element node (e.g., document) + if ( selector ) { + jQuery.find.matchesSelector( documentElement, selector ); + } + + // Make sure that the handler has a unique ID, used to find/remove it later + if ( !handler.guid ) { + handler.guid = jQuery.guid++; + } + + // Init the element's event structure and main handler, if this is the first + if ( !( events = elemData.events ) ) { + events = elemData.events = Object.create( null ); + } + if ( !( eventHandle = elemData.handle ) ) { + eventHandle = elemData.handle = function( e ) { + + // Discard the second event of a jQuery.event.trigger() and + // when an event is called after a page has unloaded + return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? + jQuery.event.dispatch.apply( elem, arguments ) : undefined; + }; + } + + // Handle multiple events separated by a space + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // There *must* be a type, no attaching namespace-only handlers + if ( !type ) { + continue; + } + + // If event changes its type, use the special event handlers for the changed type + special = jQuery.event.special[ type ] || {}; + + // If selector defined, determine special event api type, otherwise given type + type = ( selector ? special.delegateType : special.bindType ) || type; + + // Update special based on newly reset type + special = jQuery.event.special[ type ] || {}; + + // handleObj is passed to all event handlers + handleObj = jQuery.extend( { + type: type, + origType: origType, + data: data, + handler: handler, + guid: handler.guid, + selector: selector, + needsContext: selector && jQuery.expr.match.needsContext.test( selector ), + namespace: namespaces.join( "." ) + }, handleObjIn ); + + // Init the event handler queue if we're the first + if ( !( handlers = events[ type ] ) ) { + handlers = events[ type ] = []; + handlers.delegateCount = 0; + + // Only use addEventListener if the special events handler returns false + if ( !special.setup || + special.setup.call( elem, data, namespaces, eventHandle ) === false ) { + + if ( elem.addEventListener ) { + elem.addEventListener( type, eventHandle ); + } + } + } + + if ( special.add ) { + special.add.call( elem, handleObj ); + + if ( !handleObj.handler.guid ) { + handleObj.handler.guid = handler.guid; + } + } + + // Add to the element's handler list, delegates in front + if ( selector ) { + handlers.splice( handlers.delegateCount++, 0, handleObj ); + } else { + handlers.push( handleObj ); + } + + // Keep track of which events have ever been used, for event optimization + jQuery.event.global[ type ] = true; + } + + }, + + // Detach an event or set of events from an element + remove: function( elem, types, handler, selector, mappedTypes ) { + + var j, origCount, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); + + if ( !elemData || !( events = elemData.events ) ) { + return; + } + + // Once for each type.namespace in types; type may be omitted + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // Unbind all events (on this namespace, if provided) for the element + if ( !type ) { + for ( type in events ) { + jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); + } + continue; + } + + special = jQuery.event.special[ type ] || {}; + type = ( selector ? special.delegateType : special.bindType ) || type; + handlers = events[ type ] || []; + tmp = tmp[ 2 ] && + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); + + // Remove matching events + origCount = j = handlers.length; + while ( j-- ) { + handleObj = handlers[ j ]; + + if ( ( mappedTypes || origType === handleObj.origType ) && + ( !handler || handler.guid === handleObj.guid ) && + ( !tmp || tmp.test( handleObj.namespace ) ) && + ( !selector || selector === handleObj.selector || + selector === "**" && handleObj.selector ) ) { + handlers.splice( j, 1 ); + + if ( handleObj.selector ) { + handlers.delegateCount--; + } + if ( special.remove ) { + special.remove.call( elem, handleObj ); + } + } + } + + // Remove generic event handler if we removed something and no more handlers exist + // (avoids potential for endless recursion during removal of special event handlers) + if ( origCount && !handlers.length ) { + if ( !special.teardown || + special.teardown.call( elem, namespaces, elemData.handle ) === false ) { + + jQuery.removeEvent( elem, type, elemData.handle ); + } + + delete events[ type ]; + } + } + + // Remove data and the expando if it's no longer used + if ( jQuery.isEmptyObject( events ) ) { + dataPriv.remove( elem, "handle events" ); + } + }, + + dispatch: function( nativeEvent ) { + + var i, j, ret, matched, handleObj, handlerQueue, + args = new Array( arguments.length ), + + // Make a writable jQuery.Event from the native event object + event = jQuery.event.fix( nativeEvent ), + + handlers = ( + dataPriv.get( this, "events" ) || Object.create( null ) + )[ event.type ] || [], + special = jQuery.event.special[ event.type ] || {}; + + // Use the fix-ed jQuery.Event rather than the (read-only) native event + args[ 0 ] = event; + + for ( i = 1; i < arguments.length; i++ ) { + args[ i ] = arguments[ i ]; + } + + event.delegateTarget = this; + + // Call the preDispatch hook for the mapped type, and let it bail if desired + if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { + return; + } + + // Determine handlers + handlerQueue = jQuery.event.handlers.call( this, event, handlers ); + + // Run delegates first; they may want to stop propagation beneath us + i = 0; + while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { + event.currentTarget = matched.elem; + + j = 0; + while ( ( handleObj = matched.handlers[ j++ ] ) && + !event.isImmediatePropagationStopped() ) { + + // If the event is namespaced, then each handler is only invoked if it is + // specially universal or its namespaces are a superset of the event's. + if ( !event.rnamespace || handleObj.namespace === false || + event.rnamespace.test( handleObj.namespace ) ) { + + event.handleObj = handleObj; + event.data = handleObj.data; + + ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || + handleObj.handler ).apply( matched.elem, args ); + + if ( ret !== undefined ) { + if ( ( event.result = ret ) === false ) { + event.preventDefault(); + event.stopPropagation(); + } + } + } + } + } + + // Call the postDispatch hook for the mapped type + if ( special.postDispatch ) { + special.postDispatch.call( this, event ); + } + + return event.result; + }, + + handlers: function( event, handlers ) { + var i, handleObj, sel, matchedHandlers, matchedSelectors, + handlerQueue = [], + delegateCount = handlers.delegateCount, + cur = event.target; + + // Find delegate handlers + if ( delegateCount && + + // Support: IE <=9 + // Black-hole SVG instance trees (trac-13180) + cur.nodeType && + + // Support: Firefox <=42 + // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) + // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click + // Support: IE 11 only + // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) + !( event.type === "click" && event.button >= 1 ) ) { + + for ( ; cur !== this; cur = cur.parentNode || this ) { + + // Don't check non-elements (#13208) + // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) + if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { + matchedHandlers = []; + matchedSelectors = {}; + for ( i = 0; i < delegateCount; i++ ) { + handleObj = handlers[ i ]; + + // Don't conflict with Object.prototype properties (#13203) + sel = handleObj.selector + " "; + + if ( matchedSelectors[ sel ] === undefined ) { + matchedSelectors[ sel ] = handleObj.needsContext ? + jQuery( sel, this ).index( cur ) > -1 : + jQuery.find( sel, this, null, [ cur ] ).length; + } + if ( matchedSelectors[ sel ] ) { + matchedHandlers.push( handleObj ); + } + } + if ( matchedHandlers.length ) { + handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); + } + } + } + } + + // Add the remaining (directly-bound) handlers + cur = this; + if ( delegateCount < handlers.length ) { + handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); + } + + return handlerQueue; + }, + + addProp: function( name, hook ) { + Object.defineProperty( jQuery.Event.prototype, name, { + enumerable: true, + configurable: true, + + get: isFunction( hook ) ? + function() { + if ( this.originalEvent ) { + return hook( this.originalEvent ); + } + } : + function() { + if ( this.originalEvent ) { + return this.originalEvent[ name ]; + } + }, + + set: function( value ) { + Object.defineProperty( this, name, { + enumerable: true, + configurable: true, + writable: true, + value: value + } ); + } + } ); + }, + + fix: function( originalEvent ) { + return originalEvent[ jQuery.expando ] ? + originalEvent : + new jQuery.Event( originalEvent ); + }, + + special: { + load: { + + // Prevent triggered image.load events from bubbling to window.load + noBubble: true + }, + click: { + + // Utilize native event to ensure correct state for checkable inputs + setup: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Claim the first handler + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + // dataPriv.set( el, "click", ... ) + leverageNative( el, "click", returnTrue ); + } + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Force setup before triggering a click + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + leverageNative( el, "click" ); + } + + // Return non-false to allow normal event-path propagation + return true; + }, + + // For cross-browser consistency, suppress native .click() on links + // Also prevent it if we're currently inside a leveraged native-event stack + _default: function( event ) { + var target = event.target; + return rcheckableType.test( target.type ) && + target.click && nodeName( target, "input" ) && + dataPriv.get( target, "click" ) || + nodeName( target, "a" ); + } + }, + + beforeunload: { + postDispatch: function( event ) { + + // Support: Firefox 20+ + // Firefox doesn't alert if the returnValue field is not set. + if ( event.result !== undefined && event.originalEvent ) { + event.originalEvent.returnValue = event.result; + } + } + } + } +}; + +// Ensure the presence of an event listener that handles manually-triggered +// synthetic events by interrupting progress until reinvoked in response to +// *native* events that it fires directly, ensuring that state changes have +// already occurred before other listeners are invoked. +function leverageNative( el, type, expectSync ) { + + // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add + if ( !expectSync ) { + if ( dataPriv.get( el, type ) === undefined ) { + jQuery.event.add( el, type, returnTrue ); + } + return; + } + + // Register the controller as a special universal handler for all event namespaces + dataPriv.set( el, type, false ); + jQuery.event.add( el, type, { + namespace: false, + handler: function( event ) { + var notAsync, result, + saved = dataPriv.get( this, type ); + + if ( ( event.isTrigger & 1 ) && this[ type ] ) { + + // Interrupt processing of the outer synthetic .trigger()ed event + // Saved data should be false in such cases, but might be a leftover capture object + // from an async native handler (gh-4350) + if ( !saved.length ) { + + // Store arguments for use when handling the inner native event + // There will always be at least one argument (an event object), so this array + // will not be confused with a leftover capture object. + saved = slice.call( arguments ); + dataPriv.set( this, type, saved ); + + // Trigger the native event and capture its result + // Support: IE <=9 - 11+ + // focus() and blur() are asynchronous + notAsync = expectSync( this, type ); + this[ type ](); + result = dataPriv.get( this, type ); + if ( saved !== result || notAsync ) { + dataPriv.set( this, type, false ); + } else { + result = {}; + } + if ( saved !== result ) { + + // Cancel the outer synthetic event + event.stopImmediatePropagation(); + event.preventDefault(); + + // Support: Chrome 86+ + // In Chrome, if an element having a focusout handler is blurred by + // clicking outside of it, it invokes the handler synchronously. If + // that handler calls `.remove()` on the element, the data is cleared, + // leaving `result` undefined. We need to guard against this. + return result && result.value; + } + + // If this is an inner synthetic event for an event with a bubbling surrogate + // (focus or blur), assume that the surrogate already propagated from triggering the + // native event and prevent that from happening again here. + // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the + // bubbling surrogate propagates *after* the non-bubbling base), but that seems + // less bad than duplication. + } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { + event.stopPropagation(); + } + + // If this is a native event triggered above, everything is now in order + // Fire an inner synthetic event with the original arguments + } else if ( saved.length ) { + + // ...and capture the result + dataPriv.set( this, type, { + value: jQuery.event.trigger( + + // Support: IE <=9 - 11+ + // Extend with the prototype to reset the above stopImmediatePropagation() + jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), + saved.slice( 1 ), + this + ) + } ); + + // Abort handling of the native event + event.stopImmediatePropagation(); + } + } + } ); +} + +jQuery.removeEvent = function( elem, type, handle ) { + + // This "if" is needed for plain objects + if ( elem.removeEventListener ) { + elem.removeEventListener( type, handle ); + } +}; + +jQuery.Event = function( src, props ) { + + // Allow instantiation without the 'new' keyword + if ( !( this instanceof jQuery.Event ) ) { + return new jQuery.Event( src, props ); + } + + // Event object + if ( src && src.type ) { + this.originalEvent = src; + this.type = src.type; + + // Events bubbling up the document may have been marked as prevented + // by a handler lower down the tree; reflect the correct value. + this.isDefaultPrevented = src.defaultPrevented || + src.defaultPrevented === undefined && + + // Support: Android <=2.3 only + src.returnValue === false ? + returnTrue : + returnFalse; + + // Create target properties + // Support: Safari <=6 - 7 only + // Target should not be a text node (#504, #13143) + this.target = ( src.target && src.target.nodeType === 3 ) ? + src.target.parentNode : + src.target; + + this.currentTarget = src.currentTarget; + this.relatedTarget = src.relatedTarget; + + // Event type + } else { + this.type = src; + } + + // Put explicitly provided properties onto the event object + if ( props ) { + jQuery.extend( this, props ); + } + + // Create a timestamp if incoming event doesn't have one + this.timeStamp = src && src.timeStamp || Date.now(); + + // Mark it as fixed + this[ jQuery.expando ] = true; +}; + +// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding +// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html +jQuery.Event.prototype = { + constructor: jQuery.Event, + isDefaultPrevented: returnFalse, + isPropagationStopped: returnFalse, + isImmediatePropagationStopped: returnFalse, + isSimulated: false, + + preventDefault: function() { + var e = this.originalEvent; + + this.isDefaultPrevented = returnTrue; + + if ( e && !this.isSimulated ) { + e.preventDefault(); + } + }, + stopPropagation: function() { + var e = this.originalEvent; + + this.isPropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopPropagation(); + } + }, + stopImmediatePropagation: function() { + var e = this.originalEvent; + + this.isImmediatePropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopImmediatePropagation(); + } + + this.stopPropagation(); + } +}; + +// Includes all common event props including KeyEvent and MouseEvent specific props +jQuery.each( { + altKey: true, + bubbles: true, + cancelable: true, + changedTouches: true, + ctrlKey: true, + detail: true, + eventPhase: true, + metaKey: true, + pageX: true, + pageY: true, + shiftKey: true, + view: true, + "char": true, + code: true, + charCode: true, + key: true, + keyCode: true, + button: true, + buttons: true, + clientX: true, + clientY: true, + offsetX: true, + offsetY: true, + pointerId: true, + pointerType: true, + screenX: true, + screenY: true, + targetTouches: true, + toElement: true, + touches: true, + which: true +}, jQuery.event.addProp ); + +jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { + jQuery.event.special[ type ] = { + + // Utilize native event if possible so blur/focus sequence is correct + setup: function() { + + // Claim the first handler + // dataPriv.set( this, "focus", ... ) + // dataPriv.set( this, "blur", ... ) + leverageNative( this, type, expectSync ); + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function() { + + // Force setup before trigger + leverageNative( this, type ); + + // Return non-false to allow normal event-path propagation + return true; + }, + + // Suppress native focus or blur as it's already being fired + // in leverageNative. + _default: function() { + return true; + }, + + delegateType: delegateType + }; +} ); + +// Create mouseenter/leave events using mouseover/out and event-time checks +// so that event delegation works in jQuery. +// Do the same for pointerenter/pointerleave and pointerover/pointerout +// +// Support: Safari 7 only +// Safari sends mouseenter too often; see: +// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 +// for the description of the bug (it existed in older Chrome versions as well). +jQuery.each( { + mouseenter: "mouseover", + mouseleave: "mouseout", + pointerenter: "pointerover", + pointerleave: "pointerout" +}, function( orig, fix ) { + jQuery.event.special[ orig ] = { + delegateType: fix, + bindType: fix, + + handle: function( event ) { + var ret, + target = this, + related = event.relatedTarget, + handleObj = event.handleObj; + + // For mouseenter/leave call the handler if related is outside the target. + // NB: No relatedTarget if the mouse left/entered the browser window + if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { + event.type = handleObj.origType; + ret = handleObj.handler.apply( this, arguments ); + event.type = fix; + } + return ret; + } + }; +} ); + +jQuery.fn.extend( { + + on: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn ); + }, + one: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn, 1 ); + }, + off: function( types, selector, fn ) { + var handleObj, type; + if ( types && types.preventDefault && types.handleObj ) { + + // ( event ) dispatched jQuery.Event + handleObj = types.handleObj; + jQuery( types.delegateTarget ).off( + handleObj.namespace ? + handleObj.origType + "." + handleObj.namespace : + handleObj.origType, + handleObj.selector, + handleObj.handler + ); + return this; + } + if ( typeof types === "object" ) { + + // ( types-object [, selector] ) + for ( type in types ) { + this.off( type, selector, types[ type ] ); + } + return this; + } + if ( selector === false || typeof selector === "function" ) { + + // ( types [, fn] ) + fn = selector; + selector = undefined; + } + if ( fn === false ) { + fn = returnFalse; + } + return this.each( function() { + jQuery.event.remove( this, types, fn, selector ); + } ); + } +} ); + + +var + + // Support: IE <=10 - 11, Edge 12 - 13 only + // In IE/Edge using regex groups here causes severe slowdowns. + // See https://connect.microsoft.com/IE/feedback/details/1736512/ + rnoInnerhtml = /\s*$/g; + +// Prefer a tbody over its parent table for containing new rows +function manipulationTarget( elem, content ) { + if ( nodeName( elem, "table" ) && + nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { + + return jQuery( elem ).children( "tbody" )[ 0 ] || elem; + } + + return elem; +} + +// Replace/restore the type attribute of script elements for safe DOM manipulation +function disableScript( elem ) { + elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; + return elem; +} +function restoreScript( elem ) { + if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { + elem.type = elem.type.slice( 5 ); + } else { + elem.removeAttribute( "type" ); + } + + return elem; +} + +function cloneCopyEvent( src, dest ) { + var i, l, type, pdataOld, udataOld, udataCur, events; + + if ( dest.nodeType !== 1 ) { + return; + } + + // 1. Copy private data: events, handlers, etc. + if ( dataPriv.hasData( src ) ) { + pdataOld = dataPriv.get( src ); + events = pdataOld.events; + + if ( events ) { + dataPriv.remove( dest, "handle events" ); + + for ( type in events ) { + for ( i = 0, l = events[ type ].length; i < l; i++ ) { + jQuery.event.add( dest, type, events[ type ][ i ] ); + } + } + } + } + + // 2. Copy user data + if ( dataUser.hasData( src ) ) { + udataOld = dataUser.access( src ); + udataCur = jQuery.extend( {}, udataOld ); + + dataUser.set( dest, udataCur ); + } +} + +// Fix IE bugs, see support tests +function fixInput( src, dest ) { + var nodeName = dest.nodeName.toLowerCase(); + + // Fails to persist the checked state of a cloned checkbox or radio button. + if ( nodeName === "input" && rcheckableType.test( src.type ) ) { + dest.checked = src.checked; + + // Fails to return the selected option to the default selected state when cloning options + } else if ( nodeName === "input" || nodeName === "textarea" ) { + dest.defaultValue = src.defaultValue; + } +} + +function domManip( collection, args, callback, ignored ) { + + // Flatten any nested arrays + args = flat( args ); + + var fragment, first, scripts, hasScripts, node, doc, + i = 0, + l = collection.length, + iNoClone = l - 1, + value = args[ 0 ], + valueIsFunction = isFunction( value ); + + // We can't cloneNode fragments that contain checked, in WebKit + if ( valueIsFunction || + ( l > 1 && typeof value === "string" && + !support.checkClone && rchecked.test( value ) ) ) { + return collection.each( function( index ) { + var self = collection.eq( index ); + if ( valueIsFunction ) { + args[ 0 ] = value.call( this, index, self.html() ); + } + domManip( self, args, callback, ignored ); + } ); + } + + if ( l ) { + fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); + first = fragment.firstChild; + + if ( fragment.childNodes.length === 1 ) { + fragment = first; + } + + // Require either new content or an interest in ignored elements to invoke the callback + if ( first || ignored ) { + scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); + hasScripts = scripts.length; + + // Use the original fragment for the last item + // instead of the first because it can end up + // being emptied incorrectly in certain situations (#8070). + for ( ; i < l; i++ ) { + node = fragment; + + if ( i !== iNoClone ) { + node = jQuery.clone( node, true, true ); + + // Keep references to cloned scripts for later restoration + if ( hasScripts ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( scripts, getAll( node, "script" ) ); + } + } + + callback.call( collection[ i ], node, i ); + } + + if ( hasScripts ) { + doc = scripts[ scripts.length - 1 ].ownerDocument; + + // Reenable scripts + jQuery.map( scripts, restoreScript ); + + // Evaluate executable scripts on first document insertion + for ( i = 0; i < hasScripts; i++ ) { + node = scripts[ i ]; + if ( rscriptType.test( node.type || "" ) && + !dataPriv.access( node, "globalEval" ) && + jQuery.contains( doc, node ) ) { + + if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { + + // Optional AJAX dependency, but won't run scripts if not present + if ( jQuery._evalUrl && !node.noModule ) { + jQuery._evalUrl( node.src, { + nonce: node.nonce || node.getAttribute( "nonce" ) + }, doc ); + } + } else { + DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); + } + } + } + } + } + } + + return collection; +} + +function remove( elem, selector, keepData ) { + var node, + nodes = selector ? jQuery.filter( selector, elem ) : elem, + i = 0; + + for ( ; ( node = nodes[ i ] ) != null; i++ ) { + if ( !keepData && node.nodeType === 1 ) { + jQuery.cleanData( getAll( node ) ); + } + + if ( node.parentNode ) { + if ( keepData && isAttached( node ) ) { + setGlobalEval( getAll( node, "script" ) ); + } + node.parentNode.removeChild( node ); + } + } + + return elem; +} + +jQuery.extend( { + htmlPrefilter: function( html ) { + return html; + }, + + clone: function( elem, dataAndEvents, deepDataAndEvents ) { + var i, l, srcElements, destElements, + clone = elem.cloneNode( true ), + inPage = isAttached( elem ); + + // Fix IE cloning issues + if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && + !jQuery.isXMLDoc( elem ) ) { + + // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 + destElements = getAll( clone ); + srcElements = getAll( elem ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + fixInput( srcElements[ i ], destElements[ i ] ); + } + } + + // Copy the events from the original to the clone + if ( dataAndEvents ) { + if ( deepDataAndEvents ) { + srcElements = srcElements || getAll( elem ); + destElements = destElements || getAll( clone ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + cloneCopyEvent( srcElements[ i ], destElements[ i ] ); + } + } else { + cloneCopyEvent( elem, clone ); + } + } + + // Preserve script evaluation history + destElements = getAll( clone, "script" ); + if ( destElements.length > 0 ) { + setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); + } + + // Return the cloned set + return clone; + }, + + cleanData: function( elems ) { + var data, elem, type, + special = jQuery.event.special, + i = 0; + + for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { + if ( acceptData( elem ) ) { + if ( ( data = elem[ dataPriv.expando ] ) ) { + if ( data.events ) { + for ( type in data.events ) { + if ( special[ type ] ) { + jQuery.event.remove( elem, type ); + + // This is a shortcut to avoid jQuery.event.remove's overhead + } else { + jQuery.removeEvent( elem, type, data.handle ); + } + } + } + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataPriv.expando ] = undefined; + } + if ( elem[ dataUser.expando ] ) { + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataUser.expando ] = undefined; + } + } + } + } +} ); + +jQuery.fn.extend( { + detach: function( selector ) { + return remove( this, selector, true ); + }, + + remove: function( selector ) { + return remove( this, selector ); + }, + + text: function( value ) { + return access( this, function( value ) { + return value === undefined ? + jQuery.text( this ) : + this.empty().each( function() { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + this.textContent = value; + } + } ); + }, null, value, arguments.length ); + }, + + append: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.appendChild( elem ); + } + } ); + }, + + prepend: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.insertBefore( elem, target.firstChild ); + } + } ); + }, + + before: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this ); + } + } ); + }, + + after: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this.nextSibling ); + } + } ); + }, + + empty: function() { + var elem, + i = 0; + + for ( ; ( elem = this[ i ] ) != null; i++ ) { + if ( elem.nodeType === 1 ) { + + // Prevent memory leaks + jQuery.cleanData( getAll( elem, false ) ); + + // Remove any remaining nodes + elem.textContent = ""; + } + } + + return this; + }, + + clone: function( dataAndEvents, deepDataAndEvents ) { + dataAndEvents = dataAndEvents == null ? false : dataAndEvents; + deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; + + return this.map( function() { + return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); + } ); + }, + + html: function( value ) { + return access( this, function( value ) { + var elem = this[ 0 ] || {}, + i = 0, + l = this.length; + + if ( value === undefined && elem.nodeType === 1 ) { + return elem.innerHTML; + } + + // See if we can take a shortcut and just use innerHTML + if ( typeof value === "string" && !rnoInnerhtml.test( value ) && + !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { + + value = jQuery.htmlPrefilter( value ); + + try { + for ( ; i < l; i++ ) { + elem = this[ i ] || {}; + + // Remove element nodes and prevent memory leaks + if ( elem.nodeType === 1 ) { + jQuery.cleanData( getAll( elem, false ) ); + elem.innerHTML = value; + } + } + + elem = 0; + + // If using innerHTML throws an exception, use the fallback method + } catch ( e ) {} + } + + if ( elem ) { + this.empty().append( value ); + } + }, null, value, arguments.length ); + }, + + replaceWith: function() { + var ignored = []; + + // Make the changes, replacing each non-ignored context element with the new content + return domManip( this, arguments, function( elem ) { + var parent = this.parentNode; + + if ( jQuery.inArray( this, ignored ) < 0 ) { + jQuery.cleanData( getAll( this ) ); + if ( parent ) { + parent.replaceChild( elem, this ); + } + } + + // Force callback invocation + }, ignored ); + } +} ); + +jQuery.each( { + appendTo: "append", + prependTo: "prepend", + insertBefore: "before", + insertAfter: "after", + replaceAll: "replaceWith" +}, function( name, original ) { + jQuery.fn[ name ] = function( selector ) { + var elems, + ret = [], + insert = jQuery( selector ), + last = insert.length - 1, + i = 0; + + for ( ; i <= last; i++ ) { + elems = i === last ? this : this.clone( true ); + jQuery( insert[ i ] )[ original ]( elems ); + + // Support: Android <=4.0 only, PhantomJS 1 only + // .get() because push.apply(_, arraylike) throws on ancient WebKit + push.apply( ret, elems.get() ); + } + + return this.pushStack( ret ); + }; +} ); +var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); + +var getStyles = function( elem ) { + + // Support: IE <=11 only, Firefox <=30 (#15098, #14150) + // IE throws on elements created in popups + // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" + var view = elem.ownerDocument.defaultView; + + if ( !view || !view.opener ) { + view = window; + } + + return view.getComputedStyle( elem ); + }; + +var swap = function( elem, options, callback ) { + var ret, name, + old = {}; + + // Remember the old values, and insert the new ones + for ( name in options ) { + old[ name ] = elem.style[ name ]; + elem.style[ name ] = options[ name ]; + } + + ret = callback.call( elem ); + + // Revert the old values + for ( name in options ) { + elem.style[ name ] = old[ name ]; + } + + return ret; +}; + + +var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); + + + +( function() { + + // Executing both pixelPosition & boxSizingReliable tests require only one layout + // so they're executed at the same time to save the second computation. + function computeStyleTests() { + + // This is a singleton, we need to execute it only once + if ( !div ) { + return; + } + + container.style.cssText = "position:absolute;left:-11111px;width:60px;" + + "margin-top:1px;padding:0;border:0"; + div.style.cssText = + "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + + "margin:auto;border:1px;padding:1px;" + + "width:60%;top:1%"; + documentElement.appendChild( container ).appendChild( div ); + + var divStyle = window.getComputedStyle( div ); + pixelPositionVal = divStyle.top !== "1%"; + + // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 + reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; + + // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 + // Some styles come back with percentage values, even though they shouldn't + div.style.right = "60%"; + pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; + + // Support: IE 9 - 11 only + // Detect misreporting of content dimensions for box-sizing:border-box elements + boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; + + // Support: IE 9 only + // Detect overflow:scroll screwiness (gh-3699) + // Support: Chrome <=64 + // Don't get tricked when zoom affects offsetWidth (gh-4029) + div.style.position = "absolute"; + scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; + + documentElement.removeChild( container ); + + // Nullify the div so it wouldn't be stored in the memory and + // it will also be a sign that checks already performed + div = null; + } + + function roundPixelMeasures( measure ) { + return Math.round( parseFloat( measure ) ); + } + + var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, + reliableTrDimensionsVal, reliableMarginLeftVal, + container = document.createElement( "div" ), + div = document.createElement( "div" ); + + // Finish early in limited (non-browser) environments + if ( !div.style ) { + return; + } + + // Support: IE <=9 - 11 only + // Style of cloned element affects source element cloned (#8908) + div.style.backgroundClip = "content-box"; + div.cloneNode( true ).style.backgroundClip = ""; + support.clearCloneStyle = div.style.backgroundClip === "content-box"; + + jQuery.extend( support, { + boxSizingReliable: function() { + computeStyleTests(); + return boxSizingReliableVal; + }, + pixelBoxStyles: function() { + computeStyleTests(); + return pixelBoxStylesVal; + }, + pixelPosition: function() { + computeStyleTests(); + return pixelPositionVal; + }, + reliableMarginLeft: function() { + computeStyleTests(); + return reliableMarginLeftVal; + }, + scrollboxSize: function() { + computeStyleTests(); + return scrollboxSizeVal; + }, + + // Support: IE 9 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Behavior in IE 9 is more subtle than in newer versions & it passes + // some versions of this test; make sure not to make it pass there! + // + // Support: Firefox 70+ + // Only Firefox includes border widths + // in computed dimensions. (gh-4529) + reliableTrDimensions: function() { + var table, tr, trChild, trStyle; + if ( reliableTrDimensionsVal == null ) { + table = document.createElement( "table" ); + tr = document.createElement( "tr" ); + trChild = document.createElement( "div" ); + + table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate"; + tr.style.cssText = "border:1px solid"; + + // Support: Chrome 86+ + // Height set through cssText does not get applied. + // Computed height then comes back as 0. + tr.style.height = "1px"; + trChild.style.height = "9px"; + + // Support: Android 8 Chrome 86+ + // In our bodyBackground.html iframe, + // display for all div elements is set to "inline", + // which causes a problem only in Android 8 Chrome 86. + // Ensuring the div is display: block + // gets around this issue. + trChild.style.display = "block"; + + documentElement + .appendChild( table ) + .appendChild( tr ) + .appendChild( trChild ); + + trStyle = window.getComputedStyle( tr ); + reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) + + parseInt( trStyle.borderTopWidth, 10 ) + + parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight; + + documentElement.removeChild( table ); + } + return reliableTrDimensionsVal; + } + } ); +} )(); + + +function curCSS( elem, name, computed ) { + var width, minWidth, maxWidth, ret, + + // Support: Firefox 51+ + // Retrieving style before computed somehow + // fixes an issue with getting wrong values + // on detached elements + style = elem.style; + + computed = computed || getStyles( elem ); + + // getPropertyValue is needed for: + // .css('filter') (IE 9 only, #12537) + // .css('--customProperty) (#3144) + if ( computed ) { + ret = computed.getPropertyValue( name ) || computed[ name ]; + + if ( ret === "" && !isAttached( elem ) ) { + ret = jQuery.style( elem, name ); + } + + // A tribute to the "awesome hack by Dean Edwards" + // Android Browser returns percentage for some values, + // but width seems to be reliably pixels. + // This is against the CSSOM draft spec: + // https://drafts.csswg.org/cssom/#resolved-values + if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { + + // Remember the original values + width = style.width; + minWidth = style.minWidth; + maxWidth = style.maxWidth; + + // Put in the new values to get a computed value out + style.minWidth = style.maxWidth = style.width = ret; + ret = computed.width; + + // Revert the changed values + style.width = width; + style.minWidth = minWidth; + style.maxWidth = maxWidth; + } + } + + return ret !== undefined ? + + // Support: IE <=9 - 11 only + // IE returns zIndex value as an integer. + ret + "" : + ret; +} + + +function addGetHookIf( conditionFn, hookFn ) { + + // Define the hook, we'll check on the first run if it's really needed. + return { + get: function() { + if ( conditionFn() ) { + + // Hook not needed (or it's not possible to use it due + // to missing dependency), remove it. + delete this.get; + return; + } + + // Hook needed; redefine it so that the support test is not executed again. + return ( this.get = hookFn ).apply( this, arguments ); + } + }; +} + + +var cssPrefixes = [ "Webkit", "Moz", "ms" ], + emptyStyle = document.createElement( "div" ).style, + vendorProps = {}; + +// Return a vendor-prefixed property or undefined +function vendorPropName( name ) { + + // Check for vendor prefixed names + var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), + i = cssPrefixes.length; + + while ( i-- ) { + name = cssPrefixes[ i ] + capName; + if ( name in emptyStyle ) { + return name; + } + } +} + +// Return a potentially-mapped jQuery.cssProps or vendor prefixed property +function finalPropName( name ) { + var final = jQuery.cssProps[ name ] || vendorProps[ name ]; + + if ( final ) { + return final; + } + if ( name in emptyStyle ) { + return name; + } + return vendorProps[ name ] = vendorPropName( name ) || name; +} + + +var + + // Swappable if display is none or starts with table + // except "table", "table-cell", or "table-caption" + // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display + rdisplayswap = /^(none|table(?!-c[ea]).+)/, + rcustomProp = /^--/, + cssShow = { position: "absolute", visibility: "hidden", display: "block" }, + cssNormalTransform = { + letterSpacing: "0", + fontWeight: "400" + }; + +function setPositiveNumber( _elem, value, subtract ) { + + // Any relative (+/-) values have already been + // normalized at this point + var matches = rcssNum.exec( value ); + return matches ? + + // Guard against undefined "subtract", e.g., when used as in cssHooks + Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : + value; +} + +function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { + var i = dimension === "width" ? 1 : 0, + extra = 0, + delta = 0; + + // Adjustment may not be necessary + if ( box === ( isBorderBox ? "border" : "content" ) ) { + return 0; + } + + for ( ; i < 4; i += 2 ) { + + // Both box models exclude margin + if ( box === "margin" ) { + delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); + } + + // If we get here with a content-box, we're seeking "padding" or "border" or "margin" + if ( !isBorderBox ) { + + // Add padding + delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + + // For "border" or "margin", add border + if ( box !== "padding" ) { + delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + + // But still keep track of it otherwise + } else { + extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + + // If we get here with a border-box (content + padding + border), we're seeking "content" or + // "padding" or "margin" + } else { + + // For "content", subtract padding + if ( box === "content" ) { + delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + } + + // For "content" or "padding", subtract border + if ( box !== "margin" ) { + delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + } + } + + // Account for positive content-box scroll gutter when requested by providing computedVal + if ( !isBorderBox && computedVal >= 0 ) { + + // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border + // Assuming integer scroll gutter, subtract the rest and round down + delta += Math.max( 0, Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + computedVal - + delta - + extra - + 0.5 + + // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter + // Use an explicit zero to avoid NaN (gh-3964) + ) ) || 0; + } + + return delta; +} + +function getWidthOrHeight( elem, dimension, extra ) { + + // Start with computed style + var styles = getStyles( elem ), + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). + // Fake content-box until we know it's needed to know the true value. + boxSizingNeeded = !support.boxSizingReliable() || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + valueIsBorderBox = isBorderBox, + + val = curCSS( elem, dimension, styles ), + offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); + + // Support: Firefox <=54 + // Return a confounding non-pixel value or feign ignorance, as appropriate. + if ( rnumnonpx.test( val ) ) { + if ( !extra ) { + return val; + } + val = "auto"; + } + + + // Support: IE 9 - 11 only + // Use offsetWidth/offsetHeight for when box sizing is unreliable. + // In those cases, the computed value can be trusted to be border-box. + if ( ( !support.boxSizingReliable() && isBorderBox || + + // Support: IE 10 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Interestingly, in some cases IE 9 doesn't suffer from this issue. + !support.reliableTrDimensions() && nodeName( elem, "tr" ) || + + // Fall back to offsetWidth/offsetHeight when value is "auto" + // This happens for inline elements with no explicit setting (gh-3571) + val === "auto" || + + // Support: Android <=4.1 - 4.3 only + // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) + !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && + + // Make sure the element is visible & connected + elem.getClientRects().length ) { + + isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; + + // Where available, offsetWidth/offsetHeight approximate border box dimensions. + // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the + // retrieved value as a content box dimension. + valueIsBorderBox = offsetProp in elem; + if ( valueIsBorderBox ) { + val = elem[ offsetProp ]; + } + } + + // Normalize "" and auto + val = parseFloat( val ) || 0; + + // Adjust for the element's box model + return ( val + + boxModelAdjustment( + elem, + dimension, + extra || ( isBorderBox ? "border" : "content" ), + valueIsBorderBox, + styles, + + // Provide the current computed size to request scroll gutter calculation (gh-3589) + val + ) + ) + "px"; +} + +jQuery.extend( { + + // Add in style property hooks for overriding the default + // behavior of getting and setting a style property + cssHooks: { + opacity: { + get: function( elem, computed ) { + if ( computed ) { + + // We should always get a number back from opacity + var ret = curCSS( elem, "opacity" ); + return ret === "" ? "1" : ret; + } + } + } + }, + + // Don't automatically add "px" to these possibly-unitless properties + cssNumber: { + "animationIterationCount": true, + "columnCount": true, + "fillOpacity": true, + "flexGrow": true, + "flexShrink": true, + "fontWeight": true, + "gridArea": true, + "gridColumn": true, + "gridColumnEnd": true, + "gridColumnStart": true, + "gridRow": true, + "gridRowEnd": true, + "gridRowStart": true, + "lineHeight": true, + "opacity": true, + "order": true, + "orphans": true, + "widows": true, + "zIndex": true, + "zoom": true + }, + + // Add in properties whose names you wish to fix before + // setting or getting the value + cssProps: {}, + + // Get and set the style property on a DOM Node + style: function( elem, name, value, extra ) { + + // Don't set styles on text and comment nodes + if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { + return; + } + + // Make sure that we're working with the right name + var ret, type, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ), + style = elem.style; + + // Make sure that we're working with the right name. We don't + // want to query the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Gets hook for the prefixed version, then unprefixed version + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // Check if we're setting a value + if ( value !== undefined ) { + type = typeof value; + + // Convert "+=" or "-=" to relative numbers (#7345) + if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { + value = adjustCSS( elem, name, ret ); + + // Fixes bug #9237 + type = "number"; + } + + // Make sure that null and NaN values aren't set (#7116) + if ( value == null || value !== value ) { + return; + } + + // If a number was passed in, add the unit (except for certain CSS properties) + // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append + // "px" to a few hardcoded values. + if ( type === "number" && !isCustomProp ) { + value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); + } + + // background-* props affect original clone's values + if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { + style[ name ] = "inherit"; + } + + // If a hook was provided, use that value, otherwise just set the specified value + if ( !hooks || !( "set" in hooks ) || + ( value = hooks.set( elem, value, extra ) ) !== undefined ) { + + if ( isCustomProp ) { + style.setProperty( name, value ); + } else { + style[ name ] = value; + } + } + + } else { + + // If a hook was provided get the non-computed value from there + if ( hooks && "get" in hooks && + ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { + + return ret; + } + + // Otherwise just get the value from the style object + return style[ name ]; + } + }, + + css: function( elem, name, extra, styles ) { + var val, num, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ); + + // Make sure that we're working with the right name. We don't + // want to modify the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Try prefixed name followed by the unprefixed name + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // If a hook was provided get the computed value from there + if ( hooks && "get" in hooks ) { + val = hooks.get( elem, true, extra ); + } + + // Otherwise, if a way to get the computed value exists, use that + if ( val === undefined ) { + val = curCSS( elem, name, styles ); + } + + // Convert "normal" to computed value + if ( val === "normal" && name in cssNormalTransform ) { + val = cssNormalTransform[ name ]; + } + + // Make numeric if forced or a qualifier was provided and val looks numeric + if ( extra === "" || extra ) { + num = parseFloat( val ); + return extra === true || isFinite( num ) ? num || 0 : val; + } + + return val; + } +} ); + +jQuery.each( [ "height", "width" ], function( _i, dimension ) { + jQuery.cssHooks[ dimension ] = { + get: function( elem, computed, extra ) { + if ( computed ) { + + // Certain elements can have dimension info if we invisibly show them + // but it must have a current display style that would benefit + return rdisplayswap.test( jQuery.css( elem, "display" ) ) && + + // Support: Safari 8+ + // Table columns in Safari have non-zero offsetWidth & zero + // getBoundingClientRect().width unless display is changed. + // Support: IE <=11 only + // Running getBoundingClientRect on a disconnected node + // in IE throws an error. + ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? + swap( elem, cssShow, function() { + return getWidthOrHeight( elem, dimension, extra ); + } ) : + getWidthOrHeight( elem, dimension, extra ); + } + }, + + set: function( elem, value, extra ) { + var matches, + styles = getStyles( elem ), + + // Only read styles.position if the test has a chance to fail + // to avoid forcing a reflow. + scrollboxSizeBuggy = !support.scrollboxSize() && + styles.position === "absolute", + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) + boxSizingNeeded = scrollboxSizeBuggy || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + subtract = extra ? + boxModelAdjustment( + elem, + dimension, + extra, + isBorderBox, + styles + ) : + 0; + + // Account for unreliable border-box dimensions by comparing offset* to computed and + // faking a content-box to get border and padding (gh-3699) + if ( isBorderBox && scrollboxSizeBuggy ) { + subtract -= Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + parseFloat( styles[ dimension ] ) - + boxModelAdjustment( elem, dimension, "border", false, styles ) - + 0.5 + ); + } + + // Convert to pixels if value adjustment is needed + if ( subtract && ( matches = rcssNum.exec( value ) ) && + ( matches[ 3 ] || "px" ) !== "px" ) { + + elem.style[ dimension ] = value; + value = jQuery.css( elem, dimension ); + } + + return setPositiveNumber( elem, value, subtract ); + } + }; +} ); + +jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, + function( elem, computed ) { + if ( computed ) { + return ( parseFloat( curCSS( elem, "marginLeft" ) ) || + elem.getBoundingClientRect().left - + swap( elem, { marginLeft: 0 }, function() { + return elem.getBoundingClientRect().left; + } ) + ) + "px"; + } + } +); + +// These hooks are used by animate to expand properties +jQuery.each( { + margin: "", + padding: "", + border: "Width" +}, function( prefix, suffix ) { + jQuery.cssHooks[ prefix + suffix ] = { + expand: function( value ) { + var i = 0, + expanded = {}, + + // Assumes a single number if not a string + parts = typeof value === "string" ? value.split( " " ) : [ value ]; + + for ( ; i < 4; i++ ) { + expanded[ prefix + cssExpand[ i ] + suffix ] = + parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; + } + + return expanded; + } + }; + + if ( prefix !== "margin" ) { + jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; + } +} ); + +jQuery.fn.extend( { + css: function( name, value ) { + return access( this, function( elem, name, value ) { + var styles, len, + map = {}, + i = 0; + + if ( Array.isArray( name ) ) { + styles = getStyles( elem ); + len = name.length; + + for ( ; i < len; i++ ) { + map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); + } + + return map; + } + + return value !== undefined ? + jQuery.style( elem, name, value ) : + jQuery.css( elem, name ); + }, name, value, arguments.length > 1 ); + } +} ); + + +function Tween( elem, options, prop, end, easing ) { + return new Tween.prototype.init( elem, options, prop, end, easing ); +} +jQuery.Tween = Tween; + +Tween.prototype = { + constructor: Tween, + init: function( elem, options, prop, end, easing, unit ) { + this.elem = elem; + this.prop = prop; + this.easing = easing || jQuery.easing._default; + this.options = options; + this.start = this.now = this.cur(); + this.end = end; + this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); + }, + cur: function() { + var hooks = Tween.propHooks[ this.prop ]; + + return hooks && hooks.get ? + hooks.get( this ) : + Tween.propHooks._default.get( this ); + }, + run: function( percent ) { + var eased, + hooks = Tween.propHooks[ this.prop ]; + + if ( this.options.duration ) { + this.pos = eased = jQuery.easing[ this.easing ]( + percent, this.options.duration * percent, 0, 1, this.options.duration + ); + } else { + this.pos = eased = percent; + } + this.now = ( this.end - this.start ) * eased + this.start; + + if ( this.options.step ) { + this.options.step.call( this.elem, this.now, this ); + } + + if ( hooks && hooks.set ) { + hooks.set( this ); + } else { + Tween.propHooks._default.set( this ); + } + return this; + } +}; + +Tween.prototype.init.prototype = Tween.prototype; + +Tween.propHooks = { + _default: { + get: function( tween ) { + var result; + + // Use a property on the element directly when it is not a DOM element, + // or when there is no matching style property that exists. + if ( tween.elem.nodeType !== 1 || + tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { + return tween.elem[ tween.prop ]; + } + + // Passing an empty string as a 3rd parameter to .css will automatically + // attempt a parseFloat and fallback to a string if the parse fails. + // Simple values such as "10px" are parsed to Float; + // complex values such as "rotate(1rad)" are returned as-is. + result = jQuery.css( tween.elem, tween.prop, "" ); + + // Empty strings, null, undefined and "auto" are converted to 0. + return !result || result === "auto" ? 0 : result; + }, + set: function( tween ) { + + // Use step hook for back compat. + // Use cssHook if its there. + // Use .style if available and use plain properties where available. + if ( jQuery.fx.step[ tween.prop ] ) { + jQuery.fx.step[ tween.prop ]( tween ); + } else if ( tween.elem.nodeType === 1 && ( + jQuery.cssHooks[ tween.prop ] || + tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { + jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); + } else { + tween.elem[ tween.prop ] = tween.now; + } + } + } +}; + +// Support: IE <=9 only +// Panic based approach to setting things on disconnected nodes +Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { + set: function( tween ) { + if ( tween.elem.nodeType && tween.elem.parentNode ) { + tween.elem[ tween.prop ] = tween.now; + } + } +}; + +jQuery.easing = { + linear: function( p ) { + return p; + }, + swing: function( p ) { + return 0.5 - Math.cos( p * Math.PI ) / 2; + }, + _default: "swing" +}; + +jQuery.fx = Tween.prototype.init; + +// Back compat <1.8 extension point +jQuery.fx.step = {}; + + + + +var + fxNow, inProgress, + rfxtypes = /^(?:toggle|show|hide)$/, + rrun = /queueHooks$/; + +function schedule() { + if ( inProgress ) { + if ( document.hidden === false && window.requestAnimationFrame ) { + window.requestAnimationFrame( schedule ); + } else { + window.setTimeout( schedule, jQuery.fx.interval ); + } + + jQuery.fx.tick(); + } +} + +// Animations created synchronously will run synchronously +function createFxNow() { + window.setTimeout( function() { + fxNow = undefined; + } ); + return ( fxNow = Date.now() ); +} + +// Generate parameters to create a standard animation +function genFx( type, includeWidth ) { + var which, + i = 0, + attrs = { height: type }; + + // If we include width, step value is 1 to do all cssExpand values, + // otherwise step value is 2 to skip over Left and Right + includeWidth = includeWidth ? 1 : 0; + for ( ; i < 4; i += 2 - includeWidth ) { + which = cssExpand[ i ]; + attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; + } + + if ( includeWidth ) { + attrs.opacity = attrs.width = type; + } + + return attrs; +} + +function createTween( value, prop, animation ) { + var tween, + collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), + index = 0, + length = collection.length; + for ( ; index < length; index++ ) { + if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { + + // We're done with this property + return tween; + } + } +} + +function defaultPrefilter( elem, props, opts ) { + var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, + isBox = "width" in props || "height" in props, + anim = this, + orig = {}, + style = elem.style, + hidden = elem.nodeType && isHiddenWithinTree( elem ), + dataShow = dataPriv.get( elem, "fxshow" ); + + // Queue-skipping animations hijack the fx hooks + if ( !opts.queue ) { + hooks = jQuery._queueHooks( elem, "fx" ); + if ( hooks.unqueued == null ) { + hooks.unqueued = 0; + oldfire = hooks.empty.fire; + hooks.empty.fire = function() { + if ( !hooks.unqueued ) { + oldfire(); + } + }; + } + hooks.unqueued++; + + anim.always( function() { + + // Ensure the complete handler is called before this completes + anim.always( function() { + hooks.unqueued--; + if ( !jQuery.queue( elem, "fx" ).length ) { + hooks.empty.fire(); + } + } ); + } ); + } + + // Detect show/hide animations + for ( prop in props ) { + value = props[ prop ]; + if ( rfxtypes.test( value ) ) { + delete props[ prop ]; + toggle = toggle || value === "toggle"; + if ( value === ( hidden ? "hide" : "show" ) ) { + + // Pretend to be hidden if this is a "show" and + // there is still data from a stopped show/hide + if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { + hidden = true; + + // Ignore all other no-op show/hide data + } else { + continue; + } + } + orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); + } + } + + // Bail out if this is a no-op like .hide().hide() + propTween = !jQuery.isEmptyObject( props ); + if ( !propTween && jQuery.isEmptyObject( orig ) ) { + return; + } + + // Restrict "overflow" and "display" styles during box animations + if ( isBox && elem.nodeType === 1 ) { + + // Support: IE <=9 - 11, Edge 12 - 15 + // Record all 3 overflow attributes because IE does not infer the shorthand + // from identically-valued overflowX and overflowY and Edge just mirrors + // the overflowX value there. + opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; + + // Identify a display type, preferring old show/hide data over the CSS cascade + restoreDisplay = dataShow && dataShow.display; + if ( restoreDisplay == null ) { + restoreDisplay = dataPriv.get( elem, "display" ); + } + display = jQuery.css( elem, "display" ); + if ( display === "none" ) { + if ( restoreDisplay ) { + display = restoreDisplay; + } else { + + // Get nonempty value(s) by temporarily forcing visibility + showHide( [ elem ], true ); + restoreDisplay = elem.style.display || restoreDisplay; + display = jQuery.css( elem, "display" ); + showHide( [ elem ] ); + } + } + + // Animate inline elements as inline-block + if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { + if ( jQuery.css( elem, "float" ) === "none" ) { + + // Restore the original display value at the end of pure show/hide animations + if ( !propTween ) { + anim.done( function() { + style.display = restoreDisplay; + } ); + if ( restoreDisplay == null ) { + display = style.display; + restoreDisplay = display === "none" ? "" : display; + } + } + style.display = "inline-block"; + } + } + } + + if ( opts.overflow ) { + style.overflow = "hidden"; + anim.always( function() { + style.overflow = opts.overflow[ 0 ]; + style.overflowX = opts.overflow[ 1 ]; + style.overflowY = opts.overflow[ 2 ]; + } ); + } + + // Implement show/hide animations + propTween = false; + for ( prop in orig ) { + + // General show/hide setup for this element animation + if ( !propTween ) { + if ( dataShow ) { + if ( "hidden" in dataShow ) { + hidden = dataShow.hidden; + } + } else { + dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); + } + + // Store hidden/visible for toggle so `.stop().toggle()` "reverses" + if ( toggle ) { + dataShow.hidden = !hidden; + } + + // Show elements before animating them + if ( hidden ) { + showHide( [ elem ], true ); + } + + /* eslint-disable no-loop-func */ + + anim.done( function() { + + /* eslint-enable no-loop-func */ + + // The final step of a "hide" animation is actually hiding the element + if ( !hidden ) { + showHide( [ elem ] ); + } + dataPriv.remove( elem, "fxshow" ); + for ( prop in orig ) { + jQuery.style( elem, prop, orig[ prop ] ); + } + } ); + } + + // Per-property setup + propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); + if ( !( prop in dataShow ) ) { + dataShow[ prop ] = propTween.start; + if ( hidden ) { + propTween.end = propTween.start; + propTween.start = 0; + } + } + } +} + +function propFilter( props, specialEasing ) { + var index, name, easing, value, hooks; + + // camelCase, specialEasing and expand cssHook pass + for ( index in props ) { + name = camelCase( index ); + easing = specialEasing[ name ]; + value = props[ index ]; + if ( Array.isArray( value ) ) { + easing = value[ 1 ]; + value = props[ index ] = value[ 0 ]; + } + + if ( index !== name ) { + props[ name ] = value; + delete props[ index ]; + } + + hooks = jQuery.cssHooks[ name ]; + if ( hooks && "expand" in hooks ) { + value = hooks.expand( value ); + delete props[ name ]; + + // Not quite $.extend, this won't overwrite existing keys. + // Reusing 'index' because we have the correct "name" + for ( index in value ) { + if ( !( index in props ) ) { + props[ index ] = value[ index ]; + specialEasing[ index ] = easing; + } + } + } else { + specialEasing[ name ] = easing; + } + } +} + +function Animation( elem, properties, options ) { + var result, + stopped, + index = 0, + length = Animation.prefilters.length, + deferred = jQuery.Deferred().always( function() { + + // Don't match elem in the :animated selector + delete tick.elem; + } ), + tick = function() { + if ( stopped ) { + return false; + } + var currentTime = fxNow || createFxNow(), + remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), + + // Support: Android 2.3 only + // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) + temp = remaining / animation.duration || 0, + percent = 1 - temp, + index = 0, + length = animation.tweens.length; + + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( percent ); + } + + deferred.notifyWith( elem, [ animation, percent, remaining ] ); + + // If there's more to do, yield + if ( percent < 1 && length ) { + return remaining; + } + + // If this was an empty animation, synthesize a final progress notification + if ( !length ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + } + + // Resolve the animation and report its conclusion + deferred.resolveWith( elem, [ animation ] ); + return false; + }, + animation = deferred.promise( { + elem: elem, + props: jQuery.extend( {}, properties ), + opts: jQuery.extend( true, { + specialEasing: {}, + easing: jQuery.easing._default + }, options ), + originalProperties: properties, + originalOptions: options, + startTime: fxNow || createFxNow(), + duration: options.duration, + tweens: [], + createTween: function( prop, end ) { + var tween = jQuery.Tween( elem, animation.opts, prop, end, + animation.opts.specialEasing[ prop ] || animation.opts.easing ); + animation.tweens.push( tween ); + return tween; + }, + stop: function( gotoEnd ) { + var index = 0, + + // If we are going to the end, we want to run all the tweens + // otherwise we skip this part + length = gotoEnd ? animation.tweens.length : 0; + if ( stopped ) { + return this; + } + stopped = true; + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( 1 ); + } + + // Resolve when we played the last frame; otherwise, reject + if ( gotoEnd ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + deferred.resolveWith( elem, [ animation, gotoEnd ] ); + } else { + deferred.rejectWith( elem, [ animation, gotoEnd ] ); + } + return this; + } + } ), + props = animation.props; + + propFilter( props, animation.opts.specialEasing ); + + for ( ; index < length; index++ ) { + result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); + if ( result ) { + if ( isFunction( result.stop ) ) { + jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = + result.stop.bind( result ); + } + return result; + } + } + + jQuery.map( props, createTween, animation ); + + if ( isFunction( animation.opts.start ) ) { + animation.opts.start.call( elem, animation ); + } + + // Attach callbacks from options + animation + .progress( animation.opts.progress ) + .done( animation.opts.done, animation.opts.complete ) + .fail( animation.opts.fail ) + .always( animation.opts.always ); + + jQuery.fx.timer( + jQuery.extend( tick, { + elem: elem, + anim: animation, + queue: animation.opts.queue + } ) + ); + + return animation; +} + +jQuery.Animation = jQuery.extend( Animation, { + + tweeners: { + "*": [ function( prop, value ) { + var tween = this.createTween( prop, value ); + adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); + return tween; + } ] + }, + + tweener: function( props, callback ) { + if ( isFunction( props ) ) { + callback = props; + props = [ "*" ]; + } else { + props = props.match( rnothtmlwhite ); + } + + var prop, + index = 0, + length = props.length; + + for ( ; index < length; index++ ) { + prop = props[ index ]; + Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; + Animation.tweeners[ prop ].unshift( callback ); + } + }, + + prefilters: [ defaultPrefilter ], + + prefilter: function( callback, prepend ) { + if ( prepend ) { + Animation.prefilters.unshift( callback ); + } else { + Animation.prefilters.push( callback ); + } + } +} ); + +jQuery.speed = function( speed, easing, fn ) { + var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { + complete: fn || !fn && easing || + isFunction( speed ) && speed, + duration: speed, + easing: fn && easing || easing && !isFunction( easing ) && easing + }; + + // Go to the end state if fx are off + if ( jQuery.fx.off ) { + opt.duration = 0; + + } else { + if ( typeof opt.duration !== "number" ) { + if ( opt.duration in jQuery.fx.speeds ) { + opt.duration = jQuery.fx.speeds[ opt.duration ]; + + } else { + opt.duration = jQuery.fx.speeds._default; + } + } + } + + // Normalize opt.queue - true/undefined/null -> "fx" + if ( opt.queue == null || opt.queue === true ) { + opt.queue = "fx"; + } + + // Queueing + opt.old = opt.complete; + + opt.complete = function() { + if ( isFunction( opt.old ) ) { + opt.old.call( this ); + } + + if ( opt.queue ) { + jQuery.dequeue( this, opt.queue ); + } + }; + + return opt; +}; + +jQuery.fn.extend( { + fadeTo: function( speed, to, easing, callback ) { + + // Show any hidden elements after setting opacity to 0 + return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() + + // Animate to the value specified + .end().animate( { opacity: to }, speed, easing, callback ); + }, + animate: function( prop, speed, easing, callback ) { + var empty = jQuery.isEmptyObject( prop ), + optall = jQuery.speed( speed, easing, callback ), + doAnimation = function() { + + // Operate on a copy of prop so per-property easing won't be lost + var anim = Animation( this, jQuery.extend( {}, prop ), optall ); + + // Empty animations, or finishing resolves immediately + if ( empty || dataPriv.get( this, "finish" ) ) { + anim.stop( true ); + } + }; + + doAnimation.finish = doAnimation; + + return empty || optall.queue === false ? + this.each( doAnimation ) : + this.queue( optall.queue, doAnimation ); + }, + stop: function( type, clearQueue, gotoEnd ) { + var stopQueue = function( hooks ) { + var stop = hooks.stop; + delete hooks.stop; + stop( gotoEnd ); + }; + + if ( typeof type !== "string" ) { + gotoEnd = clearQueue; + clearQueue = type; + type = undefined; + } + if ( clearQueue ) { + this.queue( type || "fx", [] ); + } + + return this.each( function() { + var dequeue = true, + index = type != null && type + "queueHooks", + timers = jQuery.timers, + data = dataPriv.get( this ); + + if ( index ) { + if ( data[ index ] && data[ index ].stop ) { + stopQueue( data[ index ] ); + } + } else { + for ( index in data ) { + if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { + stopQueue( data[ index ] ); + } + } + } + + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && + ( type == null || timers[ index ].queue === type ) ) { + + timers[ index ].anim.stop( gotoEnd ); + dequeue = false; + timers.splice( index, 1 ); + } + } + + // Start the next in the queue if the last step wasn't forced. + // Timers currently will call their complete callbacks, which + // will dequeue but only if they were gotoEnd. + if ( dequeue || !gotoEnd ) { + jQuery.dequeue( this, type ); + } + } ); + }, + finish: function( type ) { + if ( type !== false ) { + type = type || "fx"; + } + return this.each( function() { + var index, + data = dataPriv.get( this ), + queue = data[ type + "queue" ], + hooks = data[ type + "queueHooks" ], + timers = jQuery.timers, + length = queue ? queue.length : 0; + + // Enable finishing flag on private data + data.finish = true; + + // Empty the queue first + jQuery.queue( this, type, [] ); + + if ( hooks && hooks.stop ) { + hooks.stop.call( this, true ); + } + + // Look for any active animations, and finish them + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && timers[ index ].queue === type ) { + timers[ index ].anim.stop( true ); + timers.splice( index, 1 ); + } + } + + // Look for any animations in the old queue and finish them + for ( index = 0; index < length; index++ ) { + if ( queue[ index ] && queue[ index ].finish ) { + queue[ index ].finish.call( this ); + } + } + + // Turn off finishing flag + delete data.finish; + } ); + } +} ); + +jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { + var cssFn = jQuery.fn[ name ]; + jQuery.fn[ name ] = function( speed, easing, callback ) { + return speed == null || typeof speed === "boolean" ? + cssFn.apply( this, arguments ) : + this.animate( genFx( name, true ), speed, easing, callback ); + }; +} ); + +// Generate shortcuts for custom animations +jQuery.each( { + slideDown: genFx( "show" ), + slideUp: genFx( "hide" ), + slideToggle: genFx( "toggle" ), + fadeIn: { opacity: "show" }, + fadeOut: { opacity: "hide" }, + fadeToggle: { opacity: "toggle" } +}, function( name, props ) { + jQuery.fn[ name ] = function( speed, easing, callback ) { + return this.animate( props, speed, easing, callback ); + }; +} ); + +jQuery.timers = []; +jQuery.fx.tick = function() { + var timer, + i = 0, + timers = jQuery.timers; + + fxNow = Date.now(); + + for ( ; i < timers.length; i++ ) { + timer = timers[ i ]; + + // Run the timer and safely remove it when done (allowing for external removal) + if ( !timer() && timers[ i ] === timer ) { + timers.splice( i--, 1 ); + } + } + + if ( !timers.length ) { + jQuery.fx.stop(); + } + fxNow = undefined; +}; + +jQuery.fx.timer = function( timer ) { + jQuery.timers.push( timer ); + jQuery.fx.start(); +}; + +jQuery.fx.interval = 13; +jQuery.fx.start = function() { + if ( inProgress ) { + return; + } + + inProgress = true; + schedule(); +}; + +jQuery.fx.stop = function() { + inProgress = null; +}; + +jQuery.fx.speeds = { + slow: 600, + fast: 200, + + // Default speed + _default: 400 +}; + + +// Based off of the plugin by Clint Helfers, with permission. +// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ +jQuery.fn.delay = function( time, type ) { + time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; + type = type || "fx"; + + return this.queue( type, function( next, hooks ) { + var timeout = window.setTimeout( next, time ); + hooks.stop = function() { + window.clearTimeout( timeout ); + }; + } ); +}; + + +( function() { + var input = document.createElement( "input" ), + select = document.createElement( "select" ), + opt = select.appendChild( document.createElement( "option" ) ); + + input.type = "checkbox"; + + // Support: Android <=4.3 only + // Default value for a checkbox should be "on" + support.checkOn = input.value !== ""; + + // Support: IE <=11 only + // Must access selectedIndex to make default options select + support.optSelected = opt.selected; + + // Support: IE <=11 only + // An input loses its value after becoming a radio + input = document.createElement( "input" ); + input.value = "t"; + input.type = "radio"; + support.radioValue = input.value === "t"; +} )(); + + +var boolHook, + attrHandle = jQuery.expr.attrHandle; + +jQuery.fn.extend( { + attr: function( name, value ) { + return access( this, jQuery.attr, name, value, arguments.length > 1 ); + }, + + removeAttr: function( name ) { + return this.each( function() { + jQuery.removeAttr( this, name ); + } ); + } +} ); + +jQuery.extend( { + attr: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set attributes on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + // Fallback to prop when attributes are not supported + if ( typeof elem.getAttribute === "undefined" ) { + return jQuery.prop( elem, name, value ); + } + + // Attribute hooks are determined by the lowercase version + // Grab necessary hook if one is defined + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + hooks = jQuery.attrHooks[ name.toLowerCase() ] || + ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); + } + + if ( value !== undefined ) { + if ( value === null ) { + jQuery.removeAttr( elem, name ); + return; + } + + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + elem.setAttribute( name, value + "" ); + return value; + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + ret = jQuery.find.attr( elem, name ); + + // Non-existent attributes return null, we normalize to undefined + return ret == null ? undefined : ret; + }, + + attrHooks: { + type: { + set: function( elem, value ) { + if ( !support.radioValue && value === "radio" && + nodeName( elem, "input" ) ) { + var val = elem.value; + elem.setAttribute( "type", value ); + if ( val ) { + elem.value = val; + } + return value; + } + } + } + }, + + removeAttr: function( elem, value ) { + var name, + i = 0, + + // Attribute names can contain non-HTML whitespace characters + // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 + attrNames = value && value.match( rnothtmlwhite ); + + if ( attrNames && elem.nodeType === 1 ) { + while ( ( name = attrNames[ i++ ] ) ) { + elem.removeAttribute( name ); + } + } + } +} ); + +// Hooks for boolean attributes +boolHook = { + set: function( elem, value, name ) { + if ( value === false ) { + + // Remove boolean attributes when set to false + jQuery.removeAttr( elem, name ); + } else { + elem.setAttribute( name, name ); + } + return name; + } +}; + +jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { + var getter = attrHandle[ name ] || jQuery.find.attr; + + attrHandle[ name ] = function( elem, name, isXML ) { + var ret, handle, + lowercaseName = name.toLowerCase(); + + if ( !isXML ) { + + // Avoid an infinite loop by temporarily removing this function from the getter + handle = attrHandle[ lowercaseName ]; + attrHandle[ lowercaseName ] = ret; + ret = getter( elem, name, isXML ) != null ? + lowercaseName : + null; + attrHandle[ lowercaseName ] = handle; + } + return ret; + }; +} ); + + + + +var rfocusable = /^(?:input|select|textarea|button)$/i, + rclickable = /^(?:a|area)$/i; + +jQuery.fn.extend( { + prop: function( name, value ) { + return access( this, jQuery.prop, name, value, arguments.length > 1 ); + }, + + removeProp: function( name ) { + return this.each( function() { + delete this[ jQuery.propFix[ name ] || name ]; + } ); + } +} ); + +jQuery.extend( { + prop: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set properties on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + + // Fix name and attach hooks + name = jQuery.propFix[ name ] || name; + hooks = jQuery.propHooks[ name ]; + } + + if ( value !== undefined ) { + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + return ( elem[ name ] = value ); + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + return elem[ name ]; + }, + + propHooks: { + tabIndex: { + get: function( elem ) { + + // Support: IE <=9 - 11 only + // elem.tabIndex doesn't always return the + // correct value when it hasn't been explicitly set + // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ + // Use proper attribute retrieval(#12072) + var tabindex = jQuery.find.attr( elem, "tabindex" ); + + if ( tabindex ) { + return parseInt( tabindex, 10 ); + } + + if ( + rfocusable.test( elem.nodeName ) || + rclickable.test( elem.nodeName ) && + elem.href + ) { + return 0; + } + + return -1; + } + } + }, + + propFix: { + "for": "htmlFor", + "class": "className" + } +} ); + +// Support: IE <=11 only +// Accessing the selectedIndex property +// forces the browser to respect setting selected +// on the option +// The getter ensures a default option is selected +// when in an optgroup +// eslint rule "no-unused-expressions" is disabled for this code +// since it considers such accessions noop +if ( !support.optSelected ) { + jQuery.propHooks.selected = { + get: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent && parent.parentNode ) { + parent.parentNode.selectedIndex; + } + return null; + }, + set: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent ) { + parent.selectedIndex; + + if ( parent.parentNode ) { + parent.parentNode.selectedIndex; + } + } + } + }; +} + +jQuery.each( [ + "tabIndex", + "readOnly", + "maxLength", + "cellSpacing", + "cellPadding", + "rowSpan", + "colSpan", + "useMap", + "frameBorder", + "contentEditable" +], function() { + jQuery.propFix[ this.toLowerCase() ] = this; +} ); + + + + + // Strip and collapse whitespace according to HTML spec + // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace + function stripAndCollapse( value ) { + var tokens = value.match( rnothtmlwhite ) || []; + return tokens.join( " " ); + } + + +function getClass( elem ) { + return elem.getAttribute && elem.getAttribute( "class" ) || ""; +} + +function classesToArray( value ) { + if ( Array.isArray( value ) ) { + return value; + } + if ( typeof value === "string" ) { + return value.match( rnothtmlwhite ) || []; + } + return []; +} + +jQuery.fn.extend( { + addClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + if ( cur.indexOf( " " + clazz + " " ) < 0 ) { + cur += clazz + " "; + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + removeClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + if ( !arguments.length ) { + return this.attr( "class", "" ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + + // This expression is here for better compressibility (see addClass) + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + + // Remove *all* instances + while ( cur.indexOf( " " + clazz + " " ) > -1 ) { + cur = cur.replace( " " + clazz + " ", " " ); + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + toggleClass: function( value, stateVal ) { + var type = typeof value, + isValidValue = type === "string" || Array.isArray( value ); + + if ( typeof stateVal === "boolean" && isValidValue ) { + return stateVal ? this.addClass( value ) : this.removeClass( value ); + } + + if ( isFunction( value ) ) { + return this.each( function( i ) { + jQuery( this ).toggleClass( + value.call( this, i, getClass( this ), stateVal ), + stateVal + ); + } ); + } + + return this.each( function() { + var className, i, self, classNames; + + if ( isValidValue ) { + + // Toggle individual class names + i = 0; + self = jQuery( this ); + classNames = classesToArray( value ); + + while ( ( className = classNames[ i++ ] ) ) { + + // Check each className given, space separated list + if ( self.hasClass( className ) ) { + self.removeClass( className ); + } else { + self.addClass( className ); + } + } + + // Toggle whole class name + } else if ( value === undefined || type === "boolean" ) { + className = getClass( this ); + if ( className ) { + + // Store className if set + dataPriv.set( this, "__className__", className ); + } + + // If the element has a class name or if we're passed `false`, + // then remove the whole classname (if there was one, the above saved it). + // Otherwise bring back whatever was previously saved (if anything), + // falling back to the empty string if nothing was stored. + if ( this.setAttribute ) { + this.setAttribute( "class", + className || value === false ? + "" : + dataPriv.get( this, "__className__" ) || "" + ); + } + } + } ); + }, + + hasClass: function( selector ) { + var className, elem, + i = 0; + + className = " " + selector + " "; + while ( ( elem = this[ i++ ] ) ) { + if ( elem.nodeType === 1 && + ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { + return true; + } + } + + return false; + } +} ); + + + + +var rreturn = /\r/g; + +jQuery.fn.extend( { + val: function( value ) { + var hooks, ret, valueIsFunction, + elem = this[ 0 ]; + + if ( !arguments.length ) { + if ( elem ) { + hooks = jQuery.valHooks[ elem.type ] || + jQuery.valHooks[ elem.nodeName.toLowerCase() ]; + + if ( hooks && + "get" in hooks && + ( ret = hooks.get( elem, "value" ) ) !== undefined + ) { + return ret; + } + + ret = elem.value; + + // Handle most common string cases + if ( typeof ret === "string" ) { + return ret.replace( rreturn, "" ); + } + + // Handle cases where value is null/undef or number + return ret == null ? "" : ret; + } + + return; + } + + valueIsFunction = isFunction( value ); + + return this.each( function( i ) { + var val; + + if ( this.nodeType !== 1 ) { + return; + } + + if ( valueIsFunction ) { + val = value.call( this, i, jQuery( this ).val() ); + } else { + val = value; + } + + // Treat null/undefined as ""; convert numbers to string + if ( val == null ) { + val = ""; + + } else if ( typeof val === "number" ) { + val += ""; + + } else if ( Array.isArray( val ) ) { + val = jQuery.map( val, function( value ) { + return value == null ? "" : value + ""; + } ); + } + + hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; + + // If set returns undefined, fall back to normal setting + if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { + this.value = val; + } + } ); + } +} ); + +jQuery.extend( { + valHooks: { + option: { + get: function( elem ) { + + var val = jQuery.find.attr( elem, "value" ); + return val != null ? + val : + + // Support: IE <=10 - 11 only + // option.text throws exceptions (#14686, #14858) + // Strip and collapse whitespace + // https://html.spec.whatwg.org/#strip-and-collapse-whitespace + stripAndCollapse( jQuery.text( elem ) ); + } + }, + select: { + get: function( elem ) { + var value, option, i, + options = elem.options, + index = elem.selectedIndex, + one = elem.type === "select-one", + values = one ? null : [], + max = one ? index + 1 : options.length; + + if ( index < 0 ) { + i = max; + + } else { + i = one ? index : 0; + } + + // Loop through all the selected options + for ( ; i < max; i++ ) { + option = options[ i ]; + + // Support: IE <=9 only + // IE8-9 doesn't update selected after form reset (#2551) + if ( ( option.selected || i === index ) && + + // Don't return options that are disabled or in a disabled optgroup + !option.disabled && + ( !option.parentNode.disabled || + !nodeName( option.parentNode, "optgroup" ) ) ) { + + // Get the specific value for the option + value = jQuery( option ).val(); + + // We don't need an array for one selects + if ( one ) { + return value; + } + + // Multi-Selects return an array + values.push( value ); + } + } + + return values; + }, + + set: function( elem, value ) { + var optionSet, option, + options = elem.options, + values = jQuery.makeArray( value ), + i = options.length; + + while ( i-- ) { + option = options[ i ]; + + /* eslint-disable no-cond-assign */ + + if ( option.selected = + jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 + ) { + optionSet = true; + } + + /* eslint-enable no-cond-assign */ + } + + // Force browsers to behave consistently when non-matching value is set + if ( !optionSet ) { + elem.selectedIndex = -1; + } + return values; + } + } + } +} ); + +// Radios and checkboxes getter/setter +jQuery.each( [ "radio", "checkbox" ], function() { + jQuery.valHooks[ this ] = { + set: function( elem, value ) { + if ( Array.isArray( value ) ) { + return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); + } + } + }; + if ( !support.checkOn ) { + jQuery.valHooks[ this ].get = function( elem ) { + return elem.getAttribute( "value" ) === null ? "on" : elem.value; + }; + } +} ); + + + + +// Return jQuery for attributes-only inclusion + + +support.focusin = "onfocusin" in window; + + +var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, + stopPropagationCallback = function( e ) { + e.stopPropagation(); + }; + +jQuery.extend( jQuery.event, { + + trigger: function( event, data, elem, onlyHandlers ) { + + var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, + eventPath = [ elem || document ], + type = hasOwn.call( event, "type" ) ? event.type : event, + namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; + + cur = lastElement = tmp = elem = elem || document; + + // Don't do events on text and comment nodes + if ( elem.nodeType === 3 || elem.nodeType === 8 ) { + return; + } + + // focus/blur morphs to focusin/out; ensure we're not firing them right now + if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { + return; + } + + if ( type.indexOf( "." ) > -1 ) { + + // Namespaced trigger; create a regexp to match event type in handle() + namespaces = type.split( "." ); + type = namespaces.shift(); + namespaces.sort(); + } + ontype = type.indexOf( ":" ) < 0 && "on" + type; + + // Caller can pass in a jQuery.Event object, Object, or just an event type string + event = event[ jQuery.expando ] ? + event : + new jQuery.Event( type, typeof event === "object" && event ); + + // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) + event.isTrigger = onlyHandlers ? 2 : 3; + event.namespace = namespaces.join( "." ); + event.rnamespace = event.namespace ? + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : + null; + + // Clean up the event in case it is being reused + event.result = undefined; + if ( !event.target ) { + event.target = elem; + } + + // Clone any incoming data and prepend the event, creating the handler arg list + data = data == null ? + [ event ] : + jQuery.makeArray( data, [ event ] ); + + // Allow special events to draw outside the lines + special = jQuery.event.special[ type ] || {}; + if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { + return; + } + + // Determine event propagation path in advance, per W3C events spec (#9951) + // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) + if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { + + bubbleType = special.delegateType || type; + if ( !rfocusMorph.test( bubbleType + type ) ) { + cur = cur.parentNode; + } + for ( ; cur; cur = cur.parentNode ) { + eventPath.push( cur ); + tmp = cur; + } + + // Only add window if we got to document (e.g., not plain obj or detached DOM) + if ( tmp === ( elem.ownerDocument || document ) ) { + eventPath.push( tmp.defaultView || tmp.parentWindow || window ); + } + } + + // Fire handlers on the event path + i = 0; + while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { + lastElement = cur; + event.type = i > 1 ? + bubbleType : + special.bindType || type; + + // jQuery handler + handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] && + dataPriv.get( cur, "handle" ); + if ( handle ) { + handle.apply( cur, data ); + } + + // Native handler + handle = ontype && cur[ ontype ]; + if ( handle && handle.apply && acceptData( cur ) ) { + event.result = handle.apply( cur, data ); + if ( event.result === false ) { + event.preventDefault(); + } + } + } + event.type = type; + + // If nobody prevented the default action, do it now + if ( !onlyHandlers && !event.isDefaultPrevented() ) { + + if ( ( !special._default || + special._default.apply( eventPath.pop(), data ) === false ) && + acceptData( elem ) ) { + + // Call a native DOM method on the target with the same name as the event. + // Don't do default actions on window, that's where global variables be (#6170) + if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { + + // Don't re-trigger an onFOO event when we call its FOO() method + tmp = elem[ ontype ]; + + if ( tmp ) { + elem[ ontype ] = null; + } + + // Prevent re-triggering of the same event, since we already bubbled it above + jQuery.event.triggered = type; + + if ( event.isPropagationStopped() ) { + lastElement.addEventListener( type, stopPropagationCallback ); + } + + elem[ type ](); + + if ( event.isPropagationStopped() ) { + lastElement.removeEventListener( type, stopPropagationCallback ); + } + + jQuery.event.triggered = undefined; + + if ( tmp ) { + elem[ ontype ] = tmp; + } + } + } + } + + return event.result; + }, + + // Piggyback on a donor event to simulate a different one + // Used only for `focus(in | out)` events + simulate: function( type, elem, event ) { + var e = jQuery.extend( + new jQuery.Event(), + event, + { + type: type, + isSimulated: true + } + ); + + jQuery.event.trigger( e, null, elem ); + } + +} ); + +jQuery.fn.extend( { + + trigger: function( type, data ) { + return this.each( function() { + jQuery.event.trigger( type, data, this ); + } ); + }, + triggerHandler: function( type, data ) { + var elem = this[ 0 ]; + if ( elem ) { + return jQuery.event.trigger( type, data, elem, true ); + } + } +} ); + + +// Support: Firefox <=44 +// Firefox doesn't have focus(in | out) events +// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 +// +// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 +// focus(in | out) events fire after focus & blur events, +// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order +// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 +if ( !support.focusin ) { + jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { + + // Attach a single capturing handler on the document while someone wants focusin/focusout + var handler = function( event ) { + jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); + }; + + jQuery.event.special[ fix ] = { + setup: function() { + + // Handle: regular nodes (via `this.ownerDocument`), window + // (via `this.document`) & document (via `this`). + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ); + + if ( !attaches ) { + doc.addEventListener( orig, handler, true ); + } + dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); + }, + teardown: function() { + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ) - 1; + + if ( !attaches ) { + doc.removeEventListener( orig, handler, true ); + dataPriv.remove( doc, fix ); + + } else { + dataPriv.access( doc, fix, attaches ); + } + } + }; + } ); +} +var location = window.location; + +var nonce = { guid: Date.now() }; + +var rquery = ( /\?/ ); + + + +// Cross-browser xml parsing +jQuery.parseXML = function( data ) { + var xml, parserErrorElem; + if ( !data || typeof data !== "string" ) { + return null; + } + + // Support: IE 9 - 11 only + // IE throws on parseFromString with invalid input. + try { + xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); + } catch ( e ) {} + + parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ]; + if ( !xml || parserErrorElem ) { + jQuery.error( "Invalid XML: " + ( + parserErrorElem ? + jQuery.map( parserErrorElem.childNodes, function( el ) { + return el.textContent; + } ).join( "\n" ) : + data + ) ); + } + return xml; +}; + + +var + rbracket = /\[\]$/, + rCRLF = /\r?\n/g, + rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, + rsubmittable = /^(?:input|select|textarea|keygen)/i; + +function buildParams( prefix, obj, traditional, add ) { + var name; + + if ( Array.isArray( obj ) ) { + + // Serialize array item. + jQuery.each( obj, function( i, v ) { + if ( traditional || rbracket.test( prefix ) ) { + + // Treat each array item as a scalar. + add( prefix, v ); + + } else { + + // Item is non-scalar (array or object), encode its numeric index. + buildParams( + prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", + v, + traditional, + add + ); + } + } ); + + } else if ( !traditional && toType( obj ) === "object" ) { + + // Serialize object item. + for ( name in obj ) { + buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); + } + + } else { + + // Serialize scalar item. + add( prefix, obj ); + } +} + +// Serialize an array of form elements or a set of +// key/values into a query string +jQuery.param = function( a, traditional ) { + var prefix, + s = [], + add = function( key, valueOrFunction ) { + + // If value is a function, invoke it and use its return value + var value = isFunction( valueOrFunction ) ? + valueOrFunction() : + valueOrFunction; + + s[ s.length ] = encodeURIComponent( key ) + "=" + + encodeURIComponent( value == null ? "" : value ); + }; + + if ( a == null ) { + return ""; + } + + // If an array was passed in, assume that it is an array of form elements. + if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { + + // Serialize the form elements + jQuery.each( a, function() { + add( this.name, this.value ); + } ); + + } else { + + // If traditional, encode the "old" way (the way 1.3.2 or older + // did it), otherwise encode params recursively. + for ( prefix in a ) { + buildParams( prefix, a[ prefix ], traditional, add ); + } + } + + // Return the resulting serialization + return s.join( "&" ); +}; + +jQuery.fn.extend( { + serialize: function() { + return jQuery.param( this.serializeArray() ); + }, + serializeArray: function() { + return this.map( function() { + + // Can add propHook for "elements" to filter or add form elements + var elements = jQuery.prop( this, "elements" ); + return elements ? jQuery.makeArray( elements ) : this; + } ).filter( function() { + var type = this.type; + + // Use .is( ":disabled" ) so that fieldset[disabled] works + return this.name && !jQuery( this ).is( ":disabled" ) && + rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && + ( this.checked || !rcheckableType.test( type ) ); + } ).map( function( _i, elem ) { + var val = jQuery( this ).val(); + + if ( val == null ) { + return null; + } + + if ( Array.isArray( val ) ) { + return jQuery.map( val, function( val ) { + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ); + } + + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ).get(); + } +} ); + + +var + r20 = /%20/g, + rhash = /#.*$/, + rantiCache = /([?&])_=[^&]*/, + rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, + + // #7653, #8125, #8152: local protocol detection + rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, + rnoContent = /^(?:GET|HEAD)$/, + rprotocol = /^\/\//, + + /* Prefilters + * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) + * 2) These are called: + * - BEFORE asking for a transport + * - AFTER param serialization (s.data is a string if s.processData is true) + * 3) key is the dataType + * 4) the catchall symbol "*" can be used + * 5) execution will start with transport dataType and THEN continue down to "*" if needed + */ + prefilters = {}, + + /* Transports bindings + * 1) key is the dataType + * 2) the catchall symbol "*" can be used + * 3) selection will start with transport dataType and THEN go to "*" if needed + */ + transports = {}, + + // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression + allTypes = "*/".concat( "*" ), + + // Anchor tag for parsing the document origin + originAnchor = document.createElement( "a" ); + +originAnchor.href = location.href; + +// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport +function addToPrefiltersOrTransports( structure ) { + + // dataTypeExpression is optional and defaults to "*" + return function( dataTypeExpression, func ) { + + if ( typeof dataTypeExpression !== "string" ) { + func = dataTypeExpression; + dataTypeExpression = "*"; + } + + var dataType, + i = 0, + dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; + + if ( isFunction( func ) ) { + + // For each dataType in the dataTypeExpression + while ( ( dataType = dataTypes[ i++ ] ) ) { + + // Prepend if requested + if ( dataType[ 0 ] === "+" ) { + dataType = dataType.slice( 1 ) || "*"; + ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); + + // Otherwise append + } else { + ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); + } + } + } + }; +} + +// Base inspection function for prefilters and transports +function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { + + var inspected = {}, + seekingTransport = ( structure === transports ); + + function inspect( dataType ) { + var selected; + inspected[ dataType ] = true; + jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { + var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); + if ( typeof dataTypeOrTransport === "string" && + !seekingTransport && !inspected[ dataTypeOrTransport ] ) { + + options.dataTypes.unshift( dataTypeOrTransport ); + inspect( dataTypeOrTransport ); + return false; + } else if ( seekingTransport ) { + return !( selected = dataTypeOrTransport ); + } + } ); + return selected; + } + + return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); +} + +// A special extend for ajax options +// that takes "flat" options (not to be deep extended) +// Fixes #9887 +function ajaxExtend( target, src ) { + var key, deep, + flatOptions = jQuery.ajaxSettings.flatOptions || {}; + + for ( key in src ) { + if ( src[ key ] !== undefined ) { + ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; + } + } + if ( deep ) { + jQuery.extend( true, target, deep ); + } + + return target; +} + +/* Handles responses to an ajax request: + * - finds the right dataType (mediates between content-type and expected dataType) + * - returns the corresponding response + */ +function ajaxHandleResponses( s, jqXHR, responses ) { + + var ct, type, finalDataType, firstDataType, + contents = s.contents, + dataTypes = s.dataTypes; + + // Remove auto dataType and get content-type in the process + while ( dataTypes[ 0 ] === "*" ) { + dataTypes.shift(); + if ( ct === undefined ) { + ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); + } + } + + // Check if we're dealing with a known content-type + if ( ct ) { + for ( type in contents ) { + if ( contents[ type ] && contents[ type ].test( ct ) ) { + dataTypes.unshift( type ); + break; + } + } + } + + // Check to see if we have a response for the expected dataType + if ( dataTypes[ 0 ] in responses ) { + finalDataType = dataTypes[ 0 ]; + } else { + + // Try convertible dataTypes + for ( type in responses ) { + if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { + finalDataType = type; + break; + } + if ( !firstDataType ) { + firstDataType = type; + } + } + + // Or just use first one + finalDataType = finalDataType || firstDataType; + } + + // If we found a dataType + // We add the dataType to the list if needed + // and return the corresponding response + if ( finalDataType ) { + if ( finalDataType !== dataTypes[ 0 ] ) { + dataTypes.unshift( finalDataType ); + } + return responses[ finalDataType ]; + } +} + +/* Chain conversions given the request and the original response + * Also sets the responseXXX fields on the jqXHR instance + */ +function ajaxConvert( s, response, jqXHR, isSuccess ) { + var conv2, current, conv, tmp, prev, + converters = {}, + + // Work with a copy of dataTypes in case we need to modify it for conversion + dataTypes = s.dataTypes.slice(); + + // Create converters map with lowercased keys + if ( dataTypes[ 1 ] ) { + for ( conv in s.converters ) { + converters[ conv.toLowerCase() ] = s.converters[ conv ]; + } + } + + current = dataTypes.shift(); + + // Convert to each sequential dataType + while ( current ) { + + if ( s.responseFields[ current ] ) { + jqXHR[ s.responseFields[ current ] ] = response; + } + + // Apply the dataFilter if provided + if ( !prev && isSuccess && s.dataFilter ) { + response = s.dataFilter( response, s.dataType ); + } + + prev = current; + current = dataTypes.shift(); + + if ( current ) { + + // There's only work to do if current dataType is non-auto + if ( current === "*" ) { + + current = prev; + + // Convert response if prev dataType is non-auto and differs from current + } else if ( prev !== "*" && prev !== current ) { + + // Seek a direct converter + conv = converters[ prev + " " + current ] || converters[ "* " + current ]; + + // If none found, seek a pair + if ( !conv ) { + for ( conv2 in converters ) { + + // If conv2 outputs current + tmp = conv2.split( " " ); + if ( tmp[ 1 ] === current ) { + + // If prev can be converted to accepted input + conv = converters[ prev + " " + tmp[ 0 ] ] || + converters[ "* " + tmp[ 0 ] ]; + if ( conv ) { + + // Condense equivalence converters + if ( conv === true ) { + conv = converters[ conv2 ]; + + // Otherwise, insert the intermediate dataType + } else if ( converters[ conv2 ] !== true ) { + current = tmp[ 0 ]; + dataTypes.unshift( tmp[ 1 ] ); + } + break; + } + } + } + } + + // Apply converter (if not an equivalence) + if ( conv !== true ) { + + // Unless errors are allowed to bubble, catch and return them + if ( conv && s.throws ) { + response = conv( response ); + } else { + try { + response = conv( response ); + } catch ( e ) { + return { + state: "parsererror", + error: conv ? e : "No conversion from " + prev + " to " + current + }; + } + } + } + } + } + } + + return { state: "success", data: response }; +} + +jQuery.extend( { + + // Counter for holding the number of active queries + active: 0, + + // Last-Modified header cache for next request + lastModified: {}, + etag: {}, + + ajaxSettings: { + url: location.href, + type: "GET", + isLocal: rlocalProtocol.test( location.protocol ), + global: true, + processData: true, + async: true, + contentType: "application/x-www-form-urlencoded; charset=UTF-8", + + /* + timeout: 0, + data: null, + dataType: null, + username: null, + password: null, + cache: null, + throws: false, + traditional: false, + headers: {}, + */ + + accepts: { + "*": allTypes, + text: "text/plain", + html: "text/html", + xml: "application/xml, text/xml", + json: "application/json, text/javascript" + }, + + contents: { + xml: /\bxml\b/, + html: /\bhtml/, + json: /\bjson\b/ + }, + + responseFields: { + xml: "responseXML", + text: "responseText", + json: "responseJSON" + }, + + // Data converters + // Keys separate source (or catchall "*") and destination types with a single space + converters: { + + // Convert anything to text + "* text": String, + + // Text to html (true = no transformation) + "text html": true, + + // Evaluate text as a json expression + "text json": JSON.parse, + + // Parse text as xml + "text xml": jQuery.parseXML + }, + + // For options that shouldn't be deep extended: + // you can add your own custom options here if + // and when you create one that shouldn't be + // deep extended (see ajaxExtend) + flatOptions: { + url: true, + context: true + } + }, + + // Creates a full fledged settings object into target + // with both ajaxSettings and settings fields. + // If target is omitted, writes into ajaxSettings. + ajaxSetup: function( target, settings ) { + return settings ? + + // Building a settings object + ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : + + // Extending ajaxSettings + ajaxExtend( jQuery.ajaxSettings, target ); + }, + + ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), + ajaxTransport: addToPrefiltersOrTransports( transports ), + + // Main method + ajax: function( url, options ) { + + // If url is an object, simulate pre-1.5 signature + if ( typeof url === "object" ) { + options = url; + url = undefined; + } + + // Force options to be an object + options = options || {}; + + var transport, + + // URL without anti-cache param + cacheURL, + + // Response headers + responseHeadersString, + responseHeaders, + + // timeout handle + timeoutTimer, + + // Url cleanup var + urlAnchor, + + // Request state (becomes false upon send and true upon completion) + completed, + + // To know if global events are to be dispatched + fireGlobals, + + // Loop variable + i, + + // uncached part of the url + uncached, + + // Create the final options object + s = jQuery.ajaxSetup( {}, options ), + + // Callbacks context + callbackContext = s.context || s, + + // Context for global events is callbackContext if it is a DOM node or jQuery collection + globalEventContext = s.context && + ( callbackContext.nodeType || callbackContext.jquery ) ? + jQuery( callbackContext ) : + jQuery.event, + + // Deferreds + deferred = jQuery.Deferred(), + completeDeferred = jQuery.Callbacks( "once memory" ), + + // Status-dependent callbacks + statusCode = s.statusCode || {}, + + // Headers (they are sent all at once) + requestHeaders = {}, + requestHeadersNames = {}, + + // Default abort message + strAbort = "canceled", + + // Fake xhr + jqXHR = { + readyState: 0, + + // Builds headers hashtable if needed + getResponseHeader: function( key ) { + var match; + if ( completed ) { + if ( !responseHeaders ) { + responseHeaders = {}; + while ( ( match = rheaders.exec( responseHeadersString ) ) ) { + responseHeaders[ match[ 1 ].toLowerCase() + " " ] = + ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) + .concat( match[ 2 ] ); + } + } + match = responseHeaders[ key.toLowerCase() + " " ]; + } + return match == null ? null : match.join( ", " ); + }, + + // Raw string + getAllResponseHeaders: function() { + return completed ? responseHeadersString : null; + }, + + // Caches the header + setRequestHeader: function( name, value ) { + if ( completed == null ) { + name = requestHeadersNames[ name.toLowerCase() ] = + requestHeadersNames[ name.toLowerCase() ] || name; + requestHeaders[ name ] = value; + } + return this; + }, + + // Overrides response content-type header + overrideMimeType: function( type ) { + if ( completed == null ) { + s.mimeType = type; + } + return this; + }, + + // Status-dependent callbacks + statusCode: function( map ) { + var code; + if ( map ) { + if ( completed ) { + + // Execute the appropriate callbacks + jqXHR.always( map[ jqXHR.status ] ); + } else { + + // Lazy-add the new callbacks in a way that preserves old ones + for ( code in map ) { + statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; + } + } + } + return this; + }, + + // Cancel the request + abort: function( statusText ) { + var finalText = statusText || strAbort; + if ( transport ) { + transport.abort( finalText ); + } + done( 0, finalText ); + return this; + } + }; + + // Attach deferreds + deferred.promise( jqXHR ); + + // Add protocol if not provided (prefilters might expect it) + // Handle falsy url in the settings object (#10093: consistency with old signature) + // We also use the url parameter if available + s.url = ( ( url || s.url || location.href ) + "" ) + .replace( rprotocol, location.protocol + "//" ); + + // Alias method option to type as per ticket #12004 + s.type = options.method || options.type || s.method || s.type; + + // Extract dataTypes list + s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; + + // A cross-domain request is in order when the origin doesn't match the current origin. + if ( s.crossDomain == null ) { + urlAnchor = document.createElement( "a" ); + + // Support: IE <=8 - 11, Edge 12 - 15 + // IE throws exception on accessing the href property if url is malformed, + // e.g. http://example.com:80x/ + try { + urlAnchor.href = s.url; + + // Support: IE <=8 - 11 only + // Anchor's host property isn't correctly set when s.url is relative + urlAnchor.href = urlAnchor.href; + s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== + urlAnchor.protocol + "//" + urlAnchor.host; + } catch ( e ) { + + // If there is an error parsing the URL, assume it is crossDomain, + // it can be rejected by the transport if it is invalid + s.crossDomain = true; + } + } + + // Convert data if not already a string + if ( s.data && s.processData && typeof s.data !== "string" ) { + s.data = jQuery.param( s.data, s.traditional ); + } + + // Apply prefilters + inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); + + // If request was aborted inside a prefilter, stop there + if ( completed ) { + return jqXHR; + } + + // We can fire global events as of now if asked to + // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) + fireGlobals = jQuery.event && s.global; + + // Watch for a new set of requests + if ( fireGlobals && jQuery.active++ === 0 ) { + jQuery.event.trigger( "ajaxStart" ); + } + + // Uppercase the type + s.type = s.type.toUpperCase(); + + // Determine if request has content + s.hasContent = !rnoContent.test( s.type ); + + // Save the URL in case we're toying with the If-Modified-Since + // and/or If-None-Match header later on + // Remove hash to simplify url manipulation + cacheURL = s.url.replace( rhash, "" ); + + // More options handling for requests with no content + if ( !s.hasContent ) { + + // Remember the hash so we can put it back + uncached = s.url.slice( cacheURL.length ); + + // If data is available and should be processed, append data to url + if ( s.data && ( s.processData || typeof s.data === "string" ) ) { + cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; + + // #9682: remove data so that it's not used in an eventual retry + delete s.data; + } + + // Add or update anti-cache param if needed + if ( s.cache === false ) { + cacheURL = cacheURL.replace( rantiCache, "$1" ); + uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + + uncached; + } + + // Put hash and anti-cache on the URL that will be requested (gh-1732) + s.url = cacheURL + uncached; + + // Change '%20' to '+' if this is encoded form body content (gh-2658) + } else if ( s.data && s.processData && + ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { + s.data = s.data.replace( r20, "+" ); + } + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + if ( jQuery.lastModified[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); + } + if ( jQuery.etag[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); + } + } + + // Set the correct header, if data is being sent + if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { + jqXHR.setRequestHeader( "Content-Type", s.contentType ); + } + + // Set the Accepts header for the server, depending on the dataType + jqXHR.setRequestHeader( + "Accept", + s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? + s.accepts[ s.dataTypes[ 0 ] ] + + ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : + s.accepts[ "*" ] + ); + + // Check for headers option + for ( i in s.headers ) { + jqXHR.setRequestHeader( i, s.headers[ i ] ); + } + + // Allow custom headers/mimetypes and early abort + if ( s.beforeSend && + ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { + + // Abort if not done already and return + return jqXHR.abort(); + } + + // Aborting is no longer a cancellation + strAbort = "abort"; + + // Install callbacks on deferreds + completeDeferred.add( s.complete ); + jqXHR.done( s.success ); + jqXHR.fail( s.error ); + + // Get transport + transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); + + // If no transport, we auto-abort + if ( !transport ) { + done( -1, "No Transport" ); + } else { + jqXHR.readyState = 1; + + // Send global event + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); + } + + // If request was aborted inside ajaxSend, stop there + if ( completed ) { + return jqXHR; + } + + // Timeout + if ( s.async && s.timeout > 0 ) { + timeoutTimer = window.setTimeout( function() { + jqXHR.abort( "timeout" ); + }, s.timeout ); + } + + try { + completed = false; + transport.send( requestHeaders, done ); + } catch ( e ) { + + // Rethrow post-completion exceptions + if ( completed ) { + throw e; + } + + // Propagate others as results + done( -1, e ); + } + } + + // Callback for when everything is done + function done( status, nativeStatusText, responses, headers ) { + var isSuccess, success, error, response, modified, + statusText = nativeStatusText; + + // Ignore repeat invocations + if ( completed ) { + return; + } + + completed = true; + + // Clear timeout if it exists + if ( timeoutTimer ) { + window.clearTimeout( timeoutTimer ); + } + + // Dereference transport for early garbage collection + // (no matter how long the jqXHR object will be used) + transport = undefined; + + // Cache response headers + responseHeadersString = headers || ""; + + // Set readyState + jqXHR.readyState = status > 0 ? 4 : 0; + + // Determine if successful + isSuccess = status >= 200 && status < 300 || status === 304; + + // Get response data + if ( responses ) { + response = ajaxHandleResponses( s, jqXHR, responses ); + } + + // Use a noop converter for missing script but not if jsonp + if ( !isSuccess && + jQuery.inArray( "script", s.dataTypes ) > -1 && + jQuery.inArray( "json", s.dataTypes ) < 0 ) { + s.converters[ "text script" ] = function() {}; + } + + // Convert no matter what (that way responseXXX fields are always set) + response = ajaxConvert( s, response, jqXHR, isSuccess ); + + // If successful, handle type chaining + if ( isSuccess ) { + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + modified = jqXHR.getResponseHeader( "Last-Modified" ); + if ( modified ) { + jQuery.lastModified[ cacheURL ] = modified; + } + modified = jqXHR.getResponseHeader( "etag" ); + if ( modified ) { + jQuery.etag[ cacheURL ] = modified; + } + } + + // if no content + if ( status === 204 || s.type === "HEAD" ) { + statusText = "nocontent"; + + // if not modified + } else if ( status === 304 ) { + statusText = "notmodified"; + + // If we have data, let's convert it + } else { + statusText = response.state; + success = response.data; + error = response.error; + isSuccess = !error; + } + } else { + + // Extract error from statusText and normalize for non-aborts + error = statusText; + if ( status || !statusText ) { + statusText = "error"; + if ( status < 0 ) { + status = 0; + } + } + } + + // Set data for the fake xhr object + jqXHR.status = status; + jqXHR.statusText = ( nativeStatusText || statusText ) + ""; + + // Success/Error + if ( isSuccess ) { + deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); + } else { + deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); + } + + // Status-dependent callbacks + jqXHR.statusCode( statusCode ); + statusCode = undefined; + + if ( fireGlobals ) { + globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", + [ jqXHR, s, isSuccess ? success : error ] ); + } + + // Complete + completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); + + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); + + // Handle the global AJAX counter + if ( !( --jQuery.active ) ) { + jQuery.event.trigger( "ajaxStop" ); + } + } + } + + return jqXHR; + }, + + getJSON: function( url, data, callback ) { + return jQuery.get( url, data, callback, "json" ); + }, + + getScript: function( url, callback ) { + return jQuery.get( url, undefined, callback, "script" ); + } +} ); + +jQuery.each( [ "get", "post" ], function( _i, method ) { + jQuery[ method ] = function( url, data, callback, type ) { + + // Shift arguments if data argument was omitted + if ( isFunction( data ) ) { + type = type || callback; + callback = data; + data = undefined; + } + + // The url can be an options object (which then must have .url) + return jQuery.ajax( jQuery.extend( { + url: url, + type: method, + dataType: type, + data: data, + success: callback + }, jQuery.isPlainObject( url ) && url ) ); + }; +} ); + +jQuery.ajaxPrefilter( function( s ) { + var i; + for ( i in s.headers ) { + if ( i.toLowerCase() === "content-type" ) { + s.contentType = s.headers[ i ] || ""; + } + } +} ); + + +jQuery._evalUrl = function( url, options, doc ) { + return jQuery.ajax( { + url: url, + + // Make this explicit, since user can override this through ajaxSetup (#11264) + type: "GET", + dataType: "script", + cache: true, + async: false, + global: false, + + // Only evaluate the response if it is successful (gh-4126) + // dataFilter is not invoked for failure responses, so using it instead + // of the default converter is kludgy but it works. + converters: { + "text script": function() {} + }, + dataFilter: function( response ) { + jQuery.globalEval( response, options, doc ); + } + } ); +}; + + +jQuery.fn.extend( { + wrapAll: function( html ) { + var wrap; + + if ( this[ 0 ] ) { + if ( isFunction( html ) ) { + html = html.call( this[ 0 ] ); + } + + // The elements to wrap the target around + wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); + + if ( this[ 0 ].parentNode ) { + wrap.insertBefore( this[ 0 ] ); + } + + wrap.map( function() { + var elem = this; + + while ( elem.firstElementChild ) { + elem = elem.firstElementChild; + } + + return elem; + } ).append( this ); + } + + return this; + }, + + wrapInner: function( html ) { + if ( isFunction( html ) ) { + return this.each( function( i ) { + jQuery( this ).wrapInner( html.call( this, i ) ); + } ); + } + + return this.each( function() { + var self = jQuery( this ), + contents = self.contents(); + + if ( contents.length ) { + contents.wrapAll( html ); + + } else { + self.append( html ); + } + } ); + }, + + wrap: function( html ) { + var htmlIsFunction = isFunction( html ); + + return this.each( function( i ) { + jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); + } ); + }, + + unwrap: function( selector ) { + this.parent( selector ).not( "body" ).each( function() { + jQuery( this ).replaceWith( this.childNodes ); + } ); + return this; + } +} ); + + +jQuery.expr.pseudos.hidden = function( elem ) { + return !jQuery.expr.pseudos.visible( elem ); +}; +jQuery.expr.pseudos.visible = function( elem ) { + return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); +}; + + + + +jQuery.ajaxSettings.xhr = function() { + try { + return new window.XMLHttpRequest(); + } catch ( e ) {} +}; + +var xhrSuccessStatus = { + + // File protocol always yields status code 0, assume 200 + 0: 200, + + // Support: IE <=9 only + // #1450: sometimes IE returns 1223 when it should be 204 + 1223: 204 + }, + xhrSupported = jQuery.ajaxSettings.xhr(); + +support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); +support.ajax = xhrSupported = !!xhrSupported; + +jQuery.ajaxTransport( function( options ) { + var callback, errorCallback; + + // Cross domain only allowed if supported through XMLHttpRequest + if ( support.cors || xhrSupported && !options.crossDomain ) { + return { + send: function( headers, complete ) { + var i, + xhr = options.xhr(); + + xhr.open( + options.type, + options.url, + options.async, + options.username, + options.password + ); + + // Apply custom fields if provided + if ( options.xhrFields ) { + for ( i in options.xhrFields ) { + xhr[ i ] = options.xhrFields[ i ]; + } + } + + // Override mime type if needed + if ( options.mimeType && xhr.overrideMimeType ) { + xhr.overrideMimeType( options.mimeType ); + } + + // X-Requested-With header + // For cross-domain requests, seeing as conditions for a preflight are + // akin to a jigsaw puzzle, we simply never set it to be sure. + // (it can always be set on a per-request basis or even using ajaxSetup) + // For same-domain requests, won't change header if already provided. + if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { + headers[ "X-Requested-With" ] = "XMLHttpRequest"; + } + + // Set headers + for ( i in headers ) { + xhr.setRequestHeader( i, headers[ i ] ); + } + + // Callback + callback = function( type ) { + return function() { + if ( callback ) { + callback = errorCallback = xhr.onload = + xhr.onerror = xhr.onabort = xhr.ontimeout = + xhr.onreadystatechange = null; + + if ( type === "abort" ) { + xhr.abort(); + } else if ( type === "error" ) { + + // Support: IE <=9 only + // On a manual native abort, IE9 throws + // errors on any property access that is not readyState + if ( typeof xhr.status !== "number" ) { + complete( 0, "error" ); + } else { + complete( + + // File: protocol always yields status 0; see #8605, #14207 + xhr.status, + xhr.statusText + ); + } + } else { + complete( + xhrSuccessStatus[ xhr.status ] || xhr.status, + xhr.statusText, + + // Support: IE <=9 only + // IE9 has no XHR2 but throws on binary (trac-11426) + // For XHR2 non-text, let the caller handle it (gh-2498) + ( xhr.responseType || "text" ) !== "text" || + typeof xhr.responseText !== "string" ? + { binary: xhr.response } : + { text: xhr.responseText }, + xhr.getAllResponseHeaders() + ); + } + } + }; + }; + + // Listen to events + xhr.onload = callback(); + errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" ); + + // Support: IE 9 only + // Use onreadystatechange to replace onabort + // to handle uncaught aborts + if ( xhr.onabort !== undefined ) { + xhr.onabort = errorCallback; + } else { + xhr.onreadystatechange = function() { + + // Check readyState before timeout as it changes + if ( xhr.readyState === 4 ) { + + // Allow onerror to be called first, + // but that will not handle a native abort + // Also, save errorCallback to a variable + // as xhr.onerror cannot be accessed + window.setTimeout( function() { + if ( callback ) { + errorCallback(); + } + } ); + } + }; + } + + // Create the abort callback + callback = callback( "abort" ); + + try { + + // Do send the request (this may raise an exception) + xhr.send( options.hasContent && options.data || null ); + } catch ( e ) { + + // #14683: Only rethrow if this hasn't been notified as an error yet + if ( callback ) { + throw e; + } + } + }, + + abort: function() { + if ( callback ) { + callback(); + } + } + }; + } +} ); + + + + +// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) +jQuery.ajaxPrefilter( function( s ) { + if ( s.crossDomain ) { + s.contents.script = false; + } +} ); + +// Install script dataType +jQuery.ajaxSetup( { + accepts: { + script: "text/javascript, application/javascript, " + + "application/ecmascript, application/x-ecmascript" + }, + contents: { + script: /\b(?:java|ecma)script\b/ + }, + converters: { + "text script": function( text ) { + jQuery.globalEval( text ); + return text; + } + } +} ); + +// Handle cache's special case and crossDomain +jQuery.ajaxPrefilter( "script", function( s ) { + if ( s.cache === undefined ) { + s.cache = false; + } + if ( s.crossDomain ) { + s.type = "GET"; + } +} ); + +// Bind script tag hack transport +jQuery.ajaxTransport( "script", function( s ) { + + // This transport only deals with cross domain or forced-by-attrs requests + if ( s.crossDomain || s.scriptAttrs ) { + var script, callback; + return { + send: function( _, complete ) { + script = jQuery( " - - + + + - - + NEWS.md +
+

Changelog

+ Source: NEWS.md
-
-

-wordpredictor 0.0.2 Unreleased -

-
-

-Bug fixes

-
    -
  • Fixed small bugs that were causing problems with GitHub actions and CRAN checks.
    • +
    +

    wordpredictor 0.0.3

    +
    +

    Bug fixes

    +
    • Disabled caching in R Markdown files, because it was causing problems with CRAN checks.
      • +
    +
    +
    +

    wordpredictor 0.0.2

    CRAN release: 2021-06-19

    +
    +

    Bug fixes

    +
    • Fixed small bugs that were causing problems with GitHub actions and CRAN checks.
    • Removed custom .Rprofile file as it was causing problems with GitHub actions.
    • Updated sample code in features.Rmd vignette so it does not cause issues with R CMD Check on MacOs.
    • Removed inst/extdata folder from .gitignore since it was causing problems with check-standard workflow on GitHub.
    • Removed non-standard characters from example in data-cleaner.R file as they were causing problems with CRAN check on “Debian Linux, R-devel, clang”.
      • -
    • Issues related to the bug fixes: #318, #319, #320 +
    • Issues related to the bug fixes: #318, #319, #320
      • -
    -
    -
    -
    -

    -wordpredictor 0.0.1 2021-06-14 -

    -
      -
    • Initial Release.
      • -
    -
    -
- - - +
+
+

wordpredictor 0.0.1

CRAN release: 2021-06-14

+
  • Initial Release.
    • +
+
-
-
-

Developed by Nadir Latif.

+
+

Developed by Nadir Latif.

-
-

Site built with pkgdown 1.6.1.

+
+

Site built with pkgdown 2.0.1.

-
-
+
+ - - - + diff --git a/docs/pkgdown.css b/docs/pkgdown.css index 1273238..80ea5b8 100644 --- a/docs/pkgdown.css +++ b/docs/pkgdown.css @@ -56,8 +56,10 @@ img.icon { float: right; } -img { +/* Ensure in-page images don't run outside their container */ +.contents img { max-width: 100%; + height: auto; } /* Fix bug in bootstrap (only seen in firefox) */ @@ -78,11 +80,10 @@ dd { /* Section anchors ---------------------------------*/ a.anchor { - margin-left: -30px; - display:inline-block; - width: 30px; - height: 30px; - visibility: hidden; + display: none; + margin-left: 5px; + width: 20px; + height: 20px; background-image: url(./link.svg); background-repeat: no-repeat; @@ -90,17 +91,15 @@ a.anchor { background-position: center center; } -.hasAnchor:hover a.anchor { - visibility: visible; -} - -@media (max-width: 767px) { - .hasAnchor:hover a.anchor { - visibility: hidden; - } +h1:hover .anchor, +h2:hover .anchor, +h3:hover .anchor, +h4:hover .anchor, +h5:hover .anchor, +h6:hover .anchor { + display: inline-block; } - /* Fixes for fixed navbar --------------------------*/ .contents h1, .contents h2, .contents h3, .contents h4 { @@ -264,31 +263,26 @@ table { /* Syntax highlighting ---------------------------------------------------- */ -pre { - word-wrap: normal; - word-break: normal; - border: 1px solid #eee; -} - -pre, code { +pre, code, pre code { background-color: #f8f8f8; color: #333; } +pre, pre code { + white-space: pre-wrap; + word-break: break-all; + overflow-wrap: break-word; +} -pre code { - overflow: auto; - word-wrap: normal; - white-space: pre; +pre { + border: 1px solid #eee; } -pre .img { +pre .img, pre .r-plt { margin: 5px 0; } -pre .img img { +pre .img img, pre .r-plt img { background-color: #fff; - display: block; - height: auto; } code a, pre a { @@ -305,9 +299,8 @@ a.sourceLine:hover { .kw {color: #264D66;} /* keyword */ .co {color: #888888;} /* comment */ -.message { color: black; font-weight: bolder;} -.error { color: orange; font-weight: bolder;} -.warning { color: #6A0366; font-weight: bolder;} +.error {font-weight: bolder;} +.warning {font-weight: bolder;} /* Clipboard --------------------------*/ @@ -365,3 +358,27 @@ mark { content: ""; } } + +/* Section anchors --------------------------------- + Added in pandoc 2.11: https://github.com/jgm/pandoc-templates/commit/9904bf71 +*/ + +div.csl-bib-body { } +div.csl-entry { + clear: both; +} +.hanging-indent div.csl-entry { + margin-left:2em; + text-indent:-2em; +} +div.csl-left-margin { + min-width:2em; + float:left; +} +div.csl-right-inline { + margin-left:2em; + padding-left:1em; +} +div.csl-indent { + margin-left: 2em; +} diff --git a/docs/pkgdown.js b/docs/pkgdown.js index 7e7048f..52afcee 100644 --- a/docs/pkgdown.js +++ b/docs/pkgdown.js @@ -2,70 +2,28 @@ (function($) { $(function() { - $('.navbar-fixed-top').headroom(); + $('nav.navbar').headroom(); - $('body').css('padding-top', $('.navbar').height() + 10); - $(window).resize(function(){ - $('body').css('padding-top', $('.navbar').height() + 10); + Toc.init({ + $nav: $("#toc"), + $scope: $("main h2, main h3, main h4, main h5, main h6") }); - $('[data-toggle="tooltip"]').tooltip(); - - var cur_path = paths(location.pathname); - var links = $("#navbar ul li a"); - var max_length = -1; - var pos = -1; - for (var i = 0; i < links.length; i++) { - if (links[i].getAttribute("href") === "#") - continue; - // Ignore external links - if (links[i].host !== location.host) - continue; - - var nav_path = paths(links[i].pathname); - - var length = prefix_length(nav_path, cur_path); - if (length > max_length) { - max_length = length; - pos = i; - } - } - - // Add class to parent
  • , and enclosing
  • if in dropdown - if (pos >= 0) { - var menu_anchor = $(links[pos]); - menu_anchor.parent().addClass("active"); - menu_anchor.closest("li.dropdown").addClass("active"); - } - }); - - function paths(pathname) { - var pieces = pathname.split("/"); - pieces.shift(); // always starts with / - - var end = pieces[pieces.length - 1]; - if (end === "index.html" || end === "") - pieces.pop(); - return(pieces); - } - - // Returns -1 if not found - function prefix_length(needle, haystack) { - if (needle.length > haystack.length) - return(-1); - - // Special case for length-0 haystack, since for loop won't run - if (haystack.length === 0) { - return(needle.length === 0 ? 0 : -1); - } + $('body').scrollspy({ + target: '#toc', + offset: 56 // headroom height + }); - for (var i = 0; i < haystack.length; i++) { - if (needle[i] != haystack[i]) - return(i); - } + // Activate popovers + $('[data-bs-toggle="popover"]').popover({ + container: 'body', + html: true, + trigger: 'focus', + placement: "top", + sanitize: false, + }); - return(haystack.length); - } + $('[data-bs-toggle="tooltip"]').tooltip(); /* Clipboard --------------------------*/ @@ -78,9 +36,9 @@ if(ClipboardJS.isSupported()) { $(document).ready(function() { - var copyButton = ""; + var copyButton = ""; - $(".examples, div.sourceCode").addClass("hasCopyButton"); + $("div.sourceCode").addClass("hasCopyButton"); // Insert copy buttons: $(copyButton).prependTo(".hasCopyButton"); @@ -89,20 +47,108 @@ $('.btn-copy-ex').tooltip({container: 'body'}); // Initialize clipboard: - var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { + var clipboard = new ClipboardJS('[data-clipboard-copy]', { text: function(trigger) { - return trigger.parentNode.textContent; + return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, ""); } }); - clipboardBtnCopies.on('success', function(e) { + clipboard.on('success', function(e) { changeTooltipMessage(e.trigger, 'Copied!'); e.clearSelection(); }); - clipboardBtnCopies.on('error', function() { + clipboard.on('error', function() { changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); }); + }); } + + /* Search marking --------------------------*/ + var url = new URL(window.location.href); + var toMark = url.searchParams.get("q"); + var mark = new Mark("div.col-md-9"); + if (toMark) { + mark.mark(toMark, { + accuracy: { + value: "complementary", + limiters: [",", ".", ":", "/"], + } + }); + } + + /* Search --------------------------*/ + /* Adapted from https://github.com/rstudio/bookdown/blob/2d692ba4b61f1e466c92e78fd712b0ab08c11d31/inst/resources/bs4_book/bs4_book.js#L25 */ + // Initialise search index on focus + var fuse; + $("#search-input").focus(async function(e) { + if (fuse) { + return; + } + + $(e.target).addClass("loading"); + var response = await fetch($("#search-input").data("search-index")); + var data = await response.json(); + + var options = { + keys: ["what", "text", "code"], + ignoreLocation: true, + threshold: 0.1, + includeMatches: true, + includeScore: true, + }; + fuse = new Fuse(data, options); + + $(e.target).removeClass("loading"); + }); + + // Use algolia autocomplete + var options = { + autoselect: true, + debug: true, + hint: false, + minLength: 2, + }; + var q; +async function searchFuse(query, callback) { + await fuse; + + var items; + if (!fuse) { + items = []; + } else { + q = query; + var results = fuse.search(query, { limit: 20 }); + items = results + .filter((x) => x.score <= 0.75) + .map((x) => x.item); + if (items.length === 0) { + items = [{dir:"Sorry 😿",previous_headings:"",title:"No results found.",what:"No results found.",path:window.location.href}]; + } + } + callback(items); +} + $("#search-input").autocomplete(options, [ + { + name: "content", + source: searchFuse, + templates: { + suggestion: (s) => { + if (s.title == s.what) { + return `${s.dir} >
    ${s.title}
    `; + } else if (s.previous_headings == "") { + return `${s.dir} >
    ${s.title}
    > ${s.what}`; + } else { + return `${s.dir} >
    ${s.title}
    > ${s.previous_headings} > ${s.what}`; + } + }, + }, + }, + ]).on('autocomplete:selected', function(event, s) { + window.location.href = s.path + "?q=" + q + "#" + s.id; + }); + }); })(window.jQuery || window.$) + + diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 84416b2..32745f5 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -1,10 +1,10 @@ -pandoc: 2.3.1 -pkgdown: 1.6.1 +pandoc: 2.14.0.3 +pkgdown: 2.0.1 pkgdown_sha: ~ articles: features: features.html overview: overview.html -last_built: 2021-06-18T13:35Z +last_built: 2022-01-03T10:52Z urls: reference: https://pakjiddat.github.io/word-predictor/reference article: https://pakjiddat.github.io/word-predictor/articles diff --git a/docs/reference/Base.html b/docs/reference/Base.html index e2a4d73..d6dbcd2 100644 --- a/docs/reference/Base.html +++ b/docs/reference/Base.html @@ -1,146 +1,67 @@ - - - - - - - -Base class for all other classes — Base • Word Predictor - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Base class for all other classes — Base • Word Predictor + Skip to contents + +
    -
    -
    -

    Base class for all other classes

    - Source: R/base.R - +
    +

    Base class for all other classes

    + Source: R/base.R +
    Base.Rd
    -
    +

    Provides a basic structure for processing text files. Also provides methods for reading and writing files and objects.

    - -

    Details

    - +
    +

    Details

    It provides pre-processing, processing and post-processing methods, which need to be overridden by derived classes.

    The pre-processing function is called before reading a file. The process @@ -148,70 +69,79 @@

    Details processing function is called on the processed data.

    Also provides methods for reading and writing text files and R objects. All class methods are private.

    -

    Methods

    - +
    +
    +

    Methods

    -

    Public methods

    - - -

    -

    Method new()

    +
    +

    Public methods

    + +

    +

    Method new()

    It initializes the current object. It is used to set the file name -and verbose options.

    Usage

    -

    Base$new(fn = NULL, lc = 100, ve = 2)

    +and verbose options.

    +

    Usage

    +

    Base$new(fn = NULL, lc = 100, ve = 2)

    +
    -

    Arguments

    -

    -
    fn

    The path to the file to clean.

    +
    +

    Arguments

    +

    fn
    +

    The path to the file to clean.

    -
    lc

    The number of lines to read and clean at a time.

    -
    ve

    The level of detail in the information messages. +

    lc
    +

    The number of lines to read and clean at a time.

    + + +
    ve
    +

    The level of detail in the information messages. Reads the given file one line at a time. It runs the given pre-processing function before reading the file. It runs the given

    -

    -

    -

    Method clone()

    -

    The objects of this class are cloneable with this method.

    Usage

    -

    Base$clone(deep = FALSE)

    -

    Arguments

    -

    -
    deep

    Whether to make a deep clone.

    +

    +
    -

    +

    +

    Method clone()

    +

    The objects of this class are cloneable with this method.

    +

    Usage

    +

    Base$clone(deep = FALSE)

    +
    +
    +

    Arguments

    +

    deep
    +

    Whether to make a deep clone.

    -
    - +

    +
    -
    -
    -

    Developed by Nadir Latif.

    +
    + +
    + + +
    +

    Developed by Nadir Latif.

    -
    -

    Site built with pkgdown 1.6.1.

    +
    +

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b/docs/reference/DataAnalyzer.html @@ -1,141 +1,66 @@ - - - - - - - -Analyzes input text files and n-gram token files — DataAnalyzer • Word Predictor - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +the TokenGenerator class.">Analyzes input text files and n-gram token files — DataAnalyzer • Word Predictor + Skip to contents + +
    -
    -
    -

    Analyzes input text files and n-gram token files

    - Source: R/data-analyzer.R - +
    +

    Analyzes input text files and n-gram token files

    + Source: R/data-analyzer.R +
    DataAnalyzer.Rd
    -
    +

    It provides a method that returns information about text files, such as number of lines and number of words. It also provides a method that displays bar plots of n-gram frequencies. Additionally it provides a method for @@ -144,9 +69,8 @@

    Analyzes input text files and n-gram token files

    - -

    Details

    - +
    +

    Details

    It provides a method that returns text file information. The text file information includes total number of lines, max, min and mean line length and file size.

    @@ -154,41 +78,45 @@

    Details n-gram tokens.

    Another method is provided which returns a list of n-grams that match the given regular expression.

    -

    Super class

    - -

    wordpredictor::Base -> DataAnalyzer

    -

    Methods

    - +
    +
    +

    Super class

    +

    wordpredictor::Base -> DataAnalyzer

    +
    +
    +

    Methods

    -

    Public methods

    - - -

    Inherited methods -
      -
    -
    - -
    -

    Method new()

    +
    +

    Public methods

    + +

    Inherited methods

      +

      Method new()

      It initializes the current object. It is used to set the file name -and verbose options.

      Usage

      -

      DataAnalyzer$new(fn = NULL, ve = 0)

      +and verbose options.

      +

      Usage

      +

      DataAnalyzer$new(fn = NULL, ve = 0)

      +
      -

      Arguments

      -

      -
      fn

      The path to the input file.

      +
      +

      Arguments

      +

      fn
      +

      The path to the input file.

      -
      ve

      The level of detail in the information messages.

      -

      -

      -

      Method plot_n_gram_stats()

      +
      ve
      +

      The level of detail in the information messages.

      + + +

      +
      + +

      +

      Method plot_n_gram_stats()

      It allows generating two type of n-gram plots. It first reads n-gram token frequencies from an input text file. The n-gram frequencies are displayed in a bar plot.

      @@ -197,334 +125,366 @@

      Arg displays the top n most occurring tokens along with their frequencies. 'coverage' displays the number of words along with their frequencies.

      -

      The plot stats are returned as a data frame.

      Usage

      -

      DataAnalyzer$plot_n_gram_stats(opts)

      - -

      Arguments

      -

      -
      opts

      The options for analyzing the data. -

        -

      • type. The type of plot to display. The options are: +

        The plot stats are returned as a data frame.

        +

        Usage

        +

        DataAnalyzer$plot_n_gram_stats(opts)

        +
        + +
        +

        Arguments

        +

        opts
        +

        The options for analyzing the data.

        • type. The type of plot to display. The options are: 'top_features', 'coverage'.

        • n. For 'top_features', it is the number of top most occurring tokens. For 'coverage' it is the first n frequencies.

        • save_to. The graphics devices to save the plot to. NULL implies plot is printed.

        • dir. The output directory where the plot will be saved.

          • -

        +
      -

      -

      Returns

      + +

      +
      +
      +

      Returns

      A data frame containing the stats.

      -

      Examples

      -

      # Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL value implies tempdir will
-# be used.
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("n2.RDS")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-# End of environment setup code
-
-# The n-gram file name
-nfn <- paste0(ed, "/n2.RDS")
-# The DataAnalyzer object is created
-da <- DataAnalyzer$new(nfn, ve = ve)
-# The top features plot is checked
-df <- da$plot_n_gram_stats(opts = list(
-    "type" = "top_features",
-    "n" = 10,
-    "save_to" = NULL,
-    "dir" = ed
-))
-# N-gram statistics are displayed
-print(df)
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-

      - -

      -

      Method get_file_info()

      +
      +
      +

      Examples

      +

      # Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL value implies tempdir will
+# be used.
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("n2.RDS")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The n-gram file name
+nfn <- paste0(ed, "/n2.RDS")
+# The DataAnalyzer object is created
+da <- DataAnalyzer$new(nfn, ve = ve)
+# The top features plot is checked
+df <- da$plot_n_gram_stats(opts = list(
+    "type" = "top_features",
+    "n" = 10,
+    "save_to" = NULL,
+    "dir" = ed
+))
+# N-gram statistics are displayed
+print(df)
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()

      +
      + + +

      +

      Method get_file_info()

      It generates information about text files. It takes as input a file or a directory containing text files. For each file it calculates the total number of lines, maximum, minimum and mean line lengths and the -total file size. The file information is returned as a data frame.

      Usage

      -

      DataAnalyzer$get_file_info(res)

      +total file size. The file information is returned as a data frame.

      +

      Usage

      +

      DataAnalyzer$get_file_info(res)

      +
      + +
      +

      Arguments

      +

      res
      +

      The name of a directory or a file name.

      -

      Arguments

      -

      -
      res

      The name of a directory or a file name.

      -

      -

      Returns

      +

      +
      +
      +

      Returns

      A data frame containing the text file statistics.

      -

      Examples

      -

      # Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("test.txt")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-# End of environment setup code
-
-# The test file name
-cfn <- paste0(ed, "/test.txt")
-# The DataAnalyzer object is created
-da <- DataAnalyzer$new(ve = ve)
-# The file info is fetched
-fi <- da$get_file_info(cfn)
-# The file information is printed
-print(fi)
-
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-

      - -

      -

      Method get_ngrams()

      +
      +
      +

      Examples

      +

      # Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("test.txt")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The test file name
+cfn <- paste0(ed, "/test.txt")
+# The DataAnalyzer object is created
+da <- DataAnalyzer$new(ve = ve)
+# The file info is fetched
+fi <- da$get_file_info(cfn)
+# The file information is printed
+print(fi)
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()

      +
      + + +

      +

      Method get_ngrams()

      It extracts a given number of n-grams and their frequencies from a n-gram token file.

      The prefix parameter specifies the regular expression for matching n-grams. If this parameter is not specified then the given number of -n-grams are randomly chosen.

      Usage

      -

      DataAnalyzer$get_ngrams(fn, c = NULL, pre = NULL)

      - -

      Arguments

      -

      -
      fn

      The n-gram file name.

      - -
      c

      The number of n-grams to return.

      - -
      pre

      The n-gram prefix, given as a regular expression.

      - -

      -

      Examples

      -

      # Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("n2.RDS")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-# End of environment setup code
-
-# The n-gram file name
-nfn <- paste0(ed, "/n2.RDS")
-# The DataAnalyzer object is created
-da <- DataAnalyzer$new(nfn, ve = ve)
-# Bi-grams starting with "and_" are returned
-df <- da$get_ngrams(fn = nfn, c = 10, pre = "^and_*")
-# The data frame is sorted by frequency
-df <- df[order(df$freq, decreasing = TRUE),]
-# The data frame is printed
-print(df)
-
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-

      - -

      -

      Method clone()

      -

      The objects of this class are cloneable with this method.

      Usage

      -

      DataAnalyzer$clone(deep = FALSE)

      - -

      Arguments

      -

      -
      deep

      Whether to make a deep clone.

      - -

      - - - -

      Examples

      - 
      
-## ------------------------------------------------
-## Method `DataAnalyzer$plot_n_gram_stats`
-## ------------------------------------------------
-
-# Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL value implies tempdir will
-# be used.
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("n2.RDS")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-
      #> [1] "CITATION" "examples" "extdata" 
      # End of environment setup code
-
-# The n-gram file name
-nfn <- paste0(ed, "/n2.RDS")
-# The DataAnalyzer object is created
-da <- DataAnalyzer$new(nfn, ve = ve)
-# The top features plot is checked
-df <- da$plot_n_gram_stats(opts = list(
-    "type" = "top_features",
-    "n" = 10,
-    "save_to" = NULL,
-    "dir" = ed
-))
-
      # N-gram statistics are displayed
-print(df)
-
      #> # A tibble: 10 x 2
-#>    pre          freq
-#>    <chr>       <dbl>
-#>  1 in_the          4
-#>  2 of_the          4
-#>  3 on_the          4
-#>  4 to_the          4
-#>  5 from_the        3
-#>  6 in_a            3
-#>  7 a_thin          2
-#>  8 and_the         2
-#>  9 between_the     2
-#> 10 down_the        2
      # The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-
-## ------------------------------------------------
-## Method `DataAnalyzer$get_file_info`
-## ------------------------------------------------
-
-# Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("test.txt")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-
      #> [1] "CITATION" "examples" "extdata" 
      # End of environment setup code
-
-# The test file name
-cfn <- paste0(ed, "/test.txt")
-# The DataAnalyzer object is created
-da <- DataAnalyzer$new(ve = ve)
-# The file info is fetched
-fi <- da$get_file_info(cfn)
-# The file information is printed
-print(fi)
-
      #> $file_stats
-#>                         fn total_lc max_ll min_ll mean_ll size
-#> 1 /tmp/Rtmpgcy76G/test.txt       73     51     28      41 3 Kb
-#> 
-#> $overall_stats
-#>   total_lc max_ll min_ll mean_ll total_s
-#> 1       73     51     28      41    3 Kb
-#> 
      
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-
-## ------------------------------------------------
-## Method `DataAnalyzer$get_ngrams`
-## ------------------------------------------------
-
-# Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("n2.RDS")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-
      #> [1] "CITATION" "examples" "extdata" 
      # End of environment setup code
-
-# The n-gram file name
-nfn <- paste0(ed, "/n2.RDS")
-# The DataAnalyzer object is created
-da <- DataAnalyzer$new(nfn, ve = ve)
-# Bi-grams starting with "and_" are returned
-df <- da$get_ngrams(fn = nfn, c = 10, pre = "^and_*")
-# The data frame is sorted by frequency
-df <- df[order(df$freq, decreasing = TRUE),]
-# The data frame is printed
-print(df)
-
      #> # A tibble: 15 x 2
-#>    pre         freq
-#>    <fct>      <dbl>
-#>  1 and_the        2
-#>  2 and_cart       1
-#>  3 and_fired      1
-#>  4 and_forget     1
-#>  5 and_leave      1
-#>  6 and_open       1
-#>  7 and_out        1
-#>  8 and_phrase     1
-#>  9 and_say        1
-#> 10 and_tea        1
-#> 11 and_tell       1
-#> 12 and_then       1
-#> 13 and_threw      1
-#> 14 and_was        1
-#> 15 and_watch      1
      
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-
      -
      - +n-grams are randomly chosen.

      +

      Usage

      +

      DataAnalyzer$get_ngrams(fn, c = NULL, pre = NULL)

      +
      +

      Arguments

      +

      fn
      +

      The n-gram file name.

      + + +
      c
      +

      The number of n-grams to return.

      -
      -
      -

      Developed by Nadir Latif.

      + +
      pre
      +

      The n-gram prefix, given as a regular expression.

      + + +

      +
      +

      Examples

      +

      # Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("n2.RDS")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The n-gram file name
+nfn <- paste0(ed, "/n2.RDS")
+# The DataAnalyzer object is created
+da <- DataAnalyzer$new(nfn, ve = ve)
+# Bi-grams starting with "and_" are returned
+df <- da$get_ngrams(fn = nfn, c = 10, pre = "^and_*")
+# The data frame is sorted by frequency
+df <- df[order(df$freq, decreasing = TRUE),]
+# The data frame is printed
+print(df)
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()

      +
      + -
      -

      Site built with pkgdown 1.6.1.

      +

      +

      Method clone()

      +

      The objects of this class are cloneable with this method.

      +

      Usage

      +

      DataAnalyzer$clone(deep = FALSE)

      - -
      +
      +

      Arguments

      +

      deep
      +

      Whether to make a deep clone.

      - + +

      +
      + +
      + +
      + +
      +

      Examples

      + 
      
+## ------------------------------------------------
+## Method `DataAnalyzer$plot_n_gram_stats`
+## ------------------------------------------------
+
+# Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL value implies tempdir will
+# be used.
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("n2.RDS")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The n-gram file name
+nfn <- paste0(ed, "/n2.RDS")
+# The DataAnalyzer object is created
+da <- DataAnalyzer$new(nfn, ve = ve)
+# The top features plot is checked
+df <- da$plot_n_gram_stats(opts = list(
+    "type" = "top_features",
+    "n" = 10,
+    "save_to" = NULL,
+    "dir" = ed
+))
+
+# N-gram statistics are displayed
+print(df)
+#> # A tibble: 10 × 2
+#>    pre          freq
+#>    <chr>       <dbl>
+#>  1 in_the          4
+#>  2 of_the          4
+#>  3 on_the          4
+#>  4 to_the          4
+#>  5 from_the        3
+#>  6 in_a            3
+#>  7 a_thin          2
+#>  8 and_the         2
+#>  9 between_the     2
+#> 10 down_the        2
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()
+
+## ------------------------------------------------
+## Method `DataAnalyzer$get_file_info`
+## ------------------------------------------------
+
+# Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("test.txt")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The test file name
+cfn <- paste0(ed, "/test.txt")
+# The DataAnalyzer object is created
+da <- DataAnalyzer$new(ve = ve)
+# The file info is fetched
+fi <- da$get_file_info(cfn)
+# The file information is printed
+print(fi)
+#> $file_stats
+#>                         fn total_lc max_ll min_ll mean_ll size
+#> 1 /tmp/RtmpcJbhpv/test.txt       73     51     28      41 3 Kb
+#> 
+#> $overall_stats
+#>   total_lc max_ll min_ll mean_ll total_s
+#> 1       73     51     28      41    3 Kb
+#> 
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()
+
+## ------------------------------------------------
+## Method `DataAnalyzer$get_ngrams`
+## ------------------------------------------------
+
+# Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("n2.RDS")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The n-gram file name
+nfn <- paste0(ed, "/n2.RDS")
+# The DataAnalyzer object is created
+da <- DataAnalyzer$new(nfn, ve = ve)
+# Bi-grams starting with "and_" are returned
+df <- da$get_ngrams(fn = nfn, c = 10, pre = "^and_*")
+# The data frame is sorted by frequency
+df <- df[order(df$freq, decreasing = TRUE),]
+# The data frame is printed
+print(df)
+#> # A tibble: 15 × 2
+#>    pre         freq
+#>    <fct>      <dbl>
+#>  1 and_the        2
+#>  2 and_cart       1
+#>  3 and_fired      1
+#>  4 and_forget     1
+#>  5 and_leave      1
+#>  6 and_open       1
+#>  7 and_out        1
+#>  8 and_phrase     1
+#>  9 and_say        1
+#> 10 and_tea        1
+#> 11 and_tell       1
+#> 12 and_then       1
+#> 13 and_threw      1
+#> 14 and_was        1
+#> 15 and_watch      1
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()
+
      +
      +
      - - +
      +

      Developed by Nadir Latif.

      +
      + +
      +

      Site built with pkgdown 2.0.1.

      +
      + +
      + + + + + diff --git a/docs/reference/DataCleaner.html b/docs/reference/DataCleaner.html index 7898ad8..fb67b23 100644 --- a/docs/reference/DataCleaner.html +++ b/docs/reference/DataCleaner.html @@ -1,182 +1,102 @@ - - - - - - - -Provides data cleaning functionality — DataCleaner • Word Predictor - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Provides data cleaning functionality — DataCleaner • Word Predictor + Skip to contents + +
      -
      -
      -

      Provides data cleaning functionality

      - Source: R/data-cleaner.R - +
      +

      Provides data cleaning functionality

      + Source: R/data-cleaner.R +
      DataCleaner.Rd
      -
      +

      It provides a memory efficient method for removing unneeded characters from text files. It is suitable for cleaning large text files.

      - -

      Details

      - +
      +

      Details

      It provides a method for cleaning text files. It allows removing bad words, stop words, non dictionary words, extra space, punctuation and non-alphabet characters. It also allows conversion to lower case. It supports large text files.

      -

      Super class

      - -

      wordpredictor::Base -> DataCleaner

      -

      Methods

      - +
      +
      +

      Super class

      +

      wordpredictor::Base -> DataCleaner

      +
      +
      +

      Methods

      -

      Public methods

      - - -

      Inherited methods -
        -
      -
      - -
      -

      Method new()

      +
      +

      Public methods

      + +

      Inherited methods

        +

        Method new()

        It initializes the current object. It is used to set the file name -and verbose options.

        Usage

        -

        DataCleaner$new(fn = NULL, opts = list(), ve = 0)

        +and verbose options.

        +

        Usage

        +

        DataCleaner$new(fn = NULL, opts = list(), ve = 0)

        +
        -

        Arguments

        -

        -
        fn

        The path to the file to clean.

        +
        +

        Arguments

        +

        fn
        +

        The path to the file to clean.

        -
        opts

        The options for data cleaning. -

          -

        • min_words. The minimum number of words per sentence.

          • + +
          opts
          +

          The options for data cleaning.

          • min_words. The minimum number of words per sentence.

          • line_count. The number of lines to read and clean at a time.

          • save_data. If the combined processed lines should be saved.

          • output_file. Name of the output file used to store the data.

            • @@ -191,13 +111,18 @@

            Arg

          • remove_extra_space. -> If leading, trailing and double spaces should be removed.

          • remove_bad. If bad words should be removed

            • -

          +
        + + +
        ve
        +

        The level of detail in the information messages.

        + -
        ve

        The level of detail in the information messages.

        +

        +
        -

        -

        -

        Method clean_file()

        +

        +

        Method clean_file()

        It provides an efficient method for cleaning text files. It removes unneeded characters from the given text file with several options.

        @@ -208,198 +133,213 @@

        Arg file.

        File cleaning progress is displayed if the verbose option was set in the class constructor. It is suitable for cleaning large text -files.

        Usage

        -

        DataCleaner$clean_file()

        - -

        Examples

        -

        # Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("test.txt")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-# End of environment setup code
-
-# The cleaned test file name
-cfn <- paste0(ed, "/test-clean.txt")
-# The test file name
-fn <- paste0(ed, "/test.txt")
-# The data cleaning options
-dc_opts <- list("output_file" = cfn)
-# The data cleaner object is created
-dc <- DataCleaner$new(fn, dc_opts, ve = ve)
-# The sample file is cleaned
-dc$clean_file()
-
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-

        - -

        -

        Method clean_lines()

        +files.

        +

        Usage

        +

        DataCleaner$clean_file()

        +
        + +
        +

        Examples

        +

        # Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("test.txt")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The cleaned test file name
+cfn <- paste0(ed, "/test-clean.txt")
+# The test file name
+fn <- paste0(ed, "/test.txt")
+# The data cleaning options
+dc_opts <- list("output_file" = cfn)
+# The data cleaner object is created
+dc <- DataCleaner$new(fn, dc_opts, ve = ve)
+# The sample file is cleaned
+dc$clean_file()
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()

        +
        + + +

        +

        Method clean_lines()

        It cleans the given lines of text using the options -passed to the current object.

        Usage

        -

        DataCleaner$clean_lines(lines)

        +passed to the current object.

        +

        Usage

        +

        DataCleaner$clean_lines(lines)

        +
        -

        Arguments

        -

        -
        lines

        The input sentences.

        +
        +

        Arguments

        +

        lines
        +

        The input sentences.

        -

        -

        Returns

        + +

        +
        +
        +

        Returns

        The cleaned lines of text.

        -

        Examples

        -

        # The level of detail in the information messages
-ve <- 0
-# Test data is read
-l <- c(
-    "If you think I'm wrong, send me a link to where it's happened",
-    "We're about 90percent done with this room",
-    "This isn't how I wanted it between us.",
-    "Almost any cute breed can become ornamental",
-    "Once upon a time there was a kingdom with a castle",
-    "That's not a thing any of us are granted'",
-    "Why are you being so difficult? she asks."
-)
-# The expected results
-res <- c(
-    "if you think wrong send me a link to where its happened",
-    "were about percent done with this room",
-    "this how i wanted it between us",
-    "almost any cute breed can become ornamental",
-    "once upon a time there was a kingdom with a castle",
-    "thats not a thing any of us are granted",
-    "why are you being so difficult she asks"
-)
-# The DataCleaner object is created
-dc <- DataCleaner$new(ve = ve)
-# The line is cleaned
-cl <- dc$clean_lines(l)
-# The cleaned lines are printed
-print(cl)
-

        - -

        -

        Method clone()

        -

        The objects of this class are cloneable with this method.

        Usage

        -

        DataCleaner$clone(deep = FALSE)

        - -

        Arguments

        -

        -
        deep

        Whether to make a deep clone.

        - -

        - - - -

        Examples

        - 
        
-## ------------------------------------------------
-## Method `DataCleaner$clean_file`
-## ------------------------------------------------
-
-# Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("test.txt")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-
        #> [1] "CITATION" "examples" "extdata" 
        # End of environment setup code
-
-# The cleaned test file name
-cfn <- paste0(ed, "/test-clean.txt")
-# The test file name
-fn <- paste0(ed, "/test.txt")
-# The data cleaning options
-dc_opts <- list("output_file" = cfn)
-# The data cleaner object is created
-dc <- DataCleaner$new(fn, dc_opts, ve = ve)
-# The sample file is cleaned
-dc$clean_file()
-
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-
-## ------------------------------------------------
-## Method `DataCleaner$clean_lines`
-## ------------------------------------------------
-
-# The level of detail in the information messages
-ve <- 0
-# Test data is read
-l <- c(
-    "If you think I'm wrong, send me a link to where it's happened",
-    "We're about 90percent done with this room",
-    "This isn't how I wanted it between us.",
-    "Almost any cute breed can become ornamental",
-    "Once upon a time there was a kingdom with a castle",
-    "That's not a thing any of us are granted'",
-    "Why are you being so difficult? she asks."
-)
-# The expected results
-res <- c(
-    "if you think wrong send me a link to where its happened",
-    "were about percent done with this room",
-    "this how i wanted it between us",
-    "almost any cute breed can become ornamental",
-    "once upon a time there was a kingdom with a castle",
-    "thats not a thing any of us are granted",
-    "why are you being so difficult she asks"
-)
-# The DataCleaner object is created
-dc <- DataCleaner$new(ve = ve)
-# The line is cleaned
-cl <- dc$clean_lines(l)
-# The cleaned lines are printed
-print(cl)
-
        #> [1] "if you think wrong send me a link to where its happened"
-#> [2] "were about percent done with this room"                 
-#> [3] "this how i wanted it between us"                        
-#> [4] "almost any cute breed can become ornamental"            
-#> [5] "once upon a time there was a kingdom with a castle"     
-#> [6] "thats not a thing any of us are granted"                
-#> [7] "why are you being so difficult she asks"
        -
        - +
        +
        +

        Examples

        +

        # The level of detail in the information messages
+ve <- 0
+# Test data is read
+l <- c(
+    "If you think I'm wrong, send me a link to where it's happened",
+    "We're about 90percent done with this room",
+    "This isn't how I wanted it between us.",
+    "Almost any cute breed can become ornamental",
+    "Once upon a time there was a kingdom with a castle",
+    "That's not a thing any of us are granted'",
+    "Why are you being so difficult? she asks."
+)
+# The expected results
+res <- c(
+    "if you think wrong send me a link to where its happened",
+    "were about percent done with this room",
+    "this how i wanted it between us",
+    "almost any cute breed can become ornamental",
+    "once upon a time there was a kingdom with a castle",
+    "thats not a thing any of us are granted",
+    "why are you being so difficult she asks"
+)
+# The DataCleaner object is created
+dc <- DataCleaner$new(ve = ve)
+# The line is cleaned
+cl <- dc$clean_lines(l)
+# The cleaned lines are printed
+print(cl)

        -
        -
        -

        Developed by Nadir Latif.

        +

        +

        Method clone()

        +

        The objects of this class are cloneable with this method.

        +

        Usage

        +

        DataCleaner$clone(deep = FALSE)

        -
        -

        Site built with pkgdown 1.6.1.

        +
        +

        Arguments

        +

        deep
        +

        Whether to make a deep clone.

        + + +

        -
        -
        +
        + +
        + +
        +

        Examples

        + 
        
+## ------------------------------------------------
+## Method `DataCleaner$clean_file`
+## ------------------------------------------------
+
+# Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("test.txt")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The cleaned test file name
+cfn <- paste0(ed, "/test-clean.txt")
+# The test file name
+fn <- paste0(ed, "/test.txt")
+# The data cleaning options
+dc_opts <- list("output_file" = cfn)
+# The data cleaner object is created
+dc <- DataCleaner$new(fn, dc_opts, ve = ve)
+# The sample file is cleaned
+dc$clean_file()
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()
+
+## ------------------------------------------------
+## Method `DataCleaner$clean_lines`
+## ------------------------------------------------
+
+# The level of detail in the information messages
+ve <- 0
+# Test data is read
+l <- c(
+    "If you think I'm wrong, send me a link to where it's happened",
+    "We're about 90percent done with this room",
+    "This isn't how I wanted it between us.",
+    "Almost any cute breed can become ornamental",
+    "Once upon a time there was a kingdom with a castle",
+    "That's not a thing any of us are granted'",
+    "Why are you being so difficult? she asks."
+)
+# The expected results
+res <- c(
+    "if you think wrong send me a link to where its happened",
+    "were about percent done with this room",
+    "this how i wanted it between us",
+    "almost any cute breed can become ornamental",
+    "once upon a time there was a kingdom with a castle",
+    "thats not a thing any of us are granted",
+    "why are you being so difficult she asks"
+)
+# The DataCleaner object is created
+dc <- DataCleaner$new(ve = ve)
+# The line is cleaned
+cl <- dc$clean_lines(l)
+# The cleaned lines are printed
+print(cl)
+#> [1] "if you think wrong send me a link to where its happened"
+#> [2] "were about percent done with this room"                 
+#> [3] "this how i wanted it between us"                        
+#> [4] "almost any cute breed can become ornamental"            
+#> [5] "once upon a time there was a kingdom with a castle"     
+#> [6] "thats not a thing any of us are granted"                
+#> [7] "why are you being so difficult she asks"                
+
        +
        +
        - +
        +

        Developed by Nadir Latif.

        +
        + +
        +

        Site built with pkgdown 2.0.1.

        +
        - - +
        + + + + + diff --git a/docs/reference/DataSampler.html b/docs/reference/DataSampler.html index 465f41e..d720aba 100644 --- a/docs/reference/DataSampler.html +++ b/docs/reference/DataSampler.html @@ -1,141 +1,66 @@ - - - - - - - -Generates data samples from text files — DataSampler • Word Predictor - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +be cleaned or randomized.">Generates data samples from text files — DataSampler • Word Predictor + Skip to contents + +
        -
        -
        -

        Generates data samples from text files

        - Source: R/data-sampler.R - +
        +

        Generates data samples from text files

        + Source: R/data-sampler.R +
        DataSampler.Rd
        -
        +

        It provides a method for generating training, testing and validation data sets from a given input text file.

        It also provides a method for generating a sample file of given size or @@ -144,269 +69,300 @@

        Generates data samples from text files

        +
        +

        Super class

        +

        wordpredictor::Base -> DataSampler

        +
        +
        +

        Methods

        + +
        +

        Public methods

        + +

        Inherited methods

          +

          Method new()

          +

          It initializes the current object. It is used to set the +verbose option.

          +

          Usage

          +

          DataSampler$new(dir = ".", ve = 0)

          +
          -

          Super class

          +
          +

          Arguments

          +

          dir
          +

          The directory for storing the input and output files.

          -

          wordpredictor::Base -> DataSampler

          -

          Methods

          - -

          Public methods

          - - -

          Inherited methods -
            -
          -
          - -
          -

          Method new()

          -

          It initializes the current object. It is used to set the -verbose option.

          Usage

          -

          DataSampler$new(dir = ".", ve = 0)

          +
          ve
          +

          The level of detail in the information messages.

          -

          Arguments

          -

          -
          dir

          The directory for storing the input and output files.

          -
          ve

          The level of detail in the information messages.

          +

          +
          -

          -

          -

          Method generate_sample()

          +

          +

          Method generate_sample()

          Generates a sample file of given size from the given input file. The file is saved to the directory given by the dir object attribute. Once the file has been generated, its contents may be cleaned or -randomized.

          Usage

          -

          DataSampler$generate_sample(fn, ss, ic, ir, ofn, is, dc_opts = NULL)

          +randomized.

          +

          Usage

          +

          DataSampler$generate_sample(fn, ss, ic, ir, ofn, is, dc_opts = NULL)

          +
          -

          Arguments

          -

          -
          fn

          The input file name. It is the short file name relative to +

          +

          Arguments

          +

          fn
          +

          The input file name. It is the short file name relative to the dir attribute.

          -
          ss

          The number of lines or proportion of lines to sample.

          - -
          ic

          If the sample file should be cleaned.

          - -
          ir

          If the sample file contents should be randomized.

          - -
          ofn

          The output file name. It will be saved to the dir.

          - -
          is

          If the sampled data should be saved to a file.

          - -
          dc_opts

          The options for cleaning the data.

          - -

          -

          Examples

          -

          # Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("input.txt")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-# End of environment setup code
-
-# The sample file name
-sfn <- paste0(ed, "/sample.txt")
-# An object of class DataSampler is created
-ds <- DataSampler$new(dir = ed, ve = ve)
-# The sample file is generated
-ds$generate_sample(
-    fn = "input.txt",
-    ss = 0.5,
-    ic = FALSE,
-    ir = FALSE,
-    ofn = "sample.txt",
-    is = TRUE
-)
-
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-

          - -

          -

          Method generate_data()

          + +
          ss
          +

          The number of lines or proportion of lines to sample.

          + + +
          ic
          +

          If the sample file should be cleaned.

          + + +
          ir
          +

          If the sample file contents should be randomized.

          + + +
          ofn
          +

          The output file name. It will be saved to the dir.

          + + +
          is
          +

          If the sampled data should be saved to a file.

          + + +
          dc_opts
          +

          The options for cleaning the data.

          + + +

          +
          +
          +

          Examples

          +

          # Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("input.txt")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The sample file name
+sfn <- paste0(ed, "/sample.txt")
+# An object of class DataSampler is created
+ds <- DataSampler$new(dir = ed, ve = ve)
+# The sample file is generated
+ds$generate_sample(
+    fn = "input.txt",
+    ss = 0.5,
+    ic = FALSE,
+    ir = FALSE,
+    ofn = "sample.txt",
+    is = TRUE
+)
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()

          +
          + + +

          +

          Method generate_data()

          It generates training, testing and validation data sets from the given input file. It first reads the file given as a parameter to the current object. It partitions the data into training, testing and validation sets, according to the perc parameter. The files are named train.txt, test.txt and va.txt and are -saved to the given output folder.

          Usage

          -

          DataSampler$generate_data(fn, percs)

          +saved to the given output folder.

          +

          Usage

          +

          DataSampler$generate_data(fn, percs)

          +
          -

          Arguments

          -

          -
          fn

          The input file name. It should be relative to the dir +

          +

          Arguments

          +

          fn
          +

          The input file name. It should be relative to the dir attribute.

          -
          percs

          The size of the training, testing and validation sets.

          - -

          -

          Examples

          -

          # Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be
-# used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("input.txt")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve)
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-# End of environment setup code
-
-# The files to clean
-fns <- c("train", "test", "validate")
-# An object of class DataSampler is created
-ds <- DataSampler$new(dir = ed, ve = ve)
-# The train, test and validation files are generated
-ds$generate_data(
-    fn = "input.txt",
-    percs = list(
-        "train" = 0.8,
-        "test" = 0.1,
-        "validate" = 0.1
-    )
-)
-
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-

          - -

          -

          Method clone()

          -

          The objects of this class are cloneable with this method.

          Usage

          -

          DataSampler$clone(deep = FALSE)

          - -

          Arguments

          -

          -
          deep

          Whether to make a deep clone.

          - -

          - - - -

          Examples

          - 
          
-## ------------------------------------------------
-## Method `DataSampler$generate_sample`
-## ------------------------------------------------
-
-# Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("input.txt")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-
          #> [1] "CITATION" "examples" "extdata" 
          # End of environment setup code
-
-# The sample file name
-sfn <- paste0(ed, "/sample.txt")
-# An object of class DataSampler is created
-ds <- DataSampler$new(dir = ed, ve = ve)
-# The sample file is generated
-ds$generate_sample(
-    fn = "input.txt",
-    ss = 0.5,
-    ic = FALSE,
-    ir = FALSE,
-    ofn = "sample.txt",
-    is = TRUE
-)
-
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-
-## ------------------------------------------------
-## Method `DataSampler$generate_data`
-## ------------------------------------------------
-
-# Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be
-# used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("input.txt")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve)
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-
          #> [1] "CITATION" "examples" "extdata" 
          # End of environment setup code
-
-# The files to clean
-fns <- c("train", "test", "validate")
-# An object of class DataSampler is created
-ds <- DataSampler$new(dir = ed, ve = ve)
-# The train, test and validation files are generated
-ds$generate_data(
-    fn = "input.txt",
-    percs = list(
-        "train" = 0.8,
-        "test" = 0.1,
-        "validate" = 0.1
-    )
-)
-
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-
          -
          - + +
          percs
          +

          The size of the training, testing and validation sets.

          + + +

          +
          +
          +

          Examples

          +

          # Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be
+# used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("input.txt")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve)
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The files to clean
+fns <- c("train", "test", "validate")
+# An object of class DataSampler is created
+ds <- DataSampler$new(dir = ed, ve = ve)
+# The train, test and validation files are generated
+ds$generate_data(
+    fn = "input.txt",
+    percs = list(
+        "train" = 0.8,
+        "test" = 0.1,
+        "validate" = 0.1
+    )
+)
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()

          -
          -
          -

          Developed by Nadir Latif.

          +

          +

          Method clone()

          +

          The objects of this class are cloneable with this method.

          +

          Usage

          +

          DataSampler$clone(deep = FALSE)

          -
          -

          Site built with pkgdown 1.6.1.

          +
          +

          Arguments

          +

          deep
          +

          Whether to make a deep clone.

          + + +

          -
          -
          +
          - +
          +
          +

          Examples

          + 
          
+## ------------------------------------------------
+## Method `DataSampler$generate_sample`
+## ------------------------------------------------
+
+# Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("input.txt")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The sample file name
+sfn <- paste0(ed, "/sample.txt")
+# An object of class DataSampler is created
+ds <- DataSampler$new(dir = ed, ve = ve)
+# The sample file is generated
+ds$generate_sample(
+    fn = "input.txt",
+    ss = 0.5,
+    ic = FALSE,
+    ir = FALSE,
+    ofn = "sample.txt",
+    is = TRUE
+)
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()
+
+## ------------------------------------------------
+## Method `DataSampler$generate_data`
+## ------------------------------------------------
+
+# Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be
+# used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("input.txt")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve)
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The files to clean
+fns <- c("train", "test", "validate")
+# An object of class DataSampler is created
+ds <- DataSampler$new(dir = ed, ve = ve)
+# The train, test and validation files are generated
+ds$generate_data(
+    fn = "input.txt",
+    percs = list(
+        "train" = 0.8,
+        "test" = 0.1,
+        "validate" = 0.1
+    )
+)
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()
+
          +
          +
          + + +
          +

          Developed by Nadir Latif.

          +
          + +
          +

          Site built with pkgdown 2.0.1.

          +
          + +
          - - + + + + diff --git a/docs/reference/EnvManager.html b/docs/reference/EnvManager.html index e1aa57a..047ca3f 100644 --- a/docs/reference/EnvManager.html +++ b/docs/reference/EnvManager.html @@ -1,282 +1,241 @@ - - - - - - - -Allows managing the test environment — EnvManager • Word Predictor - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +inst/extdata folder.">Allows managing the test environment — EnvManager • Word Predictor + Skip to contents + +
          -
          -
          -

          Allows managing the test environment

          - Source: R/env-manager.R - +
          +

          Allows managing the test environment

          + Source: R/env-manager.R +
          EnvManager.Rd
          -
          +

          This class provides a method for creating directories in the tempdir folder for testing purposes. It also provides a method for reading files from the inst/extdata folder.

          +
          +

          Super class

          +

          wordpredictor::Base -> EnvManager

          +
          +
          +

          Methods

          + +
          +

          Public methods

          + +

          Inherited methods

            +

            Method new()

            +

            It initializes the current object. It simply calls the base class +constructor.

            +

            Usage

            +

            EnvManager$new(rp = "../../", ve = 0)

            +
            -

            Super class

            +
            +

            Arguments

            +

            rp
            +

            The prefix for accessing the package root folder.

            -

            wordpredictor::Base -> EnvManager

            -

            Methods

            - -

            Public methods

            - - -

            Inherited methods -
              -
            -
            - -
            -

            Method new()

            -

            It initializes the current object. It simply calls the base class -constructor.

            Usage

            -

            EnvManager$new(rp = "../../", ve = 0)

            +
            ve
            +

            The level of detail in the information messages.

            -

            Arguments

            -

            -
            rp

            The prefix for accessing the package root folder.

            -
            ve

            The level of detail in the information messages.

            +

            +
            -

            -

            -

            Method get_data_fn()

            +

            +

            Method get_data_fn()

            Checks if the given file exists. If it does not exist, then it tries to load the file from the inst/extdata data folder of the package. It throws an error if the file was not found. If the -file exists, then the method simply returns the file name.

            Usage

            -

            EnvManager$get_data_fn(fn, dfn)

            +file exists, then the method simply returns the file name.

            +

            Usage

            +

            EnvManager$get_data_fn(fn, dfn)

            +
            + +
            +

            Arguments

            +

            fn
            +

            The file name.

            -

            Arguments

            -

            -
            fn

            The file name.

            -
            dfn

            The name of the default file in the external data folder +

            dfn
            +

            The name of the default file in the external data folder of the package.

            -

            -

            Returns

            + +

            +
            +
            +

            Returns

            The name of the file if it exists, or the full path to the default file.

            -

            -

            Method remove_files()

            -

            Removes all files in the given directory.

            Usage

            -

            EnvManager$remove_files(dn)

            - -

            Arguments

            -

            -
            dn

            The directory name.

            - -

            -

            -

            Method td_env()

            +
            + +

            +

            Method remove_files()

            +

            Removes all files in the given directory.

            +

            Usage

            +

            EnvManager$remove_files(dn)

            +
            + +
            +

            Arguments

            +

            dn
            +

            The directory name.

            + + +

            +
            + +

            +

            Method td_env()

            Removes the ed folder created by the setup_env method. Also sets -the R option, "ed" to NULL.

            Usage

            -

            EnvManager$td_env(rf = F)

            +the R option, "ed" to NULL.

            +

            Usage

            +

            EnvManager$td_env(rf = F)

            +
            + +
            +

            Arguments

            +

            rf
            +

            If the environment folder should be removed.

            -

            Arguments

            -

            -
            rf

            If the environment folder should be removed.

            -

            -

            -

            Method cp_env()

            +

            +
            + +

            +

            Method cp_env()

            Copies the ed folder created by the setup_env method to -inst/extdata.

            Usage

            -

            EnvManager$cp_env()

            +inst/extdata.

            +

            Usage

            +

            EnvManager$cp_env()

            +
            -

            -

            Method setup_env()

            + +

            +

            Method setup_env()

            Copies the given files from test folder to the -environment folder.

            Usage

            -

            EnvManager$setup_env(fns = c(), cf = NULL)

            +environment folder.

            +

            Usage

            +

            EnvManager$setup_env(fns = c(), cf = NULL)

            +
            -

            Arguments

            -

            -
            fns

            The list of test files to copy

            +
            +

            Arguments

            +

            fns
            +

            The list of test files to copy

            -
            cf

            A custom environment folder. It is a path relative to the + +

            cf
            +

            A custom environment folder. It is a path relative to the current directory. If not specified, then the tempdir function is used to generate the environment folder.

            -

            -

            Returns

            + +

            +
            +
            +

            Returns

            The list of folders that can be used during testing.

            -

            -

            Method clone()

            -

            The objects of this class are cloneable with this method.

            Usage

            -

            EnvManager$clone(deep = FALSE)

            +
            -

            Arguments

            -

            -
            deep

            Whether to make a deep clone.

            +

            +

            Method clone()

            +

            The objects of this class are cloneable with this method.

            +

            Usage

            +

            EnvManager$clone(deep = FALSE)

            +
            -

            +
            +

            Arguments

            +

            deep
            +

            Whether to make a deep clone.

            +

            +
            -
            -
            +
            -
            -
            -

            Developed by Nadir Latif.

            +
            + + +
            +

            Developed by Nadir Latif.

            -
            -

            Site built with pkgdown 1.6.1.

            +
            +

            Site built with pkgdown 2.0.1.

            -
            -
            +
            + - - - + diff --git a/docs/reference/Model.html b/docs/reference/Model.html index 844c301..1d12c36 100644 --- a/docs/reference/Model.html +++ b/docs/reference/Model.html @@ -1,140 +1,64 @@ - - - - - - - -Represents n-gram models — Model • Word Predictor - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +model.">Represents n-gram models — Model • Word Predictor + Skip to contents + +
            -
            -
            -

            Represents n-gram models

            - Source: R/model.R - +
            +

            Represents n-gram models

            + Source: R/model.R +
            Model.Rd
            -
            +

            The Model class represents n-gram models. An instance of the class is a single n-gram model. The attributes of this class are used to store n-gram model information. The class provides methods for loading and saving the @@ -142,9 +66,8 @@

            Represents n-gram models

            - -

            Details

            - +
            +

            Details

            The attributes of this class are used to store n-gram model information such as model name, model description, model file name, n-gram size, transition probabilities data, default probability for words, data cleaning and @@ -154,141 +77,182 @@

            Details object is used as input by classes that perform operations on the model such as evaluation of model performance, text predictions and comparison of model performance.

            -

            Super class

            +
            +
            +

            Super class

            +

            wordpredictor::Base -> Model

            +
            +
            +

            Public fields

            +

            pstats
            +

            The performance stats for the model.

            -

            wordpredictor::Base -> Model

            -

            Public fields

            -

            -
            pstats

            The performance stats for the model.

            +
            name
            +

            The model name.

            -
            name

            The model name.

            -
            desc

            The model description.

            +
            desc
            +

            The model description.

            -

            -

            Methods

            +

            +
            +
            +

            Methods

            -

            Public methods

            - - -

            Inherited methods -
              -
            -
            - -
            -

            Method new()

            +
            +

            Public methods

            + +

            Inherited methods

              +

              Method new()

              It initializes the current object. It is used to set the maximum n-gram number, sample size, input file name, data cleaner options, tokenization options, combined transition probabilities file -name and verbose.

              Usage

              -

              Model$new(
-  name = NULL,
-  desc = NULL,
-  fn = NULL,
-  df = NULL,
-  n = 4,
-  ssize = 0.3,
-  dir = ".",
-  dc_opts = list(),
-  tg_opts = list(),
-  ve = 0
-)

              +name and verbose.

              +

              Usage

              +

              Model$new(
+  name = NULL,
+  desc = NULL,
+  fn = NULL,
+  df = NULL,
+  n = 4,
+  ssize = 0.3,
+  dir = ".",
+  dc_opts = list(),
+  tg_opts = list(),
+  ve = 0
+)

              +
              + +
              +

              Arguments

              +

              name
              +

              The model name.

              + + +
              desc
              +

              The model description.

              + + +
              fn
              +

              The model file name.

              + + +
              df
              +

              The name of the file used to generate the model.

              + + +
              n
              +

              The maximum n-gram number supported by the model.

              + + +
              ssize
              +

              The sample size as a proportion of the input file.

              + -

              Arguments

              -

              -
              name

              The model name.

              +
              dir
              +

              The directory containing the model files.

              -
              desc

              The model description.

              -
              fn

              The model file name.

              +
              dc_opts
              +

              The data cleaner options.

              -
              df

              The name of the file used to generate the model.

              -
              n

              The maximum n-gram number supported by the model.

              +
              tg_opts
              +

              The token generator options.

              -
              ssize

              The sample size as a proportion of the input file.

              -
              dir

              The directory containing the model files.

              +
              ve
              +

              The level of detail in the information messages.

              -
              dc_opts

              The data cleaner options.

              -
              tg_opts

              The token generator options.

              +

              +
              -
              ve

              The level of detail in the information messages.

              +

              +

              Method load_model()

              +

              It loads the model using the given information

              +

              Usage

              +

              Model$load_model()

              +
              -

              -

              -

              Method load_model()

              -

              It loads the model using the given information

              Usage

              -

              Model$load_model()

              -

              -

              Method get_config()

              -

              It returns the given configuration data

              Usage

              -

              Model$get_config(cn)

              +

              +

              Method get_config()

              +

              It returns the given configuration data

              +

              Usage

              +

              Model$get_config(cn)

              +
              -

              Arguments

              -

              -
              cn

              The name of the required configuration.

              +
              +

              Arguments

              +

              cn
              +

              The name of the required configuration.

              -

              -

              Returns

              + +

              +
              +
              +

              Returns

              The configuration value.

              -

              -

              Method get_size()

              +
              + +

              +

              Method get_size()

              It returns the size of the current object. The object -size is calculated as the sum of sizes of the object attributes.

              Usage

              -

              Model$get_size()

              +size is calculated as the sum of sizes of the object attributes.

              +

              Usage

              +

              Model$get_size()

              +
              -

              Returns

              +
              +

              Returns

              The size of the object in bytes.

              -

              -

              Method clone()

              -

              The objects of this class are cloneable with this method.

              Usage

              -

              Model$clone(deep = FALSE)

              +
              -

              Arguments

              -

              -
              deep

              Whether to make a deep clone.

              +

              +

              Method clone()

              +

              The objects of this class are cloneable with this method.

              +

              Usage

              +

              Model$clone(deep = FALSE)

              +
              -

              +
              +

              Arguments

              +

              deep
              +

              Whether to make a deep clone.

              +

              +
              -
              -
              +
              -
              -
              -

              Developed by Nadir Latif.

              +
              + + +
              +

              Developed by Nadir Latif.

              -
              -

              Site built with pkgdown 1.6.1.

              +
              +

              Site built with pkgdown 2.0.1.

              -
              -
              +
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z<4!2h)qYMu3T9zmhkO!EXdq}X$1DL`7n1D;;Qc!>BCJF8BDGSK;xhwus$RHt>EZ1| zPaz%%J=k~Tcp|}bfPQi?-)`s=pglpzmQ;6)NA1ZX05;Z@=XI@Fl>WYkxDQHV(IB3A>Mrw5IbuVs z`a=nAvFwLr3hlE2b*&*9BUBy)1HlAr6^)9oUw-=|VV_b$h%=rR3P$__(%#3FApY&l zDHX1pY${chK*AwTQJy#rR65jHLR;;19rW>?*UQ8R>k0=WSZ8T%%*2;OTCu#W>;X7$ ze76EqY=wmhldQ_4vfdLzF|z&m(zqs~Klv8?J31VeYDu+>!@8`t178iBPB?gzFP|OA wLVx`EUrjv!Go9?;eo5+ozD53j`oR-(I1Ywc`fl;p$Qz*V*tea%_2|X_0wtcK0ssI2 diff --git a/docs/reference/ModelEvaluator.html b/docs/reference/ModelEvaluator.html index 53fcb20..2fe32b8 100644 --- a/docs/reference/ModelEvaluator.html +++ b/docs/reference/ModelEvaluator.html @@ -1,142 +1,68 @@ - - - - - - - -Evaluates performance of n-gram models — ModelEvaluator • Word Predictor - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +predictions.">Evaluates performance of n-gram models — ModelEvaluator • Word Predictor + Skip to contents + +
              -
              -
              -

              Evaluates performance of n-gram models

              - Source: R/model-evaluator.R - +
              +

              Evaluates performance of n-gram models

              + Source: R/model-evaluator.R +
              ModelEvaluator.Rd
              -
              +

              It provides methods for performing extrinsic and intrinsic evaluation of a n-gram model. It also provides a method for comparing performance of multiple n-gram models.

              @@ -146,9 +72,8 @@

              Evaluates performance of n-gram models

              - -

              Details

              - +
              +

              Details

              Before performing the intrinsic and extrinsic model evaluation, a validation file must be first generated. This can be done using the DataSampler class.

              Each line in the validation file is evaluated. For intrinsic evaluation @@ -158,109 +83,126 @@

              Details the actual next word is one of the three predicted next words, then the prediction is considered to be accurate. The extrinsic evaluation returns the percentage of correct and incorrect predictions.

              -

              Super class

              - -

              wordpredictor::Base -> ModelEvaluator

              -

              Methods

              - +
              +
              +

              Super class

              +

              wordpredictor::Base -> ModelEvaluator

              +
              +
              +

              Methods

              -

              Public methods

              - - -

              Inherited methods -
                -
              -
              - -
              -

              Method new()

              +
              +

              Public methods

              + +

              Inherited methods

                +

                Method new()

                It initializes the current object. It is used to set the -model file name and verbose options.

                Usage

                -

                ModelEvaluator$new(mf = NULL, ve = 0)

                +model file name and verbose options.

                +

                Usage

                +

                ModelEvaluator$new(mf = NULL, ve = 0)

                +
                + +
                +

                Arguments

                +

                mf
                +

                The model file name.

                + -

                Arguments

                -

                -
                mf

                The model file name.

                +
                ve
                +

                The level of detail in the information messages.

                -
                ve

                The level of detail in the information messages.

                -

                -

                -

                Method compare_performance()

                +

                +
                + +

                +

                Method compare_performance()

                It compares the performance of the models in the given folder.

                The performance of the model is compared for the 4 metric which are time taken, memory used, Perplexity and accuracy. The performance comparison is displayed on plots.

                4 plots are displayed. One for each performance metric. A fifth plot shows the variation of Perplexity with accuracy. All 5 plots are -plotted on one page.

                Usage

                -

                ModelEvaluator$compare_performance(opts)

                - -

                Arguments

                -

                -
                opts

                The options for comparing model performance. -

                  -

                • save_to. The graphics device to save the plot to. +plotted on one page.

                  +

                  Usage

                  +

                  ModelEvaluator$compare_performance(opts)

                  +
                  + +
                  +

                  Arguments

                  +

                  opts
                  +

                  The options for comparing model performance.

                  • save_to. The graphics device to save the plot to. NULL implies plot is printed.

                  • dir. The directory containing the model file, plot and stats.

                    • -

                  - -

                  -

                  Examples

                  -

                  # Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be
-# used.
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("def-model.RDS")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-# End of environment setup code
-# ModelEvaluator class object is created
-me <- ModelEvaluator$new(ve = ve)
-# The performance evaluation is performed
-me$compare_performance(opts = list(
-    "save_to" = NULL,
-    "dir" = ed
-))
-
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-

                  - -

                  -

                  Method plot_stats()

                  +
                + + +

                +
                +
                +

                Examples

                +

                # Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be
+# used.
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("def-model.RDS")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+# ModelEvaluator class object is created
+me <- ModelEvaluator$new(ve = ve)
+# The performance evaluation is performed
+me$compare_performance(opts = list(
+    "save_to" = NULL,
+    "dir" = ed
+))
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()

                +
                + + +

                +

                Method plot_stats()

                It plots the given stats on 5 plots. The plots are displayed on a single page.

                The 4 performance metrics which are time taken, memory, Perplexity and accuracy are plotted against the model name. Another plot -compares Perplexity with accuracy for each model.

                Usage

                -

                ModelEvaluator$plot_stats(data)

                +compares Perplexity with accuracy for each model.

                +

                Usage

                +

                ModelEvaluator$plot_stats(data)

                +
                + +
                +

                Arguments

                +

                data
                +

                The data to plot

                -

                Arguments

                -

                -
                data

                The data to plot

                -

                -

                Returns

                +

                +
                +
                +

                Returns

                The ggplot object is returned.

                -

                -

                Method evaluate_performance()

                +
                + +

                +

                Method evaluate_performance()

                It performs intrinsic and extrinsic evaluation for the given model and validation text file. The given number of lines in the validation file are used in the evaluation

                @@ -272,365 +214,403 @@

                Ret size of the model object. Time taken is the time needed for performing both evaluations.

                The results of the model evaluation are saved within the model object -and also returned.

                Usage

                -

                ModelEvaluator$evaluate_performance(lc, fn)

                +and also returned.

                +

                Usage

                +

                ModelEvaluator$evaluate_performance(lc, fn)

                +
                -

                Arguments

                -

                -
                lc

                The number of lines of text in the validation file to be +

                +

                Arguments

                +

                lc
                +

                The number of lines of text in the validation file to be used for the evaluation.

                -
                fn

                The name of the validation file. If it does not exist, then + +

                fn
                +

                The name of the validation file. If it does not exist, then the default file validation-clean.txt is checked in the models folder

                -

                -

                Returns

                + +

                +
                +
                +

                Returns

                The performance stats are returned.

                -

                Examples

                -

                # Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("def-model.RDS", "validate-clean.txt")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-# End of environment setup code
-
-# The model file name
-mfn <- paste0(ed, "/def-model.RDS")
-# The validation file name
-vfn <- paste0(ed, "/validate-clean.txt")
-
-# ModelEvaluator class object is created
-me <- ModelEvaluator$new(mf = mfn, ve = ve)
-# The performance evaluation is performed
-stats <- me$evaluate_performance(lc = 20, fn = vfn)
-# The evaluation stats are printed
-print(stats)
-
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-

                - -

                -

                Method intrinsic_evaluation()

                +
                +
                +

                Examples

                +

                # Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("def-model.RDS", "validate-clean.txt")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The model file name
+mfn <- paste0(ed, "/def-model.RDS")
+# The validation file name
+vfn <- paste0(ed, "/validate-clean.txt")
+
+# ModelEvaluator class object is created
+me <- ModelEvaluator$new(mf = mfn, ve = ve)
+# The performance evaluation is performed
+stats <- me$evaluate_performance(lc = 20, fn = vfn)
+# The evaluation stats are printed
+print(stats)
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()

                +
                + + +

                +

                Method intrinsic_evaluation()

                Evaluates the model using intrinsic evaluation based on Perplexity. The given number of sentences are taken from the -validation file. For each sentence, the Perplexity is calculated.

                Usage

                -

                ModelEvaluator$intrinsic_evaluation(lc, fn)

                +validation file. For each sentence, the Perplexity is calculated.

                +

                Usage

                +

                ModelEvaluator$intrinsic_evaluation(lc, fn)

                +
                -

                Arguments

                -

                -
                lc

                The number of lines of text in the validation file to be +

                +

                Arguments

                +

                lc
                +

                The number of lines of text in the validation file to be used for the evaluation.

                -
                fn

                The name of the validation file. If it does not exist, then + +

                fn
                +

                The name of the validation file. If it does not exist, then the default file validation-clean.txt is checked in the models folder

                -

                -

                Returns

                + +

                +
                +
                +

                Returns

                The min, max and mean Perplexity score.

                -

                Examples

                -

                # Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("def-model.RDS", "validate-clean.txt")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-# End of environment setup code
-
-# The model file name
-mfn <- paste0(ed, "/def-model.RDS")
-# The validation file name
-vfn <- paste0(ed, "/validate-clean.txt")
-
-# ModelEvaluator class object is created
-me <- ModelEvaluator$new(mf = mfn, ve = ve)
-# The intrinsic evaluation is performed
-stats <- me$intrinsic_evaluation(lc = 20, fn = vfn)
-# The evaluation stats are printed
-print(stats)
-
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-

                - -

                -

                Method extrinsic_evaluation()

                +
                +
                +

                Examples

                +

                # Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("def-model.RDS", "validate-clean.txt")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The model file name
+mfn <- paste0(ed, "/def-model.RDS")
+# The validation file name
+vfn <- paste0(ed, "/validate-clean.txt")
+
+# ModelEvaluator class object is created
+me <- ModelEvaluator$new(mf = mfn, ve = ve)
+# The intrinsic evaluation is performed
+stats <- me$intrinsic_evaluation(lc = 20, fn = vfn)
+# The evaluation stats are printed
+print(stats)
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()

                +
                + + +

                +

                Method extrinsic_evaluation()

                Evaluates the model using extrinsic evaluation based on Accuracy. The given number of sentences are taken from the validation file.

                For each sentence, the model is used to predict the next word. The accuracy stats are returned. A prediction is considered to be -correct if one of the predicted words matches the actual word.

                Usage

                -

                ModelEvaluator$extrinsic_evaluation(lc, fn)

                +correct if one of the predicted words matches the actual word.

                +

                Usage

                +

                ModelEvaluator$extrinsic_evaluation(lc, fn)

                +
                -

                Arguments

                -

                -
                lc

                The number of lines of text in the validation file to be +

                +

                Arguments

                +

                lc
                +

                The number of lines of text in the validation file to be used for the evaluation.

                -
                fn

                The name of the validation file.

                -

                -

                Returns

                +
                fn
                +

                The name of the validation file.

                + + +

                +
                +
                +

                Returns

                The number of correct and incorrect predictions.

                -

                Examples

                -

                # Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("def-model.RDS", "validate-clean.txt")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-# End of environment setup code
-
-# The model file name
-mfn <- paste0(ed, "/def-model.RDS")
-# The validation file name
-vfn <- paste0(ed, "/validate-clean.txt")
-
-# ModelEvaluator class object is created
-me <- ModelEvaluator$new(mf = mfn, ve = ve)
-# The intrinsic evaluation is performed
-stats <- me$extrinsic_evaluation(lc = 100, fn = vfn)
-# The evaluation stats are printed
-print(stats)
-
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-

                - -

                -

                Method clone()

                -

                The objects of this class are cloneable with this method.

                Usage

                -

                ModelEvaluator$clone(deep = FALSE)

                - -

                Arguments

                -

                -
                deep

                Whether to make a deep clone.

                - -

                - - - -

                Examples

                - 
                
-## ------------------------------------------------
-## Method `ModelEvaluator$compare_performance`
-## ------------------------------------------------
-
-# Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be
-# used.
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("def-model.RDS")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-
                #> [1] "CITATION" "examples" "extdata" 
                # End of environment setup code
-# ModelEvaluator class object is created
-me <- ModelEvaluator$new(ve = ve)
-# The performance evaluation is performed
-me$compare_performance(opts = list(
-    "save_to" = NULL,
-    "dir" = ed
-))
-
                
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-
-## ------------------------------------------------
-## Method `ModelEvaluator$evaluate_performance`
-## ------------------------------------------------
-
-# Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("def-model.RDS", "validate-clean.txt")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-
                #> [1] "CITATION" "examples" "extdata" 
                # End of environment setup code
-
-# The model file name
-mfn <- paste0(ed, "/def-model.RDS")
-# The validation file name
-vfn <- paste0(ed, "/validate-clean.txt")
-
-# ModelEvaluator class object is created
-me <- ModelEvaluator$new(mf = mfn, ve = ve)
-# The performance evaluation is performed
-stats <- me$evaluate_performance(lc = 20, fn = vfn)
-# The evaluation stats are printed
-print(stats)
-
                #> $m
-#> [1] 279320
-#> 
-#> $t
-#> [1] 0.747
-#> 
-#> $p
-#> [1] 2297.35
-#> 
-#> $a
-#> [1] 0
-#> 
                
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-
-## ------------------------------------------------
-## Method `ModelEvaluator$intrinsic_evaluation`
-## ------------------------------------------------
-
-# Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("def-model.RDS", "validate-clean.txt")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-
                #> [1] "CITATION" "examples" "extdata" 
                # End of environment setup code
-
-# The model file name
-mfn <- paste0(ed, "/def-model.RDS")
-# The validation file name
-vfn <- paste0(ed, "/validate-clean.txt")
-
-# ModelEvaluator class object is created
-me <- ModelEvaluator$new(mf = mfn, ve = ve)
-# The intrinsic evaluation is performed
-stats <- me$intrinsic_evaluation(lc = 20, fn = vfn)
-# The evaluation stats are printed
-print(stats)
-
                #> $min
-#> [1] 282
-#> 
-#> $max
-#> [1] 8248
-#> 
-#> $mean
-#> [1] 2297.35
-#> 
                
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-
-## ------------------------------------------------
-## Method `ModelEvaluator$extrinsic_evaluation`
-## ------------------------------------------------
-
-# Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("def-model.RDS", "validate-clean.txt")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-
                #> [1] "CITATION" "examples" "extdata" 
                # End of environment setup code
-
-# The model file name
-mfn <- paste0(ed, "/def-model.RDS")
-# The validation file name
-vfn <- paste0(ed, "/validate-clean.txt")
-
-# ModelEvaluator class object is created
-me <- ModelEvaluator$new(mf = mfn, ve = ve)
-# The intrinsic evaluation is performed
-stats <- me$extrinsic_evaluation(lc = 100, fn = vfn)
-# The evaluation stats are printed
-print(stats)
-
                #> $valid
-#> [1] 1
-#> 
-#> $invalid
-#> [1] 74
-#> 
-#> $valid_perc
-#> [1] 1.333333
-#> 
-#> $invalid_perc
-#> [1] 98.66667
-#> 
                
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-
                -
                - +
                +
                +

                Examples

                +

                # Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("def-model.RDS", "validate-clean.txt")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The model file name
+mfn <- paste0(ed, "/def-model.RDS")
+# The validation file name
+vfn <- paste0(ed, "/validate-clean.txt")
+
+# ModelEvaluator class object is created
+me <- ModelEvaluator$new(mf = mfn, ve = ve)
+# The intrinsic evaluation is performed
+stats <- me$extrinsic_evaluation(lc = 100, fn = vfn)
+# The evaluation stats are printed
+print(stats)
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()

                -
                -
                -

                Developed by Nadir Latif.

                +

                +

                Method clone()

                +

                The objects of this class are cloneable with this method.

                +

                Usage

                +

                ModelEvaluator$clone(deep = FALSE)

                -
                -

                Site built with pkgdown 1.6.1.

                +
                +

                Arguments

                +

                deep
                +

                Whether to make a deep clone.

                + + +

                -
                -
                +
                - +
                + +
                +

                Examples

                + 
                
+## ------------------------------------------------
+## Method `ModelEvaluator$compare_performance`
+## ------------------------------------------------
+
+# Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be
+# used.
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("def-model.RDS")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+# ModelEvaluator class object is created
+me <- ModelEvaluator$new(ve = ve)
+# The performance evaluation is performed
+me$compare_performance(opts = list(
+    "save_to" = NULL,
+    "dir" = ed
+))
+
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()
+
+## ------------------------------------------------
+## Method `ModelEvaluator$evaluate_performance`
+## ------------------------------------------------
+
+# Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("def-model.RDS", "validate-clean.txt")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The model file name
+mfn <- paste0(ed, "/def-model.RDS")
+# The validation file name
+vfn <- paste0(ed, "/validate-clean.txt")
+
+# ModelEvaluator class object is created
+me <- ModelEvaluator$new(mf = mfn, ve = ve)
+# The performance evaluation is performed
+stats <- me$evaluate_performance(lc = 20, fn = vfn)
+# The evaluation stats are printed
+print(stats)
+#> $m
+#> [1] 279320
+#> 
+#> $t
+#> [1] 1.018
+#> 
+#> $p
+#> [1] 2297.35
+#> 
+#> $a
+#> [1] 0
+#> 
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()
+
+## ------------------------------------------------
+## Method `ModelEvaluator$intrinsic_evaluation`
+## ------------------------------------------------
+
+# Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("def-model.RDS", "validate-clean.txt")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The model file name
+mfn <- paste0(ed, "/def-model.RDS")
+# The validation file name
+vfn <- paste0(ed, "/validate-clean.txt")
+
+# ModelEvaluator class object is created
+me <- ModelEvaluator$new(mf = mfn, ve = ve)
+# The intrinsic evaluation is performed
+stats <- me$intrinsic_evaluation(lc = 20, fn = vfn)
+# The evaluation stats are printed
+print(stats)
+#> $min
+#> [1] 282
+#> 
+#> $max
+#> [1] 8248
+#> 
+#> $mean
+#> [1] 2297.35
+#> 
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()
+
+## ------------------------------------------------
+## Method `ModelEvaluator$extrinsic_evaluation`
+## ------------------------------------------------
+
+# Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("def-model.RDS", "validate-clean.txt")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The model file name
+mfn <- paste0(ed, "/def-model.RDS")
+# The validation file name
+vfn <- paste0(ed, "/validate-clean.txt")
+
+# ModelEvaluator class object is created
+me <- ModelEvaluator$new(mf = mfn, ve = ve)
+# The intrinsic evaluation is performed
+stats <- me$extrinsic_evaluation(lc = 100, fn = vfn)
+# The evaluation stats are printed
+print(stats)
+#> $valid
+#> [1] 1
+#> 
+#> $invalid
+#> [1] 74
+#> 
+#> $valid_perc
+#> [1] 1.333333
+#> 
+#> $invalid_perc
+#> [1] 98.66667
+#> 
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()
+
                +
                +
                + + +
                +

                Developed by Nadir Latif.

                +
                + +
                +

                Site built with pkgdown 2.0.1.

                +
                +
                • - - + + + + diff --git a/docs/reference/ModelGenerator.html b/docs/reference/ModelGenerator.html index 6d33efc..a5ca4fe 100644 --- a/docs/reference/ModelGenerator.html +++ b/docs/reference/ModelGenerator.html @@ -1,334 +1,280 @@ - - - - - - - -Generates n-gram models from a text file — ModelGenerator • Word Predictor - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Generates n-gram models from a text file — ModelGenerator • Word Predictor + Skip to contents + +
                -
                -
                -

                Generates n-gram models from a text file

                - Source: R/model-generator.R - +
                +

                Generates n-gram models from a text file

                + Source: R/model-generator.R +
                ModelGenerator.Rd
                -
                +

                It provides a method for generating n-gram models. The n-gram models may be customized by specifying data cleaning and tokenization options.

                - -

                Details

                - +
                +

                Details

                It provides a method that generates a n-gram model. The n-gram model may be customized by specifying the data cleaning and tokenization options.

                The data cleaning options include removal of punctuation, stop words, extra space, non-dictionary words and bad words. The tokenization options include n-gram number and word stemming.

                -

                Super class

                - -

                wordpredictor::Base -> ModelGenerator

                -

                Methods

                - +
                +
                +

                Super class

                +

                wordpredictor::Base -> ModelGenerator

                +
                +
                +

                Methods

                -

                Public methods

                - - -

                Inherited methods -
                  -
                -
                - -
                -

                Method new()

                +
                +

                Public methods

                + +

                Inherited methods

                  +

                  Method new()

                  It initializes the current object. It is used to set the maximum n-gram number, sample size, input file name, data cleaner options, -tokenization options and verbose option.

                  Usage

                  -

                  ModelGenerator$new(
-  name = NULL,
-  desc = NULL,
-  fn = NULL,
-  df = NULL,
-  n = 4,
-  ssize = 0.3,
-  dir = ".",
-  dc_opts = list(),
-  tg_opts = list(),
-  ve = 0
-)

                  - -

                  Arguments

                  -

                  -
                  name

                  The model name.

                  - -
                  desc

                  The model description.

                  - -
                  fn

                  The model file name.

                  - -
                  df

                  The path of the input text file. It should be the short +tokenization options and verbose option.

                  +

                  Usage

                  +

                  ModelGenerator$new(
+  name = NULL,
+  desc = NULL,
+  fn = NULL,
+  df = NULL,
+  n = 4,
+  ssize = 0.3,
+  dir = ".",
+  dc_opts = list(),
+  tg_opts = list(),
+  ve = 0
+)

                  +
                  + +
                  +

                  Arguments

                  +

                  name
                  +

                  The model name.

                  + + +
                  desc
                  +

                  The model description.

                  + + +
                  fn
                  +

                  The model file name.

                  + + +
                  df
                  +

                  The path of the input text file. It should be the short file name and should be present in the data directory.

                  -
                  n

                  The n-gram size of the model.

                  -
                  ssize

                  The sample size as a proportion of the input file.

                  +
                  n
                  +

                  The n-gram size of the model.

                  -
                  dir

                  The directory containing the input and output files.

                  -
                  dc_opts

                  The data cleaner options.

                  +
                  ssize
                  +

                  The sample size as a proportion of the input file.

                  -
                  tg_opts

                  The token generator options.

                  -
                  ve

                  The level of detail in the information messages.

                  +
                  dir
                  +

                  The directory containing the input and output files.

                  + + +
                  dc_opts
                  +

                  The data cleaner options.

                  + + +
                  tg_opts
                  +

                  The token generator options.

                  + + +
                  ve
                  +

                  The level of detail in the information messages.

                  + + +

                  +
                  -

                  -

                  -

                  Method generate_model()

                  +

                  +

                  Method generate_model()

                  It generates the model using the parameters passed to the object's constructor. It generates a n-gram model file and saves -it to the model directory.

                  Usage

                  -

                  ModelGenerator$generate_model()

                  - -

                  Examples

                  -

                  # Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("input.txt")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-# End of environment setup code
-
-# ModelGenerator class object is created
-mg <- ModelGenerator$new(
-    name = "default-model",
-    desc = "1 MB size and default options",
-    fn = "def-model.RDS",
-    df = "input.txt",
-    n = 4,
-    ssize = 0.99,
-    dir = ed,
-    dc_opts = list(),
-    tg_opts = list(),
-    ve = ve
-)
-# The n-gram model is generated
-mg$generate_model()
-
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-

                  - -

                  -

                  Method clone()

                  -

                  The objects of this class are cloneable with this method.

                  Usage

                  -

                  ModelGenerator$clone(deep = FALSE)

                  - -

                  Arguments

                  -

                  -
                  deep

                  Whether to make a deep clone.

                  - -

                  - - - -

                  Examples

                  - 
                  
-## ------------------------------------------------
-## Method `ModelGenerator$generate_model`
-## ------------------------------------------------
-
-# Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("input.txt")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-
                  #> [1] "CITATION" "examples" "extdata" 
                  # End of environment setup code
-
-# ModelGenerator class object is created
-mg <- ModelGenerator$new(
-    name = "default-model",
-    desc = "1 MB size and default options",
-    fn = "def-model.RDS",
-    df = "input.txt",
-    n = 4,
-    ssize = 0.99,
-    dir = ed,
-    dc_opts = list(),
-    tg_opts = list(),
-    ve = ve
-)
-# The n-gram model is generated
-mg$generate_model()
-
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-
                  -
                  - +it to the model directory.

                  +

                  Usage

                  +

                  ModelGenerator$generate_model()

                  +
                  +

                  Examples

                  +

                  # Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("input.txt")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# ModelGenerator class object is created
+mg <- ModelGenerator$new(
+    name = "default-model",
+    desc = "1 MB size and default options",
+    fn = "def-model.RDS",
+    df = "input.txt",
+    n = 4,
+    ssize = 0.99,
+    dir = ed,
+    dc_opts = list(),
+    tg_opts = list(),
+    ve = ve
+)
+# The n-gram model is generated
+mg$generate_model()
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()

                  +
                  -
                  -
                  -

                  Developed by Nadir Latif.

                  + +

                  +

                  Method clone()

                  +

                  The objects of this class are cloneable with this method.

                  +

                  Usage

                  +

                  ModelGenerator$clone(deep = FALSE)

                  -
                  -

                  Site built with pkgdown 1.6.1.

                  +
                  +

                  Arguments

                  +

                  deep
                  +

                  Whether to make a deep clone.

                  + + +

                  -
                  -
                  +
                  - +
                  +
                  +

                  Examples

                  + 
                  
+## ------------------------------------------------
+## Method `ModelGenerator$generate_model`
+## ------------------------------------------------
+
+# Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("input.txt")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# ModelGenerator class object is created
+mg <- ModelGenerator$new(
+    name = "default-model",
+    desc = "1 MB size and default options",
+    fn = "def-model.RDS",
+    df = "input.txt",
+    n = 4,
+    ssize = 0.99,
+    dir = ed,
+    dc_opts = list(),
+    tg_opts = list(),
+    ve = ve
+)
+# The n-gram model is generated
+mg$generate_model()
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()
+
                  +
                  +
                  - - +
                  +

                  Developed by Nadir Latif.

                  +
                  + +
                  +

                  Site built with pkgdown 2.0.1.

                  +
                  + +
                  + + + + + + diff --git a/docs/reference/ModelPredictor.html b/docs/reference/ModelPredictor.html index 2a7c651..0597c8f 100644 --- a/docs/reference/ModelPredictor.html +++ b/docs/reference/ModelPredictor.html @@ -1,140 +1,64 @@ - - - - - - - -Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor • Word Predictor - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +the probability of a given word and set of previous words.">Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor • Word Predictor + Skip to contents + +
                  -
                  -
                  -

                  Allows predicting text, calculating word probabilities and Perplexity

                  - Source: R/model-predictor.R - +
                  +

                  Allows predicting text, calculating word probabilities and Perplexity

                  + Source: R/model-predictor.R +
                  ModelPredictor.Rd
                  -
                  +

                  It provides a method for predicting the new word given a set of previous words. It also provides a method for calculating the Perplexity score for a set of words. Furthermore it provides a method for calculating @@ -142,101 +66,117 @@

                  Allows predicting text, calculating word probabilities and Perplexity

                  +
                  +

                  Super class

                  +

                  wordpredictor::Base -> ModelPredictor

                  +
                  +
                  +

                  Methods

                  + +
                  +

                  Public methods

                  + +

                  Inherited methods

                    +

                    Method new()

                    +

                    It initializes the current object. It is used to set the +model file name and verbose options.

                    +

                    Usage

                    +

                    ModelPredictor$new(mf, ve = 0)

                    +
                    -

                    Super class

                    +
                    +

                    Arguments

                    +

                    mf
                    +

                    The model file name.

                    -

                    wordpredictor::Base -> ModelPredictor

                    -

                    Methods

                    - -

                    Public methods

                    - - -

                    Inherited methods -
                      -
                    -
                    - -
                    -

                    Method new()

                    -

                    It initializes the current object. It is used to set the -model file name and verbose options.

                    Usage

                    -

                    ModelPredictor$new(mf, ve = 0)

                    +
                    ve
                    +

                    The level of detail in the information messages.

                    -

                    Arguments

                    -

                    -
                    mf

                    The model file name.

                    -
                    ve

                    The level of detail in the information messages.

                    +

                    +
                    -

                    -

                    -

                    Method get_model()

                    -

                    Returns the Model class object.

                    Usage

                    -

                    ModelPredictor$get_model()

                    +

                    +

                    Method get_model()

                    +

                    Returns the Model class object.

                    +

                    Usage

                    +

                    ModelPredictor$get_model()

                    +
                    -

                    Returns

                    +
                    +

                    Returns

                    The Model class object is returned.

                    -

                    -

                    Method calc_perplexity()

                    +
                    + +

                    +

                    Method calc_perplexity()

                    The Perplexity for the given sentence is calculated. For each word, the probability of the word given the previous words is calculated. The probabilities are multiplied and then inverted. The nth root of the result is the perplexity, where n is the number of words in the sentence. If the stem_words tokenization option was specified when creating the given model file, then the previous words -are converted to their stems.

                    Usage

                    -

                    ModelPredictor$calc_perplexity(words)

                    +are converted to their stems.

                    +

                    Usage

                    +

                    ModelPredictor$calc_perplexity(words)

                    +
                    + +
                    +

                    Arguments

                    +

                    words
                    +

                    The list of words.

                    -

                    Arguments

                    -

                    -
                    words

                    The list of words.

                    -

                    -

                    Returns

                    +

                    +
                    +
                    +

                    Returns

                    The perplexity of the given list of words.

                    -

                    Examples

                    -

                    # Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("def-model.RDS")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-# End of environment setup code
-
-# The model file name
-mfn <- paste0(ed, "/def-model.RDS")
-# ModelPredictor class object is created
-mp <- ModelPredictor$new(mf = mfn, ve = ve)
-# The sentence whoose Perplexity is to be calculated
-l <- "last year at this time i was preparing for a trip to rome"
-# The line is split in to words
-w <- strsplit(l, " ")[[1]]
-# The Perplexity of the sentence is calculated
-p <- mp$calc_perplexity(w)
-# The sentence Perplexity is printed
-print(p)
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-

                    - -

                    -

                    Method predict_word()

                    +
                    +
                    +

                    Examples

                    +

                    # Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("def-model.RDS")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The model file name
+mfn <- paste0(ed, "/def-model.RDS")
+# ModelPredictor class object is created
+mp <- ModelPredictor$new(mf = mfn, ve = ve)
+# The sentence whoose Perplexity is to be calculated
+l <- "last year at this time i was preparing for a trip to rome"
+# The line is split in to words
+w <- strsplit(l, " ")[[1]]
+# The Perplexity of the sentence is calculated
+p <- mp$calc_perplexity(w)
+# The sentence Perplexity is printed
+print(p)
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()

                    +
                    + + +

                    +

                    Method predict_word()

                    Predicts the next word given a list of previous words. It checks the last n previous words in the transition probabilities data, where n is equal to 1 - n-gram size of model. If there is a @@ -244,53 +184,66 @@

                    Exa If there is no match, then the last n-1 previous words are checked. This process is continued until the last word is checked. If there is no match, then empty result is returned. The given words may -optionally be stemmed.

                    Usage

                    -

                    ModelPredictor$predict_word(words, count = 3, dc = NULL)

                    +optionally be stemmed.

                    +

                    Usage

                    +

                    ModelPredictor$predict_word(words, count = 3, dc = NULL)

                    +
                    -

                    Arguments

                    -

                    -
                    words

                    A character vector of previous words or a single vector +

                    +

                    Arguments

                    +

                    words
                    +

                    A character vector of previous words or a single vector containing the previous word text.

                    -
                    count

                    The number of results to return.

                    -
                    dc

                    A DataCleaner object. If it is given, then the given words

                    +
                    count
                    +

                    The number of results to return.

                    + + +
                    dc
                    +

                    A DataCleaner object. If it is given, then the given words

                    -

                    -

                    Returns

                    + +

                    +
                    +
                    +

                    Returns

                    The top 3 predicted words along with their probabilities.

                    -

                    Examples

                    -

                    # Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("def-model.RDS")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, "rp" = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-# End of environment setup code
-
-# The model file name
-mfn <- paste0(ed, "/def-model.RDS")
-# ModelPredictor class object is created
-mp <- ModelPredictor$new(mf = mfn, ve = ve)
-# The next word is predicted
-nws <- mp$predict_word("today is", count = 10)
-# The predicted next words are printed
-print(nws)
-
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-

                    - -

                    -

                    Method get_word_prob()

                    +
                    +
                    +

                    Examples

                    +

                    # Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("def-model.RDS")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, "rp" = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The model file name
+mfn <- paste0(ed, "/def-model.RDS")
+# ModelPredictor class object is created
+mp <- ModelPredictor$new(mf = mfn, ve = ve)
+# The next word is predicted
+nws <- mp$predict_word("today is", count = 10)
+# The predicted next words are printed
+print(nws)
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()

                    +
                    + + +

                    +

                    Method get_word_prob()

                    Calculates the probability of the given word given the previous words. The last n words are converted to numeric hash using digest2int function. All other words are ignored. n is equal to 1 - @@ -304,199 +257,214 @@

                    Exa returned. This is known as back-off. If the word probability could not be found then the default probability is returned. The default probability is calculated as 1/(N+V), Where N = number of words in -corpus and V is the number of dictionary words.

                    Usage

                    -

                    ModelPredictor$get_word_prob(word, pw)

                    +corpus and V is the number of dictionary words.

                    +

                    Usage

                    +

                    ModelPredictor$get_word_prob(word, pw)

                    +
                    -

                    Arguments

                    -

                    -
                    word

                    The word whose probability is to be calculated.

                    +
                    +

                    Arguments

                    +

                    word
                    +

                    The word whose probability is to be calculated.

                    -
                    pw

                    The previous words.

                    -

                    -

                    Returns

                    +
                    pw
                    +

                    The previous words.

                    + + +

                    +
                    +
                    +

                    Returns

                    The probability of the word given the previous words.

                    -

                    Examples

                    -

                    # Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("def-model.RDS")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, "rp" = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-# End of environment setup code
-
-# The model file name
-mfn <- paste0(ed, "/def-model.RDS")
-# ModelPredictor class object is created
-mp <- ModelPredictor$new(mf = mfn, ve = ve)
-# The probability that the next word is "you" given the prev words
-# "how" and "are"
-prob <- mp$get_word_prob(word = "you", pw = c("how", "are"))
-# The probability is printed
-print(prob)
-
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-

                    - -

                    -

                    Method clone()

                    -

                    The objects of this class are cloneable with this method.

                    Usage

                    -

                    ModelPredictor$clone(deep = FALSE)

                    - -

                    Arguments

                    -

                    -
                    deep

                    Whether to make a deep clone.

                    - -

                    - - - -

                    Examples

                    - 
                    
-## ------------------------------------------------
-## Method `ModelPredictor$calc_perplexity`
-## ------------------------------------------------
-
-# Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("def-model.RDS")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-
                    #> [1] "CITATION" "examples" "extdata" 
                    # End of environment setup code
-
-# The model file name
-mfn <- paste0(ed, "/def-model.RDS")
-# ModelPredictor class object is created
-mp <- ModelPredictor$new(mf = mfn, ve = ve)
-# The sentence whoose Perplexity is to be calculated
-l <- "last year at this time i was preparing for a trip to rome"
-# The line is split in to words
-w <- strsplit(l, " ")[[1]]
-# The Perplexity of the sentence is calculated
-p <- mp$calc_perplexity(w)
-# The sentence Perplexity is printed
-print(p)
-
                    #> [1] 1767
                    # The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-
-## ------------------------------------------------
-## Method `ModelPredictor$predict_word`
-## ------------------------------------------------
-
-# Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("def-model.RDS")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, "rp" = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-
                    #> [1] "CITATION" "examples" "extdata" 
                    # End of environment setup code
-
-# The model file name
-mfn <- paste0(ed, "/def-model.RDS")
-# ModelPredictor class object is created
-mp <- ModelPredictor$new(mf = mfn, ve = ve)
-# The next word is predicted
-nws <- mp$predict_word("today is", count = 10)
-# The predicted next words are printed
-print(nws)
-
                    #> $found
-#> [1] TRUE
-#> 
-#> $words
-#>  [1] "a"      "the"    "used"   "better" "hard"   "rare"   "to"     "best"  
-#>  [9] "carved" "dry"   
-#> 
-#> $probs
-#>  [1] 0.17460317 0.11111111 0.06349206 0.03174603 0.03174603 0.03174603
-#>  [7] 0.03174603 0.01587302 0.01587302 0.01587302
-#> 
                    
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-
-## ------------------------------------------------
-## Method `ModelPredictor$get_word_prob`
-## ------------------------------------------------
-
-# Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("def-model.RDS")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, "rp" = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-
                    #> [1] "CITATION" "examples" "extdata" 
                    # End of environment setup code
-
-# The model file name
-mfn <- paste0(ed, "/def-model.RDS")
-# ModelPredictor class object is created
-mp <- ModelPredictor$new(mf = mfn, ve = ve)
-# The probability that the next word is "you" given the prev words
-# "how" and "are"
-prob <- mp$get_word_prob(word = "you", pw = c("how", "are"))
-# The probability is printed
-print(prob)
-
                    #> [1] 0.0024581
                    
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-
                    -
                    - +
                    +
                    +

                    Examples

                    +

                    # Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("def-model.RDS")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, "rp" = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The model file name
+mfn <- paste0(ed, "/def-model.RDS")
+# ModelPredictor class object is created
+mp <- ModelPredictor$new(mf = mfn, ve = ve)
+# The probability that the next word is "you" given the prev words
+# "how" and "are"
+prob <- mp$get_word_prob(word = "you", pw = c("how", "are"))
+# The probability is printed
+print(prob)
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()

                    -
                    -
                    -

                    Developed by Nadir Latif.

                    +

                    +

                    Method clone()

                    +

                    The objects of this class are cloneable with this method.

                    +

                    Usage

                    +

                    ModelPredictor$clone(deep = FALSE)

                    -
                    -

                    Site built with pkgdown 1.6.1.

                    +
                    +

                    Arguments

                    +

                    deep
                    +

                    Whether to make a deep clone.

                    + + +

                    -
                    -
                    +
                    - +
                    +
                    +

                    Examples

                    + 
                    
+## ------------------------------------------------
+## Method `ModelPredictor$calc_perplexity`
+## ------------------------------------------------
+
+# Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("def-model.RDS")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The model file name
+mfn <- paste0(ed, "/def-model.RDS")
+# ModelPredictor class object is created
+mp <- ModelPredictor$new(mf = mfn, ve = ve)
+# The sentence whoose Perplexity is to be calculated
+l <- "last year at this time i was preparing for a trip to rome"
+# The line is split in to words
+w <- strsplit(l, " ")[[1]]
+# The Perplexity of the sentence is calculated
+p <- mp$calc_perplexity(w)
+# The sentence Perplexity is printed
+print(p)
+#> [1] 1767
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()
+
+## ------------------------------------------------
+## Method `ModelPredictor$predict_word`
+## ------------------------------------------------
+
+# Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("def-model.RDS")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, "rp" = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The model file name
+mfn <- paste0(ed, "/def-model.RDS")
+# ModelPredictor class object is created
+mp <- ModelPredictor$new(mf = mfn, ve = ve)
+# The next word is predicted
+nws <- mp$predict_word("today is", count = 10)
+# The predicted next words are printed
+print(nws)
+#> $found
+#> [1] TRUE
+#> 
+#> $words
+#>  [1] "a"      "the"    "used"   "better" "hard"   "rare"   "to"     "best"  
+#>  [9] "carved" "dry"   
+#> 
+#> $probs
+#>  [1] 0.17460317 0.11111111 0.06349206 0.03174603 0.03174603 0.03174603
+#>  [7] 0.03174603 0.01587302 0.01587302 0.01587302
+#> 
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()
+
+## ------------------------------------------------
+## Method `ModelPredictor$get_word_prob`
+## ------------------------------------------------
+
+# Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("def-model.RDS")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, "rp" = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The model file name
+mfn <- paste0(ed, "/def-model.RDS")
+# ModelPredictor class object is created
+mp <- ModelPredictor$new(mf = mfn, ve = ve)
+# The probability that the next word is "you" given the prev words
+# "how" and "are"
+prob <- mp$get_word_prob(word = "you", pw = c("how", "are"))
+# The probability is printed
+print(prob)
+#> [1] 0.0024581
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()
+
                    +
                    +
                    + + +
                    +

                    Developed by Nadir Latif.

                    +
                    + +
                    +

                    Site built with pkgdown 2.0.1.

                    +
                    + +
                    - - + + + + diff --git a/docs/reference/TPGenerator.html b/docs/reference/TPGenerator.html index 8ac169b..377ef94 100644 --- a/docs/reference/TPGenerator.html +++ b/docs/reference/TPGenerator.html @@ -1,140 +1,64 @@ - - - - - - - -Generates transition probabilities for n-grams — TPGenerator • Word Predictor - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +combined transition probabilities data can be used to implement back-off.">Generates transition probabilities for n-grams — TPGenerator • Word Predictor + Skip to contents + +
                    -
                    -
                    -

                    Generates transition probabilities for n-grams

                    - Source: R/tp-generator.R - +
                    +

                    Generates transition probabilities for n-grams

                    + Source: R/tp-generator.R +
                    TPGenerator.Rd
                    -
                    +

                    It provides a method for generating transition probabilities for the given n-gram size. It also provides a method for generating the combined transition probabilities data for n-gram sizes from 1 to the given size. The @@ -142,9 +66,8 @@

                    Generates transition probabilities for n-grams

                    - -

                    Details

                    - +
                    +

                    Details

                    It provides a method for generating n-gram transition probabilities. It reads n-gram frequencies from an input text file that is generated by the TokenGenerator class.

                    @@ -161,174 +84,186 @@

                    Details completed self contained n-gram model. By combining the transition probabilities of n-grams, back-off may be used to evaluate word probabilities or predict the next word.

                    -

                    Super class

                    - -

                    wordpredictor::Base -> TPGenerator

                    -

                    Methods

                    - +
                    +
                    +

                    Super class

                    +

                    wordpredictor::Base -> TPGenerator

                    +
                    +
                    +

                    Methods

                    -

                    Public methods

                    - - -

                    Inherited methods -
                      -
                    -
                    - -
                    -

                    Method new()

                    +
                    +

                    Public methods

                    + +

                    Inherited methods

                      +

                      Method new()

                      It initializes the current obj. It is used to set the -transition probabilities options and verbose option.

                      Usage

                      -

                      TPGenerator$new(opts = list(), ve = 0)

                      - -

                      Arguments

                      -

                      -
                      opts

                      The options for generating the transition probabilities. -

                        -

                      • save_tp. If the data should be saved.

                        • +transition probabilities options and verbose option.

                        +

                        Usage

                        +

                        TPGenerator$new(opts = list(), ve = 0)

                        +
                        + +
                        +

                        Arguments

                        +

                        opts
                        +

                        The options for generating the transition probabilities.

                        • save_tp. If the data should be saved.

                        • n. The n-gram size.

                        • dir. The directory containing the input and output files.

                          • -

                        • format. The format for the output. There are two options.

                            -

                          • plain. The data is stored in plain text.

                            • +

                          • format. The format for the output. There are two options.

                            • plain. The data is stored in plain text.

                            • obj. The data is stored as a R obj.

                            • -

                        +
                      + + +
                      ve
                      +

                      The level of detail in the information messages.

                      -
                      ve

                      The level of detail in the information messages.

                      -

                      -

                      -

                      Method generate_tp()

                      +

                      +
                      + +

                      +

                      Method generate_tp()

                      It first generates the transition probabilities for each n-gram of size from 1 to the given size. The transition probabilities are then combined into a single data frame and saved to the output folder that is given as parameter to the current object.

                      By combining the transition probabilities for all n-gram sizes from 1 to n, back-off can be used to calculate next word probabilities or -predict the next word.

                      Usage

                      -

                      TPGenerator$generate_tp()

                      - -

                      Examples

                      -

                      # Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("n1.RDS", "n2.RDS", "n3.RDS", "n4.RDS")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-# End of environment setup code
-
-# The list of output files
-fns <- c("words", "model-4", "tp2", "tp3", "tp4")
-
-# The TPGenerator object is created
-tp <- TPGenerator$new(opts = list(n = 4, dir = ed), ve = ve)
-# The combined transition probabilities are generated
-tp$generate_tp()
-
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-

                      - -

                      -

                      Method generate_tp_for_n()

                      +predict the next word.

                      +

                      Usage

                      +

                      TPGenerator$generate_tp()

                      +
                      + +
                      +

                      Examples

                      +

                      # Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("n1.RDS", "n2.RDS", "n3.RDS", "n4.RDS")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The list of output files
+fns <- c("words", "model-4", "tp2", "tp3", "tp4")
+
+# The TPGenerator object is created
+tp <- TPGenerator$new(opts = list(n = 4, dir = ed), ve = ve)
+# The combined transition probabilities are generated
+tp$generate_tp()
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()

                      +
                      + + +

                      +

                      Method generate_tp_for_n()

                      It generates the transition probabilities table for the given n-gram size. It first reads n-gram token frequencies from an input text file.

                      It then generates a data frame whose columns are the n-gram prefix, next word and next word frequency. The data frame may be saved to a file as plain text or as a R obj. If n = 1, then the -list of words is saved.

                      Usage

                      -

                      TPGenerator$generate_tp_for_n(n)

                      - -

                      Arguments

                      -

                      -
                      n

                      The n-gram size for which the tp data is generated.

                      - -

                      -

                      -

                      Method clone()

                      -

                      The objects of this class are cloneable with this method.

                      Usage

                      -

                      TPGenerator$clone(deep = FALSE)

                      - -

                      Arguments

                      -

                      -
                      deep

                      Whether to make a deep clone.

                      - -

                      - - - -

                      Examples

                      - 
                      
-## ------------------------------------------------
-## Method `TPGenerator$generate_tp`
-## ------------------------------------------------
-
-# Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("n1.RDS", "n2.RDS", "n3.RDS", "n4.RDS")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-
                      #> [1] "CITATION" "examples" "extdata" 
                      # End of environment setup code
-
-# The list of output files
-fns <- c("words", "model-4", "tp2", "tp3", "tp4")
-
-# The TPGenerator object is created
-tp <- TPGenerator$new(opts = list(n = 4, dir = ed), ve = ve)
-# The combined transition probabilities are generated
-tp$generate_tp()
-
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-
                      -
                      - +list of words is saved.

                      +

                      Usage

                      +

                      TPGenerator$generate_tp_for_n(n)

                      +
                      +

                      Arguments

                      +

                      n
                      +

                      The n-gram size for which the tp data is generated.

                      -
                      -
                      -

                      Developed by Nadir Latif.

                      + +

                      -
                      -

                      Site built with pkgdown 1.6.1.

                      +

                      +

                      Method clone()

                      +

                      The objects of this class are cloneable with this method.

                      +

                      Usage

                      +

                      TPGenerator$clone(deep = FALSE)

                      - -
                      +
                      +

                      Arguments

                      +

                      deep
                      +

                      Whether to make a deep clone.

                      + + +

                      +
                      + +
                      + +
                      + +
                      +

                      Examples

                      + 
                      
+## ------------------------------------------------
+## Method `TPGenerator$generate_tp`
+## ------------------------------------------------
+
+# Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("n1.RDS", "n2.RDS", "n3.RDS", "n4.RDS")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The list of output files
+fns <- c("words", "model-4", "tp2", "tp3", "tp4")
+
+# The TPGenerator object is created
+tp <- TPGenerator$new(opts = list(n = 4, dir = ed), ve = ve)
+# The combined transition probabilities are generated
+tp$generate_tp()
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()
+
                      +
                      +
                      - +
                      +

                      Developed by Nadir Latif.

                      +
                      + +
                      +

                      Site built with pkgdown 2.0.1.

                      +
                      - - +
                      + + + + + diff --git a/docs/reference/TokenGenerator.html b/docs/reference/TokenGenerator.html index 0e4b107..b82e38b 100644 --- a/docs/reference/TokenGenerator.html +++ b/docs/reference/TokenGenerator.html @@ -1,306 +1,236 @@ - - - - - - - -Generates n-grams from text files — TokenGenerator • Word Predictor - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Generates n-grams from text files — TokenGenerator • Word Predictor + Skip to contents + +
                      -
                      -
                      -

                      Generates n-grams from text files

                      - Source: R/token-generator.R - +
                      +

                      Generates n-grams from text files

                      + Source: R/token-generator.R +
                      TokenGenerator.Rd
                      -
                      +

                      It generates n-gram tokens along with their frequencies. The data may be saved to a file in plain text format or as a R object.

                      - -

                      Super class

                      - -

                      wordpredictor::Base -> TokenGenerator

                      -

                      Methods

                      - +
                      +

                      Super class

                      +

                      wordpredictor::Base -> TokenGenerator

                      +
                      +
                      +

                      Methods

                      -

                      Public methods

                      - - -

                      Inherited methods -
                        -
                      -
                      - -
                      -

                      Method new()

                      +
                      +

                      Public methods

                      + +

                      Inherited methods

                        +

                        Method new()

                        It initializes the current obj. It is used to set the file name, -tokenization options and verbose option.

                        Usage

                        -

                        TokenGenerator$new(fn = NULL, opts = list(), ve = 0)

                        +tokenization options and verbose option.

                        +

                        Usage

                        +

                        TokenGenerator$new(fn = NULL, opts = list(), ve = 0)

                        +
                        -

                        Arguments

                        -

                        -
                        fn

                        The path to the input file.

                        +
                        +

                        Arguments

                        +

                        fn
                        +

                        The path to the input file.

                        -
                        opts

                        The options for generating the n-gram tokens. -

                          -

                        • n. The n-gram size.

                          • + +
                          opts
                          +

                          The options for generating the n-gram tokens.

                          • n. The n-gram size.

                          • save_ngrams. If the n-gram data should be saved.

                          • min_freq. All n-grams with frequency less than min_freq are ignored.

                          • line_count. The number of lines to process at a time.

                          • stem_words. If words should be transformed to their stems.

                          • dir. The dir where the output file should be saved.

                            • -

                          • format. The format for the output. There are two options.

                              -

                            • plain. The data is stored in plain text.

                              • +

                            • format. The format for the output. There are two options.

                              • plain. The data is stored in plain text.

                              • obj. The data is stored as a R obj.

                              • -

                          +
                        + + +
                        ve
                        +

                        The level of detail in the information messages.

                        + -
                        ve

                        The level of detail in the information messages.

                        +

                        +
                        -

                        -

                        -

                        Method generate_tokens()

                        +

                        +

                        Method generate_tokens()

                        It generates n-gram tokens and their frequencies from the given file name. The tokens may be saved to a text file as plain text -or a R object.

                        Usage

                        -

                        TokenGenerator$generate_tokens()

                        +or a R object.

                        +

                        Usage

                        +

                        TokenGenerator$generate_tokens()

                        +
                        -

                        Returns

                        +
                        +

                        Returns

                        The data frame containing n-gram tokens along with their frequencies.

                        -

                        Examples

                        -

                        # Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("test-clean.txt")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-# End of environment setup code
-
-# The n-gram size
-n <- 4
-# The test file name
-tfn <- paste0(ed, "/test-clean.txt")
-# The n-gram number is set
-tg_opts <- list("n" = n, "save_ngrams" = TRUE, "dir" = ed)
-# The TokenGenerator object is created
-tg <- TokenGenerator$new(tfn, tg_opts, ve = ve)
-# The n-gram tokens are generated
-tg$generate_tokens()
-
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-

                        - -

                        -

                        Method clone()

                        -

                        The objects of this class are cloneable with this method.

                        Usage

                        -

                        TokenGenerator$clone(deep = FALSE)

                        - -

                        Arguments

                        -

                        -
                        deep

                        Whether to make a deep clone.

                        - -

                        - - - -

                        Examples

                        - 
                        
-## ------------------------------------------------
-## Method `TokenGenerator$generate_tokens`
-## ------------------------------------------------
-
-# Start of environment setup code
-# The level of detail in the information messages
-ve <- 0
-# The name of the folder that will contain all the files. It will be
-# created in the current directory. NULL implies tempdir will be used
-fn <- NULL
-# The required files. They are default files that are part of the
-# package
-rf <- c("test-clean.txt")
-# An object of class EnvManager is created
-em <- EnvManager$new(ve = ve, rp = "./")
-# The required files are downloaded
-ed <- em$setup_env(rf, fn)
-
                        #> [1] "CITATION" "examples" "extdata" 
                        # End of environment setup code
-
-# The n-gram size
-n <- 4
-# The test file name
-tfn <- paste0(ed, "/test-clean.txt")
-# The n-gram number is set
-tg_opts <- list("n" = n, "save_ngrams" = TRUE, "dir" = ed)
-# The TokenGenerator object is created
-tg <- TokenGenerator$new(tfn, tg_opts, ve = ve)
-# The n-gram tokens are generated
-tg$generate_tokens()
-
-# The test environment is removed. Comment the below line, so the
-# files generated by the function can be viewed
-em$td_env()
-
                        -
                        - +
                        +
                        +

                        Examples

                        +

                        # Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("test-clean.txt")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The n-gram size
+n <- 4
+# The test file name
+tfn <- paste0(ed, "/test-clean.txt")
+# The n-gram number is set
+tg_opts <- list("n" = n, "save_ngrams" = TRUE, "dir" = ed)
+# The TokenGenerator object is created
+tg <- TokenGenerator$new(tfn, tg_opts, ve = ve)
+# The n-gram tokens are generated
+tg$generate_tokens()
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()

                        +
                        + + +

                        +

                        Method clone()

                        +

                        The objects of this class are cloneable with this method.

                        +

                        Usage

                        +

                        TokenGenerator$clone(deep = FALSE)

                        +
                        +

                        Arguments

                        +

                        deep
                        +

                        Whether to make a deep clone.

                        -
                        -
                        -

                        Developed by Nadir Latif.

                        + +

                        -
                        -

                        Site built with pkgdown 1.6.1.

                        - -
                        +
                        - +
                        +

                        Examples

                        + 
                        
+## ------------------------------------------------
+## Method `TokenGenerator$generate_tokens`
+## ------------------------------------------------
+
+# Start of environment setup code
+# The level of detail in the information messages
+ve <- 0
+# The name of the folder that will contain all the files. It will be
+# created in the current directory. NULL implies tempdir will be used
+fn <- NULL
+# The required files. They are default files that are part of the
+# package
+rf <- c("test-clean.txt")
+# An object of class EnvManager is created
+em <- EnvManager$new(ve = ve, rp = "./")
+# The required files are downloaded
+ed <- em$setup_env(rf, fn)
+# End of environment setup code
+
+# The n-gram size
+n <- 4
+# The test file name
+tfn <- paste0(ed, "/test-clean.txt")
+# The n-gram number is set
+tg_opts <- list("n" = n, "save_ngrams" = TRUE, "dir" = ed)
+# The TokenGenerator object is created
+tg <- TokenGenerator$new(tfn, tg_opts, ve = ve)
+# The n-gram tokens are generated
+tg$generate_tokens()
+
+# The test environment is removed. Comment the below line, so the
+# files generated by the function can be viewed
+em$td_env()
+
                        +
                        +
                        - - +
                        +

                        Developed by Nadir Latif.

                        +
                        + +
                        +

                        Site built with pkgdown 2.0.1.

                        +
                        + +
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                        Reference

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                        Function reference

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                        All functions

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                        Base

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                        Base class for all other classes

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                        DataAnalyzer

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                        Analyzes input text files and n-gram token files

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                        DataCleaner

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                        Provides data cleaning functionality

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                        DataSampler

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                        Generates data samples from text files

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                        EnvManager

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                        Allows managing the test environment

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                        Model

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                        Represents n-gram models

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                        ModelEvaluator

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                        Evaluates performance of n-gram models

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                        ModelGenerator

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                        Generates n-gram models from a text file

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                        ModelPredictor

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                        Allows predicting text, calculating word probabilities and Perplexity

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                        TPGenerator

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                        Generates transition probabilities for n-grams

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                        TokenGenerator

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                        Generates n-grams from text files

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                        Developed by Nadir Latif.

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                        + + Base +
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                        Base class for all other classes
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                        + + DataAnalyzer +
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                        Analyzes input text files and n-gram token files
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                        + + DataCleaner +
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                        Provides data cleaning functionality
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                        Generates data samples from text files
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                        + + EnvManager +
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                        Allows managing the test environment
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                        + + Model +
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                        Represents n-gram models
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                        + + ModelEvaluator +
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                        Evaluates performance of n-gram models
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                        + + ModelGenerator +
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                        Generates n-gram models from a text file
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                        + + ModelPredictor +
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                        Allows predicting text, calculating word probabilities and Perplexity
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                        + + TPGenerator +
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                        Generates transition probabilities for n-grams
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                        + + TokenGenerator +
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                        Generates n-grams from text files
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                        Developed by Nadir Latif.

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                        Site built with pkgdown 1.6.1.

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                        Site built with pkgdown 2.0.1.

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                        + - - - + diff --git a/docs/reference/wordpredictor-package.html b/docs/reference/wordpredictor-package.html index 21d5764..3aff55c 100644 --- a/docs/reference/wordpredictor-package.html +++ b/docs/reference/wordpredictor-package.html @@ -1,194 +1,91 @@ - - - - - - - -wordpredictor: Develop Text Prediction Models Based on N-Grams — wordpredictor-package • Word Predictor - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +wordpredictor: Develop Text Prediction Models Based on N-Grams — wordpredictor-package • Word Predictor + Skip to contents + +
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                        wordpredictor: Develop Text Prediction Models Based on N-Grams

                        - Source: R/wordpredictor-package.R - +
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                        wordpredictor: Develop Text Prediction Models Based on N-Grams

                        + Source: R/wordpredictor-package.R +
                        wordpredictor-package.Rd
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                        A framework for developing n-gram models for text prediction. - It provides data cleaning, data sampling, extracting tokens from text, - model generation, model evaluation and word prediction. For information on how n-gram models - work we referred to: "Speech and Language Processing" - <https://web.stanford.edu/~jurafsky/slp3/3.pdf>. For optimizing R code and - using R6 classes we referred to "Advanced R" - <https://adv-r.hadley.nz/r6.html>. For writing R extensions we referred to - "R Packages", <https://r-pkgs.org/index.html>.

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                        A framework for developing n-gram models for text prediction. It provides data cleaning, data sampling, extracting tokens from text, model generation, model evaluation and word prediction. For information on how n-gram models work we referred to: "Speech and Language Processing" <https://web.stanford.edu/~jurafsky/slp3/3.pdf>. For optimizing R code and using R6 classes we referred to "Advanced R" <https://adv-r.hadley.nz/r6.html>. For writing R extensions we referred to "R Packages", <https://r-pkgs.org/index.html>.

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                        See also

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                        Useful links:

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                        Author

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                        Maintainer: Nadir Latif pakjiddat@gmail.com (ORCID)

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                        See also

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                        Useful links:

                        - -
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                        Author

                        - -

                        Maintainer: Nadir Latif pakjiddat@gmail.com (ORCID)

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                        -
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                        Developed by Nadir Latif.

                        +
                        +

                        Developed by Nadir Latif.

                        -
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                        Site built with pkgdown 1.6.1.

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                        Site built with pkgdown 2.0.1.

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                        + - - - + diff --git a/docs/search.json b/docs/search.json new file mode 100644 index 0000000..edf2f09 --- /dev/null +++ b/docs/search.json @@ -0,0 +1 @@ +[{"path":"https://pakjiddat.github.io/word-predictor/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2021 Nadir Latif Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/articles/features.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Features","text":"document describes features provided wordpredictor package. first describes generate n-gram models. Next describes evaluate performance n-gram models. Finally describes make word predictions using n-gram model.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/articles/features.html","id":"model-generation","dir":"Articles","previous_headings":"","what":"Model Generation","title":"Features","text":"wordpredictor package provides several classes can used generate n-gram models. classes may used generate n-gram models step step. alternative use ModelGenerator class combines steps provides single method generating n-gram models. following steps involved generating n-gram models:","code":""},{"path":"https://pakjiddat.github.io/word-predictor/articles/features.html","id":"data-exploration","dir":"Articles","previous_headings":"Model Generation","what":"Data Exploration","title":"Features","text":"first step generating n-gram model data exploration. involves determining type textual content various text related statistics. type text may news content, blog posts, Twitter feeds, product reviews, customer chat history etc. Example text related statistics line count, word count, average line length input file size. also important determine unwanted words symbols data vulgar words, punctuation symbols, non-alphabetical symbols etc. wordpredictor package provides DataAnalyzer class can used find statistics input data. following example shows get statistics text files within folder: word count text file can fetched using command: cat file-name | wc -w. command work Unix based systems.","code":"# The required files rf <- c( \"test.txt\", \"validate.txt\", \"validate-clean.txt\", \"test-clean.txt\" ) # The test environment is setup ed <- setup_env(rf, ve) # The DataAnalyzer object is created da <- DataAnalyzer$new(ve = ve) # Information on all text files in the ed folder is returned fi <- da$get_file_info(ed) # The file information is printed print(fi) #> $file_stats #> fn total_lc max_ll min_ll mean_ll size #> 1 /tmp/RtmpoZ8ia5/test-clean.txt 73 50 25 39 2.8 Kb #> 2 /tmp/RtmpoZ8ia5/test.txt 73 51 28 41 3 Kb #> 3 /tmp/RtmpoZ8ia5/validate-clean.txt 75 48 10 37 2.8 Kb #> 4 /tmp/RtmpoZ8ia5/validate.txt 73 50 31 40 2.9 Kb #> #> $overall_stats #> total_lc max_ll min_ll mean_ll total_s #> 1 294 51 31 41 11.5 Kb # The test environment is cleaned up clean_up(ve)"},{"path":"https://pakjiddat.github.io/word-predictor/articles/features.html","id":"data-sampling","dir":"Articles","previous_headings":"Model Generation","what":"Data Sampling","title":"Features","text":"next step generate training, testing validation samples input text file. many input text files, can combined single file using command: cat file-1 file-2 file3 > output-file. contents combined text file may need randomized. wordpredictor package provides DataSampler class can used generate random sample containing given number lines. following example shows generate random sample size 10 Mb input text file: Usually need train data set generating n-gram model. test data set testing model validation data set evaluating performance model. following example shows generate train, test validation files. train file contains first 80% lines, test set contains next 10% lines. remaining lines validation set. data validation file must different data train file. Otherwise can result -fitting model. model -fitted, model evaluation results exaggerated, overly optimistic unreliable. care taken ensure data validation train files different. example, dir parameter directory containing input.txt file generated test, validation train data files.","code":"# The required files rf <- c(\"input.txt\") # The test environment is setup ed <- setup_env(rf, ve) # The sample size as a proportion of the input.txt file ssize <- 0.1 # The data file path dfp <- paste0(ed, \"/input.txt\") # The object size is formatted obj_size <- file.size(dfp)/10^6 # The proportion of data to sample prop <- (ssize/obj_size) # An object of class DataSampler is created ds <- DataSampler$new(dir = ed, ve = ve) # The sample file is generated. # The randomized sample is saved to the file train.txt in the ed folder ds$generate_sample( fn = \"input.txt\", ss = prop, ic = F, ir = T, ofn = \"train.txt\", is = T ) # The test environment is cleaned up clean_up(ve) # The required files rf <- c(\"input.txt\") # The test environment is setup ed <- setup_env(rf, ve) # An object of class DataSampler is created ds <- DataSampler$new(dir = ed, ve = ve) # The train, test and validation files are generated ds$generate_data( fn = \"input.txt\", percs = list( \"train\" = 0.8, \"test\" = 0.1, \"validate\" = 0.1 ) ) # The test environment is cleaned up clean_up(ve)"},{"path":"https://pakjiddat.github.io/word-predictor/articles/features.html","id":"data-cleaning","dir":"Articles","previous_headings":"Model Generation","what":"Data Cleaning","title":"Features","text":"next step remove unwanted symbols words input text file. reduces memory requirement n-gram model makes efficient. Example unwanted words vulgar words, words part vocabulary, punctuation, numbers, non-printable characters extra spaces. wordpredictor package provides DataCleaner class can used remove unwanted words symbols text files. following example shows clean given text file: clean_file method reads certain number lines time, cleans lines text saves output text file. can used cleaning large text files.","code":"# The required files rf <- c(\"input.txt\") # The test environment is setup ed <- setup_env(rf, ve) # The data file path fn <- paste0(ed, \"/input.txt\") # The clean file path cfn <- paste0(ed, \"/input-clean.txt\") # The data cleaning options dc_opts = list( \"min_words\" = 2, \"to_lower\" = T, \"remove_stop\" = F, \"remove_punct\" = T, \"remove_non_dict\" = T, \"remove_non_alpha\" = T, \"remove_extra_space\" = T, \"remove_bad\" = F, \"output_file\" = cfn ) # The data cleaner object is created dc <- DataCleaner$new(fn, dc_opts, ve = ve) # The sample file is cleaned and saved as input-clean.txt in the ed dir dc$clean_file() # The test environment is cleaned up clean_up(ve)"},{"path":"https://pakjiddat.github.io/word-predictor/articles/features.html","id":"tokenization","dir":"Articles","previous_headings":"Model Generation","what":"Tokenization","title":"Features","text":"next step generate n-gram tokens cleaned text file. TokenGenerator class allows generating n-gram tokens given size given input text file. following example shows generate n-grams tokens size 1,2,3 4: code generates files n1.RDS, n2.RDS, n3.RDS n4.RDS data directory. files contains n-gram tokens along frequencies. N-grams larger size provide context. Usually n-grams size 4 generated. Two important customization options supported TokenGenerator class min_freq stem_words. min_freq sets minimum frequency n-gram tokens. n-gram tokens frequency less min_freq excluded. stem_words option used transform n-gram prefix components stems. next word transformed. n-gram token frequencies may analyzed using DataAnalyzer class. following example displays top occurring 2-gram tokens: following example shows distribution word frequencies: following example returns top 10 2-gram tokens start and_:","code":"# The required files rf <- c(\"test-clean.txt\") # The test environment is setup ed <- setup_env(rf, ve) # The test file path fn <- paste0(ed, \"/test-clean.txt\") # The n-grams are generated for (n in 1:4) { # The ngram number is set tg_opts = list(\"n\" = n, \"save_ngrams\" = T, dir = ed) # The TokenGenerator object is created tg <- TokenGenerator$new(fn, tg_opts, ve = ve) # The ngram tokens are generated tg$generate_tokens() } # The test environment is cleaned up clean_up(ve) # The required files rf <- c(\"n2.RDS\") # The test environment is setup ed <- setup_env(rf, ve) # The ngram file name fn <- paste0(ed, \"/n2.RDS\") # The DataAnalyzer object is created da <- DataAnalyzer$new(fn, ve = ve) # The top features plot is checked df <- da$plot_n_gram_stats(opts = list( \"type\" = \"top_features\", \"n\" = 10, \"save_to\" = \"png\", \"dir\" = \"./reference/figures\" )) # The output file path fn <- paste0(\"./reference/figures/top_features.png\") knitr::include_graphics(fn) # The test environment is cleaned up clean_up(ve) # The required files rf <- c(\"n2.RDS\") # The test environment is setup ed <- setup_env(rf, ve) # The ngram file name fn <- paste0(ed, \"/n2.RDS\") # The DataAnalyzer object is created da <- DataAnalyzer$new(fn, ve = ve) # The top features plot is checked df <- da$plot_n_gram_stats(opts = list( \"type\" = \"coverage\", \"n\" = 10, \"save_to\" = \"png\", \"dir\" = \"./reference/figures\" )) # The output file path fn <- paste0(\"./reference/figures/coverage.png\") knitr::include_graphics(fn) # The test environment is cleaned up clean_up(ve) # The required files rf <- c(\"n2.RDS\") # The test environment is setup ed <- setup_env(rf, ve) # The ngram file name fn <- paste0(ed, \"/n2.RDS\") # The DataAnalyzer object is created da <- DataAnalyzer$new(ve = ve) # Bi-grams starting with \"and_\" are returned df <- da$get_ngrams(fn = fn, c = 10, pre = \"^and_*\") # The data frame is sorted by frequency df <- df[order(df$freq, decreasing = T),] # The first 10 rows of the data frame are printed knitr::kable(df[1:10,], col.names = c(\"Prefix\", \"Frequency\")) # The test environment is cleaned up clean_up(ve)"},{"path":"https://pakjiddat.github.io/word-predictor/articles/features.html","id":"transition-probabilities","dir":"Articles","previous_headings":"Model Generation","what":"Transition Probabilities","title":"Features","text":"next step generating n-gram model generate transition probabilities (tp) n-gram files. TPGenerator class used generate tps. n-gram token file corresponding tp file generated. tp files combined single file containing tp data n-grams size 1, 2, 3, 4 etc. following example shows generate combined tps n-grams size 1, 2, 3 4: code produces file model-4.RDS.","code":"# The required files rf <- c(\"n1.RDS\", \"n2.RDS\", \"n3.RDS\", \"n4.RDS\") # The test environment is setup ed <- setup_env(rf, ve) # The TPGenerator object is created tp <- TPGenerator$new(opts = list(n = 4, dir = ed), ve = ve) # The combined transition probabilities are generated tp$generate_tp() # The test environment is cleaned up clean_up(ve)"},{"path":"https://pakjiddat.github.io/word-predictor/articles/features.html","id":"the-model-file","dir":"Articles","previous_headings":"Model Generation","what":"The model file","title":"Features","text":"final step generate n-gram model file files generated previous steps. Model class contains method load_model, reads combined tps files files used model. instance Model class represents n-gram model.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/articles/features.html","id":"generating-the-model-in-one-step","dir":"Articles","previous_headings":"Model Generation","what":"Generating the model in one step","title":"Features","text":"previous steps may combined single step. ModelGenerator class allows generating final n-gram model using single method call. following example generates n-gram model using default data cleaning tokenization options:","code":"# The required files rf <- c(\"input.txt\") # The test environment is setup ed <- setup_env(rf, ve) # The following code generates n-gram model using default options for data # cleaning and tokenization. See the following section on how to customize these # options. Note that input.txt is the name of the input data file. It should be # present in the data directory. dir is the directory containing the input and output files. It is set to the path of the environment directory, ed. # ModelGenerator class object is created mg <- ModelGenerator$new( name = \"def-model\", desc = \"N-gram model generating using default options\", fn = \"def-model.RDS\", df = \"input.txt\", n = 4, ssize = 0.1, dir = ed, dc_opts = list(), tg_opts = list(), ve = ve ) # Generates n-gram model. The output is the file def-model.RDS mg$generate_model() # The test environment is cleaned up clean_up(ve)"},{"path":"https://pakjiddat.github.io/word-predictor/articles/features.html","id":"evaluating-the-model-performance","dir":"Articles","previous_headings":"","what":"Evaluating the model performance","title":"Features","text":"wordpredictor package provides ModelEvaluator class evaluating performance generated n-gram model. Intrinsic Extrinsic evaluation supported. Also performance several n-gram models may compared. following example performs Intrinsic evaluation. measures Perplexity score sentence validation.txt file, generated data sampling step. returns minimum, mean maximum Perplexity score line. following example performs Extrinsic evaluation. measures accuracy score sentence validation.txt file. sentence model used predict last word sentence given previous words. last word correctly predicted, prediction considered accurate.","code":"# The required files rf <- c(\"def-model.RDS\", \"validate-clean.txt\") # The test environment is setup ed <- setup_env(rf, ve) # The model file name mfn <- paste0(ed, \"/def-model.RDS\") # The path to the cleaned validation file vfn <- paste0(ed, \"/validate-clean.txt\") # ModelEvaluator class object is created me <- ModelEvaluator$new(mf = mfn, ve = ve) # The intrinsic evaluation is performed on first 20 lines stats <- me$intrinsic_evaluation(lc = 20, fn = vfn) # The test environment is cleaned up clean_up(ve) # The required files rf <- c(\"def-model.RDS\", \"validate-clean.txt\") # The test environment is setup ed <- setup_env(rf, ve) # The model file name mfn <- paste0(ed, \"/def-model.RDS\") # The path to the cleaned validation file vfn <- paste0(ed, \"/validate-clean.txt\") # ModelEvaluator class object is created me <- ModelEvaluator$new(mf = mfn, ve = ve) # The intrinsic evaluation is performed on first 100 lines stats <- me$extrinsic_evaluation(lc = 100, fn = vfn) # The test environment is cleaned up clean_up(ve)"},{"path":"https://pakjiddat.github.io/word-predictor/articles/features.html","id":"making-word-predictions","dir":"Articles","previous_headings":"","what":"Making word predictions","title":"Features","text":"n-gram model generated previous step can used predict next word given set words. following example shows predict next word. returns 3 possible next words along probabilities.","code":"# The required files rf <- c(\"def-model.RDS\") # The test environment is setup ed <- setup_env(rf, ve) # The model file name mfn <- paste0(ed, \"/def-model.RDS\") # An object of class ModelPredictor is created. The mf parameter is the name of # the model file that was generated in the previous example. mp <- ModelPredictor$new(mf = mfn, ve = ve) # Given the words: \"how are\", the next word is predicted. The top 3 most likely # next words are returned along with their respective probabilities. res <- mp$predict_word(words = \"how are\", 3) # The test environment is cleaned up clean_up(ve)"},{"path":"https://pakjiddat.github.io/word-predictor/articles/overview.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Overview","text":"document describes theory behind n-gram models generated wordpredictor package. also provides code examples describe use package. goal wordpredictor package provide flexible easy use framework generating n-gram models word prediction. package allows generating n-gram models. also allows exploring n-gram frequencies using plots. Additionally provides methods measuring n-gram model performance using Perplexity accuracy. n-gram model may customized using several options n-gram size, data cleaning options options converting text tokens.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/articles/overview.html","id":"how-the-model-works","dir":"Articles","previous_headings":"","what":"How the model works","title":"Overview","text":"n-gram model generated wordpredictor package uses Markov model approximating language model. means probability word depends probability n-1 previous words. Maximum Likelihood Estimation (MLE) used calculate probability word. probability word calculated regarding word last component n-gram. total number occurrences n-gram divided total number occurrences (n-1)-gram. gives probability word. n-gram model generated steps. first step, input data cleaned. Unwanted symbols words removed input data. next step, cleaned data file read. N-grams extracted file, starting 1-grams configured n-gram size. 1-gram, 2-gram, 3-gram etc tokens saved separate files along frequency. 3-gram file contains extracted 3-grams respective frequencies. next step generate transition probability tables n-gram file. 1-gram file transition probability table simply list unique words along word frequencies. n-gram files, transition probability table data frame 3 columns. hash n-gram prefixes, next word id next word probability. n-gram prefix set n-1 components last component. n-1 components combined using “_” converted numeric hash value using digest2Int method digest package. next word id numeric index next word list 1-grams. next word probability probability next word given previous n-1 words. calculated using Maximum Likelihood Estimation (MLE) described . Instead storing n-gram prefix strings, single number saved. Also instead storing next word, numeric index next word saved. saves lot memory allows data stored, improves n-gram model’s efficiency. R, number requires fixed amount storage, 56 bytes. contrast memory required store string increases number characters string. data frames represent transition probability table combined single data frame. combined transition probability table used make word predictions.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/articles/overview.html","id":"using-the-model-to-predict-words","dir":"Articles","previous_headings":"","what":"Using the model to predict words","title":"Overview","text":"predict word, word along n-1 previous words used input. model computes hash previous words looks hash combined transition probabilities table. hash found, model extracts top 3 next word ids highest probabilities. model looks next word text corresponds next word ids. result top 3 likely next words along probabilities. hash found, hash n-2 previous words calculated looked combined transition probabilities table. process repeated previous words. happens, model returns “word found” message. method checking transition probabilities lower level n-grams called back-. alternate method predicting word use interpolation. involves weighing summing probabilities n-gram size.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/articles/overview.html","id":"predicting-the-model-performance","dir":"Articles","previous_headings":"","what":"Predicting the model performance","title":"Overview","text":"wordpredictor package provides methods performing intrinsic extrinsic evaluation n-gram model. wordpredictor package performs intrinsic evaluation calculating mean Perplexity score sentences validation text file. Perplexity sentence calculated taking N-th root inverse product probabilities words sentence. N number words sentence. probability word calculated considering n-1 words word. word found transition probabilities table, n-2 words looked . process repeated previous words. word found 1-gram list, probability word calculated simply dividing number times word occurs total number words. word found 1-gram list, model uses default probability probability word. default probability calculated using Laplace Smoothing. Laplace Smoothing involves adding 1 frequency count word vocabulary. Essentially means total number words data set increased vc, vc number words vocabulary. Laplace Smoothing 1 added word count. Since unknown word occurs zero times, Laplace Smoothing count 1. default probability calculated : P(unk) = 1/(N+VC), N total number words data set VC number words vocabulary. default probability assigned unknown words. Alternative methods Laplace Smoothing Add-k smoothing, Kneser-Ney smoothing Good-Turing Smoothing. wordpredictor package uses file /usr/share/dict/cracklib-small dictionary file. file pre-installed Linux distributions. Extrinsic evaluation involves calculating accuracy score. model tries predict last word sentence. actual last word one 3 words predicted model, prediction considered accurate. accuracy score number sentences correctly predicted.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/articles/overview.html","id":"generating-the-model","dir":"Articles","previous_headings":"","what":"Generating the model","title":"Overview","text":"ModelGenerator class allows generating final n-gram model using single method call. following example generates n-gram model using default data cleaning tokenization options:","code":"# The required files rf <- c(\"input.txt\") # The test environment is setup ed <- setup_env(rf, ve) # The following code generates n-gram model using default options for data # cleaning and tokenization. See the following section on how to customize these # options. Note that input.txt is the name of the input data file. It should be # present in the ed directory. The generated model file is also placed in this # directory. # ModelGenerator class object is created mg <- ModelGenerator$new( name = \"def-model\", desc = \"N-gram model generating using default options\", fn = \"def-model.RDS\", df = \"input.txt\", n = 4, ssize = 0.1, dir = ed, dc_opts = list(), tg_opts = list(), ve = ve ) # Generates n-gram model. The output is the file # ./data/model/def-model.RDS mg$generate_model() # The test environment is cleaned up clean_up(ve)"},{"path":"https://pakjiddat.github.io/word-predictor/articles/overview.html","id":"evaluating-the-model-performance","dir":"Articles","previous_headings":"","what":"Evaluating the model performance","title":"Overview","text":"wordpredictor package provides ModelEvaluator class evaluating performance generated n-gram model. following example performs intrinsic evaluation. measures Perplexity score sentence validation.txt file. returns minimum, mean maximum Perplexity score line. following example performs extrinsic evaluation. measures accuracy score sentence validation.txt file. sentence model used predict last word sentence given previous words. last word correctly predicted, prediction considered accurate. extrinsic evaluation returns number correct incorrect predictions.","code":"# The required files rf <- c(\"def-model.RDS\", \"validate-clean.txt\") # The test environment is setup ed <- setup_env(rf, ve) # The model file name mfn <- paste0(ed, \"/def-model.RDS\") # The path to the cleaned validation file vfn <- paste0(ed, \"/validate-clean.txt\") # ModelEvaluator class object is created me <- ModelEvaluator$new(mf = mfn, ve = ve) # The intrinsic evaluation is performed on first 20 lines stats <- me$intrinsic_evaluation(lc = 20, fn = vfn) # The test environment is cleaned up clean_up(ve) # The required files rf <- c(\"def-model.RDS\", \"validate-clean.txt\") # The test environment is setup ed <- setup_env(rf, ve) # The model file name mfn <- paste0(ed, \"/def-model.RDS\") # The path to the cleaned validation file vfn <- paste0(ed, \"/validate-clean.txt\") # ModelEvaluator class object is created me <- ModelEvaluator$new(mf = mfn, ve = ve) # The intrinsic evaluation is performed on first 100 lines stats <- me$extrinsic_evaluation(lc = 100, fn = vfn) # The test environment is cleaned up clean_up(ve)"},{"path":"https://pakjiddat.github.io/word-predictor/articles/overview.html","id":"how-to-predict-a-word","dir":"Articles","previous_headings":"","what":"How to predict a word","title":"Overview","text":"following example shows predict next word. returns 3 possible next words along probabilities.","code":"# The required files rf <- c(\"def-model.RDS\", \"validate-clean.txt\") # The test environment is setup ed <- setup_env(rf, ve) # The model file name mfn <- paste0(ed, \"/def-model.RDS\") # An object of class ModelPredictor is created. The mf parameter is the name of # the model file that was generated in the previous example. mp <- ModelPredictor$new(mf = mfn, ve = ve) # Given the words: \"how are\", the next word is predicted. The top 3 most likely # next words are returned along with their respective probabilities. res <- mp$predict_word(words = \"how are\", 3) # The test environment is cleaned up clean_up(ve)"},{"path":"https://pakjiddat.github.io/word-predictor/articles/overview.html","id":"demo","dir":"Articles","previous_headings":"","what":"Demo","title":"Overview","text":"wordpredictor package includes demo called “word-predictor.” demo Shiny application displays ten likely words given set words. access demo, run following command R shell: demo(\"word-predictor\", package = \"wordpredictor\", ask = F).","code":""},{"path":"https://pakjiddat.github.io/word-predictor/articles/overview.html","id":"package-dependencies","dir":"Articles","previous_headings":"","what":"Package dependencies","title":"Overview","text":"wordpredictor package uses following packages: digest, dply, ggplot2, R6, testthat stingr following packages useful package development: quanteda, tm hash lintr styler pkgdown pryr,","code":""},{"path":"https://pakjiddat.github.io/word-predictor/articles/overview.html","id":"useful-links","dir":"Articles","previous_headings":"","what":"Useful Links","title":"Overview","text":"following articles tutorials useful: N-Gram Model Probability Smoothing Natural Language Processing Natural Language Processing Fun! Quanteda Tutorials","code":""},{"path":[]},{"path":"https://pakjiddat.github.io/word-predictor/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Nadir Latif. Author, maintainer.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Latif N (2021). Develop Text Prediction Models Based N-Grams. R package version 1.0.0, https://github.com/pakjiddat/word-predictor.","code":"@Manual{, title = {Develop Text Prediction Models Based on N-Grams}, author = {Nadir Latif}, year = {2021}, note = {R package version 1.0.0}, url = {https://github.com/pakjiddat/word-predictor}, }"},{"path":"https://pakjiddat.github.io/word-predictor/index.html","id":"introduction","dir":"","previous_headings":"","what":"Develop Text Prediction Models Based on N-Grams","title":"Develop Text Prediction Models Based on N-Grams","text":"goal wordpredictor package provide flexible easy use framework generating n-gram models word prediction. package allows generating n-gram models input text files. also allows exploring n-grams using plots. Additionally provides methods measuring n-gram model performance using Perplexity accuracy. n-gram model may customized using several options n-gram size, data cleaning options options tokenization.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Develop Text Prediction Models Based on N-Grams","text":"can install released version wordpredictor CRAN : development version GitHub :","code":"install.packages(\"wordpredictor\") # install.packages(\"devtools\") devtools::install_github(\"pakjiddat/word-predictor\")"},{"path":"https://pakjiddat.github.io/word-predictor/index.html","id":"package-structure","dir":"","previous_headings":"","what":"Package structure","title":"Develop Text Prediction Models Based on N-Grams","text":"wordpredictor package based R6 classes. easy customize improve. provides following classes: DataAnalyzer. allows analyzing n-grams. DataCleaner. allows cleaning text files. supports several data cleaning options. DataSampler. allows generating sample files specified size text file. also allows generating train, test validation sample files given input text file. TokenGenerator. allows generating n-gram tokens given size. TPGenerator. allows generating transition probabilities given n-gram file. ModelGenerator. allows generating n-gram models using different configuration options. ModelEvaluator. allows evaluating performance n-gram models. 4 metrics used compare performance. : Perplexity, accuracy, memory time taken. ModelPredictor. allows predicting next word, given set previous words. Base. base class classes. allows provides methods reading writing files processing large text files. Information package can obtained using command line package website. example, command: ?wordpredictor returns information given class works parameter details class method.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/index.html","id":"generating-the-model","dir":"","previous_headings":"","what":"Generating the model","title":"Develop Text Prediction Models Based on N-Grams","text":"following example shows generate n-gram model. code generates file def-model.RDS. file represents n-gram model.","code":"# The required files rf <- c(\"input.txt\") # The test environment is setup ed <- setup_env(rf, ve) # The following code generates n-gram model using default options for data # cleaning and tokenization. See the following section on how to customize these # options. Note that input.txt is the name of the input data file. It should be # present in the data directory. ddir is the data directory. mdir is the model # directory. The output model file, which is def-model.RDS will be placed in # this directory. # ModelGenerator class object is created mg <- ModelGenerator$new( name = \"def-model\", desc = \"N-gram model generating using default options\", fn = \"def-model.RDS\", df = \"input.txt\", n = 4, ssize = 10, dir = ed, dc_opts = list(), tg_opts = list(), ve = ve ) # Generates n-gram model. The output is the file def-model.RDS mg$generate_model() # The test environment is cleaned up clean_up(ve)"},{"path":"https://pakjiddat.github.io/word-predictor/index.html","id":"predicting-words","dir":"","previous_headings":"","what":"Predicting words","title":"Develop Text Prediction Models Based on N-Grams","text":"following example shows predict next word given set words:","code":"# The required files rf <- c(\"def-model.RDS\") # The test environment is setup ed <- setup_env(rf, ve) #> [1] \"CITATION\" \"demo\" \"DESCRIPTION\" \"examples\" \"extdata\" #> [6] \"help\" \"html\" \"INDEX\" \"LICENSE\" \"Meta\" #> [11] \"NAMESPACE\" \"NEWS.md\" \"R\" # The model file name mfn <- paste0(ed, \"/def-model.RDS\") # An object of class ModelPredictor is created. The mf parameter is the name of # the model file that was generated in the previous example. mp <- ModelPredictor$new(mf = mfn) # Given the words: \"how are\", the next word is predicted. The top 3 most likely # next words are returned along with their respective probabilities. res <- mp$predict_word(words = \"how are\", 3) # The test environment is cleaned up clean_up(ve)"},{"path":"https://pakjiddat.github.io/word-predictor/index.html","id":"analyzing-n-grams","dir":"","previous_headings":"","what":"Analyzing N-grams","title":"Develop Text Prediction Models Based on N-Grams","text":"wordpredictor package includes class called DataAnalyzer, can used get idea frequency distribution n-grams model. model generation process described , creates n-gram file model directory. n-gram number less equal n-gram size model, n-gram file generated. example n-gram size model 4. 4 n-gram files generated model folder. files : n1.RDS, n2.RDS, n3.RDS n4.RDS. n2.RDS file contains n-grams size 2. following example plots top 10 occurring bi-grams along frequencies: following example plots n-gram frequency coverage. shows percentage n-grams frequency 1, 2 … 10. following example shows get list bi-grams starting “great_” along frequencies. also shows get frequency bi-gram “great_deal”.","code":"# The required files rf <- c(\"n2.RDS\") # The test environment is setup ed <- setup_env(rf, ve) #> [1] \"CITATION\" \"demo\" \"DESCRIPTION\" \"examples\" \"extdata\" #> [6] \"help\" \"html\" \"INDEX\" \"LICENSE\" \"Meta\" #> [11] \"NAMESPACE\" \"NEWS.md\" \"R\" # The file name fn <- paste0(ed, \"/n2.RDS\") # An object of class DataAnalyzer is created. The fn parameter is the path to # the n-gram file. da <- DataAnalyzer$new(fn = fn) # The top 10 most occurring features are plotted df <- da$plot_n_gram_stats(opts = list( \"type\" = \"top_features\", \"n\" = 10, \"save_to\" = NULL, \"dir\" = NULL )) # The test environment is cleaned up clean_up(ve) # The required files rf <- c(\"n2.RDS\") # The test environment is setup ed <- setup_env(rf, ve) #> [1] \"CITATION\" \"demo\" \"DESCRIPTION\" \"examples\" \"extdata\" #> [6] \"help\" \"html\" \"INDEX\" \"LICENSE\" \"Meta\" #> [11] \"NAMESPACE\" \"NEWS.md\" \"R\" # The file name fn <- paste0(ed, \"/n2.RDS\") # An object of class DataAnalyzer is created. The fn parameter is the path to # the n-gram file. da <- DataAnalyzer$new(fn = fn) # The top 10 most occurring features are plotted df <- da$plot_n_gram_stats(opts = list( \"type\" = \"coverage\", \"n\" = 10, \"save_to\" = NULL, \"dir\" = NULL )) # The test environment is cleaned up clean_up(ve) # The required files rf <- c(\"n2.RDS\") # The test environment is setup ed <- setup_env(rf, ve) #> [1] \"CITATION\" \"demo\" \"DESCRIPTION\" \"examples\" \"extdata\" #> [6] \"help\" \"html\" \"INDEX\" \"LICENSE\" \"Meta\" #> [11] \"NAMESPACE\" \"NEWS.md\" \"R\" # The file name fn <- paste0(ed, \"/n2.RDS\") # An object of class DataAnalyzer is created. The fn parameter is the path to # the n-gram file. da <- DataAnalyzer$new() # Bi-grams starting with \"great_\" are returned df <- da$get_ngrams(fn = fn, c = 10, pre = \"^great_*\") # The data frame is sorted by frequency df <- df[order(df$freq, decreasing = T),] # The frequency of the bi-gram \"great_deal\" f <- as.numeric(df[df$pre == \"great_deal\", \"freq\"]) # The test environment is cleaned up clean_up(ve)"},{"path":"https://pakjiddat.github.io/word-predictor/index.html","id":"customizing-the-n-gram-model","dir":"","previous_headings":"","what":"Customizing the n-gram model","title":"Develop Text Prediction Models Based on N-Grams","text":"dc_opts parameter ModelGenerator class specifies data cleaning options. following code shows data cleaning options default values: tg_opts parameter ModelGenerator class specifies token generation options. following code shows token generation options default values:","code":"# @field dc_opts The options for the data cleaner object. # min_words -> The minimum number of words per sentence. # line_count -> The number of lines to read and clean at a time. # save_data -> If the combined processed lines should be saved. # output_file -> Name of the output file used to store the data. # sw_file -> The stop words file path. # dict_file -> The dictionary file path. # bad_file -> The bad words file path. # to_lower -> If the words should be converted to lower case. # remove_stop -> If stop words should be removed. # remove_punct -> If punctuation symbols should be removed. # remove_non_dict -> If non dictionary words should be removed. # remove_non_alpha -> If non alphabet symbols should be removed. # remove_extra_space -> If leading, trailing and double spaces # should be removed. # remove_bad -> If bad words should be removed dc_opts = list( \"min_words\" = 2, \"line_count\" = 1000, \"save_data\" = T, \"output_file\" = NULL, \"sw_file\" = NULL, \"dict_file\" = NULL, \"bad_file\" = NULL, \"to_lower\" = T, \"remove_stop\" = F, \"remove_punct\" = T, \"remove_non_dict\" = T, \"remove_non_alpha\" = T, \"remove_extra_space\" = T, \"remove_bad\" = F ) # @field tg_opts The options for the token generator obj. # min_freq -> All ngrams with frequency less than min_freq are # ignored. # stem_words -> If words should be transformed to their stems. tg_opts = list( \"stem_words\" = F, \"min_freq\" = -1 )"},{"path":"https://pakjiddat.github.io/word-predictor/index.html","id":"evaluating-model-performance","dir":"","previous_headings":"","what":"Evaluating model performance","title":"Develop Text Prediction Models Based on N-Grams","text":"wordpredictor package allows evaluating n-gram model performance. can measure performance single model well compare performance multiple models. evaluating performance model, intrinsic extrinsic evaluation performed. Intrinsic evaluation measures Perplexity score sentence validation text file. returns minimum, maximum mean Perplexity score sentences. Extrinsic evaluation measures accuracy score sentences validation text file. tries predict last word sentence. word correctly predicted, accuracy count increased. extrinsic evaluation returns number valid invalid predictions. following example shows evaluate performance model:","code":"# The required files rf <- c(\"def-model.RDS\", \"validate.txt\") # The test environment is setup ed <- setup_env(rf, ve) # The model file name mfn <- paste0(ed, \"/def-model.RDS\") # The validation file name vfn <- paste0(ed, \"/validate.txt\") # ModelEvaluator class object is created me <- ModelEvaluator$new(mf = mfn, ve = 2) # The performance evaluation is performed. The performance stats are returned as # a data frame and also saved within the model file itself. stats <- me$evaluate_performance(lc = 20, fn = vfn) # The test environment is cleaned up clean_up(ve)"},{"path":"https://pakjiddat.github.io/word-predictor/index.html","id":"demo","dir":"","previous_headings":"","what":"Demo","title":"Develop Text Prediction Models Based on N-Grams","text":"wordpredictor package includes demo called “word-predictor”. demo Shiny application displays ten likely words given set words. access demo, run following command R shell: demo(\"word-predictor\", package = \"wordpredictor\", ask = F). following screenshot demo:","code":""},{"path":"https://pakjiddat.github.io/word-predictor/index.html","id":"website","dir":"","previous_headings":"","what":"Website","title":"Develop Text Prediction Models Based on N-Grams","text":"wordpredictor website provides details package works. includes code samples details classes methods.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/index.html","id":"benefits","dir":"","previous_headings":"","what":"Benefits","title":"Develop Text Prediction Models Based on N-Grams","text":"wordpredictor package provides easy use framework working n-gram models. allows n-gram model generation, performance evaluation word prediction.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/index.html","id":"limitations","dir":"","previous_headings":"","what":"Limitations","title":"Develop Text Prediction Models Based on N-Grams","text":"n-gram language model requires lot memory storing n-grams. wordpredictor package tested machine dual core processor 4 GB RAM. works well input data files size less 40 Mb n-gram size 4. larger data files n-gram size, memory CPU power needed.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/index.html","id":"future-work","dir":"","previous_headings":"","what":"Future Work","title":"Develop Text Prediction Models Based on N-Grams","text":"wordpredictor package may extended adding support different smoothing techniques Good-Turing, Katz-Back-handling Vocabulary Words. Support different types n-gram models Skip-Grams Syntatic n-grams. wordpredictor package used predicting words. may extended support use cases spelling correction, biological sequence analysis, data compression . require performance optimization. source code organized using R6 classes. easy extend. Contributions welcome !.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/index.html","id":"acknowledgments","dir":"","previous_headings":"","what":"Acknowledgments","title":"Develop Text Prediction Models Based on N-Grams","text":"motivated develop wordpredictor package taking courses Data Science Specialization offered John Hopkins university Coursera. like thank course instructors making courses interesting motivating students.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Base.html","id":null,"dir":"Reference","previous_headings":"","what":"Base class for all other classes — Base","title":"Base class for all other classes — Base","text":"Provides basic structure processing text files. Also provides methods reading writing files objects.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Base.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Base class for all other classes — Base","text":"provides pre-processing, processing post-processing methods, need overridden derived classes. pre-processing function called reading file. process function called processing given number lines. post processing function called processed data. Also provides methods reading writing text files R objects. class methods private.","code":""},{"path":[]},{"path":"https://pakjiddat.github.io/word-predictor/reference/Base.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Base class for all other classes — Base","text":"Base$new() Base$clone()","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Base.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Base class for all other classes — Base","text":"initializes current object. used set file name verbose options.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Base.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Base class for all other classes — Base","text":"","code":"Base$new(fn = NULL, lc = 100, ve = 2)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/Base.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Base class for all other classes — Base","text":"fn path file clean. lc number lines read clean time. ve level detail information messages. Reads given file one line time. runs given pre-processing function reading file. runs given","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Base.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Base class for all other classes — Base","text":"objects class cloneable method.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Base.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Base class for all other classes — Base","text":"","code":"Base$clone(deep = FALSE)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/Base.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Base class for all other classes — Base","text":"deep Whether make deep clone.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":null,"dir":"Reference","previous_headings":"","what":"Analyzes input text files and n-gram token files — DataAnalyzer","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"provides method returns information text files, number lines number words. also provides method displays bar plots n-gram frequencies. Additionally provides method searching n-grams n-gram token file. file generated using TokenGenerator class.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"provides method returns text file information. text file information includes total number lines, max, min mean line length file size. also provides method generates bar plot showing common n-gram tokens. Another method provided returns list n-grams match given regular expression.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"wordpredictor::Base -> DataAnalyzer","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"Inherited methods","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"DataAnalyzer$new() DataAnalyzer$plot_n_gram_stats() DataAnalyzer$get_file_info() DataAnalyzer$get_ngrams() DataAnalyzer$clone()","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"initializes current object. used set file name verbose options.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"","code":"DataAnalyzer$new(fn = NULL, ve = 0)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"fn path input file. ve level detail information messages.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"method-plot-n-gram-stats-","dir":"Reference","previous_headings":"","what":"Method plot_n_gram_stats()","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"allows generating two type n-gram plots. first reads n-gram token frequencies input text file. n-gram frequencies displayed bar plot. type plot specified type option. type options can values 'top_features' 'coverage'. 'top_features' displays top n occurring tokens along frequencies. 'coverage' displays number words along frequencies. plot stats returned data frame.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"","code":"DataAnalyzer$plot_n_gram_stats(opts)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"opts options analyzing data. type. type plot display. options : 'top_features', 'coverage'. n. 'top_features', number top occurring tokens. 'coverage' first n frequencies. save_to. graphics devices save plot . NULL implies plot printed. dir. output directory plot saved.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"data frame containing stats.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"","code":"# Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL value implies tempdir will # be used. fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"n2.RDS\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The n-gram file name nfn <- paste0(ed, \"/n2.RDS\") # The DataAnalyzer object is created da <- DataAnalyzer$new(nfn, ve = ve) # The top features plot is checked df <- da$plot_n_gram_stats(opts = list( \"type\" = \"top_features\", \"n\" = 10, \"save_to\" = NULL, \"dir\" = ed )) # N-gram statistics are displayed print(df) # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"method-get-file-info-","dir":"Reference","previous_headings":"","what":"Method get_file_info()","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"generates information text files. takes input file directory containing text files. file calculates total number lines, maximum, minimum mean line lengths total file size. file information returned data frame.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"","code":"DataAnalyzer$get_file_info(res)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"res name directory file name.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"data frame containing text file statistics.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"examples-1","dir":"Reference","previous_headings":"","what":"Examples","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"","code":"# Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"test.txt\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The test file name cfn <- paste0(ed, \"/test.txt\") # The DataAnalyzer object is created da <- DataAnalyzer$new(ve = ve) # The file info is fetched fi <- da$get_file_info(cfn) # The file information is printed print(fi) # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"method-get-ngrams-","dir":"Reference","previous_headings":"","what":"Method get_ngrams()","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"extracts given number n-grams frequencies n-gram token file. prefix parameter specifies regular expression matching n-grams. parameter specified given number n-grams randomly chosen.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"","code":"DataAnalyzer$get_ngrams(fn, c = NULL, pre = NULL)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"fn n-gram file name. c number n-grams return. pre n-gram prefix, given regular expression.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"examples-2","dir":"Reference","previous_headings":"","what":"Examples","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"","code":"# Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"n2.RDS\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The n-gram file name nfn <- paste0(ed, \"/n2.RDS\") # The DataAnalyzer object is created da <- DataAnalyzer$new(nfn, ve = ve) # Bi-grams starting with \"and_\" are returned df <- da$get_ngrams(fn = nfn, c = 10, pre = \"^and_*\") # The data frame is sorted by frequency df <- df[order(df$freq, decreasing = TRUE),] # The data frame is printed print(df) # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"objects class cloneable method.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"","code":"DataAnalyzer$clone(deep = FALSE)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"deep Whether make deep clone.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataAnalyzer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Analyzes input text files and n-gram token files — DataAnalyzer","text":"","code":"## ------------------------------------------------ ## Method `DataAnalyzer$plot_n_gram_stats` ## ------------------------------------------------ # Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL value implies tempdir will # be used. fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"n2.RDS\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The n-gram file name nfn <- paste0(ed, \"/n2.RDS\") # The DataAnalyzer object is created da <- DataAnalyzer$new(nfn, ve = ve) # The top features plot is checked df <- da$plot_n_gram_stats(opts = list( \"type\" = \"top_features\", \"n\" = 10, \"save_to\" = NULL, \"dir\" = ed )) # N-gram statistics are displayed print(df) #> # A tibble: 10 × 2 #> pre freq #> #> 1 in_the 4 #> 2 of_the 4 #> 3 on_the 4 #> 4 to_the 4 #> 5 from_the 3 #> 6 in_a 3 #> 7 a_thin 2 #> 8 and_the 2 #> 9 between_the 2 #> 10 down_the 2 # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env() ## ------------------------------------------------ ## Method `DataAnalyzer$get_file_info` ## ------------------------------------------------ # Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"test.txt\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The test file name cfn <- paste0(ed, \"/test.txt\") # The DataAnalyzer object is created da <- DataAnalyzer$new(ve = ve) # The file info is fetched fi <- da$get_file_info(cfn) # The file information is printed print(fi) #> $file_stats #> fn total_lc max_ll min_ll mean_ll size #> 1 /tmp/RtmpcJbhpv/test.txt 73 51 28 41 3 Kb #> #> $overall_stats #> total_lc max_ll min_ll mean_ll total_s #> 1 73 51 28 41 3 Kb #> # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env() ## ------------------------------------------------ ## Method `DataAnalyzer$get_ngrams` ## ------------------------------------------------ # Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"n2.RDS\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The n-gram file name nfn <- paste0(ed, \"/n2.RDS\") # The DataAnalyzer object is created da <- DataAnalyzer$new(nfn, ve = ve) # Bi-grams starting with \"and_\" are returned df <- da$get_ngrams(fn = nfn, c = 10, pre = \"^and_*\") # The data frame is sorted by frequency df <- df[order(df$freq, decreasing = TRUE),] # The data frame is printed print(df) #> # A tibble: 15 × 2 #> pre freq #> #> 1 and_the 2 #> 2 and_cart 1 #> 3 and_fired 1 #> 4 and_forget 1 #> 5 and_leave 1 #> 6 and_open 1 #> 7 and_out 1 #> 8 and_phrase 1 #> 9 and_say 1 #> 10 and_tea 1 #> 11 and_tell 1 #> 12 and_then 1 #> 13 and_threw 1 #> 14 and_was 1 #> 15 and_watch 1 # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataCleaner.html","id":null,"dir":"Reference","previous_headings":"","what":"Provides data cleaning functionality — DataCleaner","title":"Provides data cleaning functionality — DataCleaner","text":"provides memory efficient method removing unneeded characters text files. suitable cleaning large text files.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataCleaner.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Provides data cleaning functionality — DataCleaner","text":"provides method cleaning text files. allows removing bad words, stop words, non dictionary words, extra space, punctuation non-alphabet characters. also allows conversion lower case. supports large text files.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataCleaner.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Provides data cleaning functionality — DataCleaner","text":"wordpredictor::Base -> DataCleaner","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataCleaner.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Provides data cleaning functionality — DataCleaner","text":"Inherited methods","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataCleaner.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Provides data cleaning functionality — DataCleaner","text":"DataCleaner$new() DataCleaner$clean_file() DataCleaner$clean_lines() DataCleaner$clone()","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataCleaner.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Provides data cleaning functionality — DataCleaner","text":"initializes current object. used set file name verbose options.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataCleaner.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Provides data cleaning functionality — DataCleaner","text":"","code":"DataCleaner$new(fn = NULL, opts = list(), ve = 0)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataCleaner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Provides data cleaning functionality — DataCleaner","text":"fn path file clean. opts options data cleaning. min_words. minimum number words per sentence. line_count. number lines read clean time. save_data. combined processed lines saved. output_file. Name output file used store data. sw_file. stop words file path. dict_file. dictionary file path. bad_file. bad words file path. to_lower. words converted lower case. remove_stop. stop words removed. remove_punct. punctuation symbols removed. remove_non_dict. non dictionary words removed. remove_non_alpha. -> non alphabet symbols removed. remove_extra_space. -> leading, trailing double spaces removed. remove_bad. bad words removed ve level detail information messages.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataCleaner.html","id":"method-clean-file-","dir":"Reference","previous_headings":"","what":"Method clean_file()","title":"Provides data cleaning functionality — DataCleaner","text":"provides efficient method cleaning text files. removes unneeded characters given text file several options. allows removing punctuation, bad words, stop words, non-alphabetical symbols non-dictionary words. reads certain number lines given text file. removes unneeded characters lines saves lines output text file. File cleaning progress displayed verbose option set class constructor. suitable cleaning large text files.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataCleaner.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Provides data cleaning functionality — DataCleaner","text":"","code":"DataCleaner$clean_file()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataCleaner.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Provides data cleaning functionality — DataCleaner","text":"","code":"# Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"test.txt\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The cleaned test file name cfn <- paste0(ed, \"/test-clean.txt\") # The test file name fn <- paste0(ed, \"/test.txt\") # The data cleaning options dc_opts <- list(\"output_file\" = cfn) # The data cleaner object is created dc <- DataCleaner$new(fn, dc_opts, ve = ve) # The sample file is cleaned dc$clean_file() # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataCleaner.html","id":"method-clean-lines-","dir":"Reference","previous_headings":"","what":"Method clean_lines()","title":"Provides data cleaning functionality — DataCleaner","text":"cleans given lines text using options passed current object.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataCleaner.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Provides data cleaning functionality — DataCleaner","text":"","code":"DataCleaner$clean_lines(lines)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataCleaner.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Provides data cleaning functionality — DataCleaner","text":"lines input sentences.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataCleaner.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Provides data cleaning functionality — DataCleaner","text":"cleaned lines text.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataCleaner.html","id":"examples-1","dir":"Reference","previous_headings":"","what":"Examples","title":"Provides data cleaning functionality — DataCleaner","text":"","code":"# The level of detail in the information messages ve <- 0 # Test data is read l <- c( \"If you think I'm wrong, send me a link to where it's happened\", \"We're about 90percent done with this room\", \"This isn't how I wanted it between us.\", \"Almost any cute breed can become ornamental\", \"Once upon a time there was a kingdom with a castle\", \"That's not a thing any of us are granted'\", \"Why are you being so difficult? she asks.\" ) # The expected results res <- c( \"if you think wrong send me a link to where its happened\", \"were about percent done with this room\", \"this how i wanted it between us\", \"almost any cute breed can become ornamental\", \"once upon a time there was a kingdom with a castle\", \"thats not a thing any of us are granted\", \"why are you being so difficult she asks\" ) # The DataCleaner object is created dc <- DataCleaner$new(ve = ve) # The line is cleaned cl <- dc$clean_lines(l) # The cleaned lines are printed print(cl)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataCleaner.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Provides data cleaning functionality — DataCleaner","text":"objects class cloneable method.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataCleaner.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Provides data cleaning functionality — DataCleaner","text":"","code":"DataCleaner$clone(deep = FALSE)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataCleaner.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Provides data cleaning functionality — DataCleaner","text":"deep Whether make deep clone.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataCleaner.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Provides data cleaning functionality — DataCleaner","text":"","code":"## ------------------------------------------------ ## Method `DataCleaner$clean_file` ## ------------------------------------------------ # Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"test.txt\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The cleaned test file name cfn <- paste0(ed, \"/test-clean.txt\") # The test file name fn <- paste0(ed, \"/test.txt\") # The data cleaning options dc_opts <- list(\"output_file\" = cfn) # The data cleaner object is created dc <- DataCleaner$new(fn, dc_opts, ve = ve) # The sample file is cleaned dc$clean_file() # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env() ## ------------------------------------------------ ## Method `DataCleaner$clean_lines` ## ------------------------------------------------ # The level of detail in the information messages ve <- 0 # Test data is read l <- c( \"If you think I'm wrong, send me a link to where it's happened\", \"We're about 90percent done with this room\", \"This isn't how I wanted it between us.\", \"Almost any cute breed can become ornamental\", \"Once upon a time there was a kingdom with a castle\", \"That's not a thing any of us are granted'\", \"Why are you being so difficult? she asks.\" ) # The expected results res <- c( \"if you think wrong send me a link to where its happened\", \"were about percent done with this room\", \"this how i wanted it between us\", \"almost any cute breed can become ornamental\", \"once upon a time there was a kingdom with a castle\", \"thats not a thing any of us are granted\", \"why are you being so difficult she asks\" ) # The DataCleaner object is created dc <- DataCleaner$new(ve = ve) # The line is cleaned cl <- dc$clean_lines(l) # The cleaned lines are printed print(cl) #> [1] \"if you think wrong send me a link to where its happened\" #> [2] \"were about percent done with this room\" #> [3] \"this how i wanted it between us\" #> [4] \"almost any cute breed can become ornamental\" #> [5] \"once upon a time there was a kingdom with a castle\" #> [6] \"thats not a thing any of us are granted\" #> [7] \"why are you being so difficult she asks\""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataSampler.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates data samples from text files — DataSampler","title":"Generates data samples from text files — DataSampler","text":"provides method generating training, testing validation data sets given input text file. also provides method generating sample file given size number lines input text file. contents sample file may cleaned randomized.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataSampler.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Generates data samples from text files — DataSampler","text":"wordpredictor::Base -> DataSampler","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataSampler.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Generates data samples from text files — DataSampler","text":"Inherited methods","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataSampler.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Generates data samples from text files — DataSampler","text":"DataSampler$new() DataSampler$generate_sample() DataSampler$generate_data() DataSampler$clone()","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataSampler.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Generates data samples from text files — DataSampler","text":"initializes current object. used set verbose option.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataSampler.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates data samples from text files — DataSampler","text":"","code":"DataSampler$new(dir = \".\", ve = 0)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataSampler.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates data samples from text files — DataSampler","text":"dir directory storing input output files. ve level detail information messages.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataSampler.html","id":"method-generate-sample-","dir":"Reference","previous_headings":"","what":"Method generate_sample()","title":"Generates data samples from text files — DataSampler","text":"Generates sample file given size given input file. file saved directory given dir object attribute. file generated, contents may cleaned randomized.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataSampler.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates data samples from text files — DataSampler","text":"","code":"DataSampler$generate_sample(fn, ss, ic, ir, ofn, is, dc_opts = NULL)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataSampler.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates data samples from text files — DataSampler","text":"fn input file name. short file name relative dir attribute. ss number lines proportion lines sample. ic sample file cleaned. ir sample file contents randomized. ofn output file name. saved dir. sampled data saved file. dc_opts options cleaning data.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataSampler.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates data samples from text files — DataSampler","text":"","code":"# Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"input.txt\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The sample file name sfn <- paste0(ed, \"/sample.txt\") # An object of class DataSampler is created ds <- DataSampler$new(dir = ed, ve = ve) # The sample file is generated ds$generate_sample( fn = \"input.txt\", ss = 0.5, ic = FALSE, ir = FALSE, ofn = \"sample.txt\", is = TRUE ) # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataSampler.html","id":"method-generate-data-","dir":"Reference","previous_headings":"","what":"Method generate_data()","title":"Generates data samples from text files — DataSampler","text":"generates training, testing validation data sets given input file. first reads file given parameter current object. partitions data training, testing validation sets, according perc parameter. files named train.txt, test.txt va.txt saved given output folder.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataSampler.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates data samples from text files — DataSampler","text":"","code":"DataSampler$generate_data(fn, percs)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataSampler.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates data samples from text files — DataSampler","text":"fn input file name. relative dir attribute. percs size training, testing validation sets.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataSampler.html","id":"examples-1","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates data samples from text files — DataSampler","text":"","code":"# Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be # used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"input.txt\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve) # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The files to clean fns <- c(\"train\", \"test\", \"validate\") # An object of class DataSampler is created ds <- DataSampler$new(dir = ed, ve = ve) # The train, test and validation files are generated ds$generate_data( fn = \"input.txt\", percs = list( \"train\" = 0.8, \"test\" = 0.1, \"validate\" = 0.1 ) ) # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataSampler.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Generates data samples from text files — DataSampler","text":"objects class cloneable method.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataSampler.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates data samples from text files — DataSampler","text":"","code":"DataSampler$clone(deep = FALSE)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataSampler.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates data samples from text files — DataSampler","text":"deep Whether make deep clone.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/DataSampler.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates data samples from text files — DataSampler","text":"","code":"## ------------------------------------------------ ## Method `DataSampler$generate_sample` ## ------------------------------------------------ # Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"input.txt\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The sample file name sfn <- paste0(ed, \"/sample.txt\") # An object of class DataSampler is created ds <- DataSampler$new(dir = ed, ve = ve) # The sample file is generated ds$generate_sample( fn = \"input.txt\", ss = 0.5, ic = FALSE, ir = FALSE, ofn = \"sample.txt\", is = TRUE ) # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env() ## ------------------------------------------------ ## Method `DataSampler$generate_data` ## ------------------------------------------------ # Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be # used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"input.txt\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve) # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The files to clean fns <- c(\"train\", \"test\", \"validate\") # An object of class DataSampler is created ds <- DataSampler$new(dir = ed, ve = ve) # The train, test and validation files are generated ds$generate_data( fn = \"input.txt\", percs = list( \"train\" = 0.8, \"test\" = 0.1, \"validate\" = 0.1 ) ) # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":null,"dir":"Reference","previous_headings":"","what":"Allows managing the test environment — EnvManager","title":"Allows managing the test environment — EnvManager","text":"class provides method creating directories tempdir folder testing purposes. also provides method reading files inst/extdata folder.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Allows managing the test environment — EnvManager","text":"wordpredictor::Base -> EnvManager","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Allows managing the test environment — EnvManager","text":"Inherited methods","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Allows managing the test environment — EnvManager","text":"EnvManager$new() EnvManager$get_data_fn() EnvManager$remove_files() EnvManager$td_env() EnvManager$cp_env() EnvManager$setup_env() EnvManager$clone()","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Allows managing the test environment — EnvManager","text":"initializes current object. simply calls base class constructor.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Allows managing the test environment — EnvManager","text":"","code":"EnvManager$new(rp = \"../../\", ve = 0)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Allows managing the test environment — EnvManager","text":"rp prefix accessing package root folder. ve level detail information messages.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"method-get-data-fn-","dir":"Reference","previous_headings":"","what":"Method get_data_fn()","title":"Allows managing the test environment — EnvManager","text":"Checks given file exists. exist, tries load file inst/extdata data folder package. throws error file found. file exists, method simply returns file name.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Allows managing the test environment — EnvManager","text":"","code":"EnvManager$get_data_fn(fn, dfn)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Allows managing the test environment — EnvManager","text":"fn file name. dfn name default file external data folder package.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Allows managing the test environment — EnvManager","text":"name file exists, full path default file.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"method-remove-files-","dir":"Reference","previous_headings":"","what":"Method remove_files()","title":"Allows managing the test environment — EnvManager","text":"Removes files given directory.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Allows managing the test environment — EnvManager","text":"","code":"EnvManager$remove_files(dn)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Allows managing the test environment — EnvManager","text":"dn directory name.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"method-td-env-","dir":"Reference","previous_headings":"","what":"Method td_env()","title":"Allows managing the test environment — EnvManager","text":"Removes ed folder created setup_env method. Also sets R option, \"ed\" NULL.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Allows managing the test environment — EnvManager","text":"","code":"EnvManager$td_env(rf = F)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"Allows managing the test environment — EnvManager","text":"rf environment folder removed.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"method-cp-env-","dir":"Reference","previous_headings":"","what":"Method cp_env()","title":"Allows managing the test environment — EnvManager","text":"Copies ed folder created setup_env method inst/extdata.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Allows managing the test environment — EnvManager","text":"","code":"EnvManager$cp_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"method-setup-env-","dir":"Reference","previous_headings":"","what":"Method setup_env()","title":"Allows managing the test environment — EnvManager","text":"Copies given files test folder environment folder.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"Allows managing the test environment — EnvManager","text":"","code":"EnvManager$setup_env(fns = c(), cf = NULL)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"Allows managing the test environment — EnvManager","text":"fns list test files copy cf custom environment folder. path relative current directory. specified, tempdir function used generate environment folder.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Allows managing the test environment — EnvManager","text":"list folders can used testing.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Allows managing the test environment — EnvManager","text":"objects class cloneable method.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"usage-6","dir":"Reference","previous_headings":"","what":"Usage","title":"Allows managing the test environment — EnvManager","text":"","code":"EnvManager$clone(deep = FALSE)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/EnvManager.html","id":"arguments-5","dir":"Reference","previous_headings":"","what":"Arguments","title":"Allows managing the test environment — EnvManager","text":"deep Whether make deep clone.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Represents n-gram models — Model","title":"Represents n-gram models — Model","text":"Model class represents n-gram models. instance class single n-gram model. attributes class used store n-gram model information. class provides methods loading saving model.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Represents n-gram models — Model","text":"attributes class used store n-gram model information model name, model description, model file name, n-gram size, transition probabilities data, default probability words, data cleaning tokenization options, word list, model path, data directory path performance stats. model saved single file R object. model file contains information required model. model object used input classes perform operations model evaluation model performance, text predictions comparison model performance.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Model.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Represents n-gram models — Model","text":"wordpredictor::Base -> Model","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Model.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Represents n-gram models — Model","text":"pstats performance stats model. name model name. desc model description.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Model.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Represents n-gram models — Model","text":"Inherited methods","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Model.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Represents n-gram models — Model","text":"Model$new() Model$load_model() Model$get_config() Model$get_size() Model$clone()","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Model.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Represents n-gram models — Model","text":"initializes current object. used set maximum n-gram number, sample size, input file name, data cleaner options, tokenization options, combined transition probabilities file name verbose.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Model.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Represents n-gram models — Model","text":"","code":"Model$new( name = NULL, desc = NULL, fn = NULL, df = NULL, n = 4, ssize = 0.3, dir = \".\", dc_opts = list(), tg_opts = list(), ve = 0 )"},{"path":"https://pakjiddat.github.io/word-predictor/reference/Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Represents n-gram models — Model","text":"name model name. desc model description. fn model file name. df name file used generate model. n maximum n-gram number supported model. ssize sample size proportion input file. dir directory containing model files. dc_opts data cleaner options. tg_opts token generator options. ve level detail information messages.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Model.html","id":"method-load-model-","dir":"Reference","previous_headings":"","what":"Method load_model()","title":"Represents n-gram models — Model","text":"loads model using given information","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Model.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Represents n-gram models — Model","text":"","code":"Model$load_model()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/Model.html","id":"method-get-config-","dir":"Reference","previous_headings":"","what":"Method get_config()","title":"Represents n-gram models — Model","text":"returns given configuration data","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Model.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Represents n-gram models — Model","text":"","code":"Model$get_config(cn)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/Model.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Represents n-gram models — Model","text":"cn name required configuration.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Model.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Represents n-gram models — Model","text":"configuration value.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Model.html","id":"method-get-size-","dir":"Reference","previous_headings":"","what":"Method get_size()","title":"Represents n-gram models — Model","text":"returns size current object. object size calculated sum sizes object attributes.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Model.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Represents n-gram models — Model","text":"","code":"Model$get_size()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/Model.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Represents n-gram models — Model","text":"size object bytes.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Model.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Represents n-gram models — Model","text":"objects class cloneable method.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/Model.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Represents n-gram models — Model","text":"","code":"Model$clone(deep = FALSE)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/Model.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Represents n-gram models — Model","text":"deep Whether make deep clone.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluates performance of n-gram models — ModelEvaluator","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"provides methods performing extrinsic intrinsic evaluation n-gram model. also provides method comparing performance multiple n-gram models. Intrinsic evaluation based calculation Perplexity. Extrinsic evaluation involves determining percentage correct next word predictions.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"performing intrinsic extrinsic model evaluation, validation file must first generated. can done using DataSampler class. line validation file evaluated. intrinsic evaluation Perplexity line calculated. overall summary Perplexity calculations returned. includes min, max mean Perplexity. extrinsic evaluation, next word prediction performed line. actual next word one three predicted next words, prediction considered accurate. extrinsic evaluation returns percentage correct incorrect predictions.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"wordpredictor::Base -> ModelEvaluator","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"Inherited methods","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"ModelEvaluator$new() ModelEvaluator$compare_performance() ModelEvaluator$plot_stats() ModelEvaluator$evaluate_performance() ModelEvaluator$intrinsic_evaluation() ModelEvaluator$extrinsic_evaluation() ModelEvaluator$clone()","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"initializes current object. used set model file name verbose options.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"","code":"ModelEvaluator$new(mf = NULL, ve = 0)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"mf model file name. ve level detail information messages.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"method-compare-performance-","dir":"Reference","previous_headings":"","what":"Method compare_performance()","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"compares performance models given folder. performance model compared 4 metric time taken, memory used, Perplexity accuracy. performance comparison displayed plots. 4 plots displayed. One performance metric. fifth plot shows variation Perplexity accuracy. 5 plots plotted one page.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"","code":"ModelEvaluator$compare_performance(opts)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"opts options comparing model performance. save_to. graphics device save plot . NULL implies plot printed. dir. directory containing model file, plot stats.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"","code":"# Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be # used. fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"def-model.RDS\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # ModelEvaluator class object is created me <- ModelEvaluator$new(ve = ve) # The performance evaluation is performed me$compare_performance(opts = list( \"save_to\" = NULL, \"dir\" = ed )) # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"method-plot-stats-","dir":"Reference","previous_headings":"","what":"Method plot_stats()","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"plots given stats 5 plots. plots displayed single page. 4 performance metrics time taken, memory, Perplexity accuracy plotted model name. Another plot compares Perplexity accuracy model.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"","code":"ModelEvaluator$plot_stats(data)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"data data plot","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"ggplot object returned.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"method-evaluate-performance-","dir":"Reference","previous_headings":"","what":"Method evaluate_performance()","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"performs intrinsic extrinsic evaluation given model validation text file. given number lines validation file used evaluation performs two types evaluations. One intrinsic evaluation, based Perplexity, extrinsic evaluation based accuracy. returns results evaluation. 4 evaluation metrics returned. Perplexity, accuracy, memory time taken. Memory size model object. Time taken time needed performing evaluations. results model evaluation saved within model object also returned.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"","code":"ModelEvaluator$evaluate_performance(lc, fn)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"lc number lines text validation file used evaluation. fn name validation file. exist, default file validation-clean.txt checked models folder","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"performance stats returned.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"examples-1","dir":"Reference","previous_headings":"","what":"Examples","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"","code":"# Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"def-model.RDS\", \"validate-clean.txt\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The model file name mfn <- paste0(ed, \"/def-model.RDS\") # The validation file name vfn <- paste0(ed, \"/validate-clean.txt\") # ModelEvaluator class object is created me <- ModelEvaluator$new(mf = mfn, ve = ve) # The performance evaluation is performed stats <- me$evaluate_performance(lc = 20, fn = vfn) # The evaluation stats are printed print(stats) # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"method-intrinsic-evaluation-","dir":"Reference","previous_headings":"","what":"Method intrinsic_evaluation()","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"Evaluates model using intrinsic evaluation based Perplexity. given number sentences taken validation file. sentence, Perplexity calculated.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"","code":"ModelEvaluator$intrinsic_evaluation(lc, fn)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"lc number lines text validation file used evaluation. fn name validation file. exist, default file validation-clean.txt checked models folder","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"returns-2","dir":"Reference","previous_headings":"","what":"Returns","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"min, max mean Perplexity score.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"examples-2","dir":"Reference","previous_headings":"","what":"Examples","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"","code":"# Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"def-model.RDS\", \"validate-clean.txt\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The model file name mfn <- paste0(ed, \"/def-model.RDS\") # The validation file name vfn <- paste0(ed, \"/validate-clean.txt\") # ModelEvaluator class object is created me <- ModelEvaluator$new(mf = mfn, ve = ve) # The intrinsic evaluation is performed stats <- me$intrinsic_evaluation(lc = 20, fn = vfn) # The evaluation stats are printed print(stats) # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"method-extrinsic-evaluation-","dir":"Reference","previous_headings":"","what":"Method extrinsic_evaluation()","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"Evaluates model using extrinsic evaluation based Accuracy. given number sentences taken validation file. sentence, model used predict next word. accuracy stats returned. prediction considered correct one predicted words matches actual word.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"","code":"ModelEvaluator$extrinsic_evaluation(lc, fn)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"arguments-5","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"lc number lines text validation file used evaluation. fn name validation file.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"returns-3","dir":"Reference","previous_headings":"","what":"Returns","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"number correct incorrect predictions.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"examples-3","dir":"Reference","previous_headings":"","what":"Examples","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"","code":"# Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"def-model.RDS\", \"validate-clean.txt\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The model file name mfn <- paste0(ed, \"/def-model.RDS\") # The validation file name vfn <- paste0(ed, \"/validate-clean.txt\") # ModelEvaluator class object is created me <- ModelEvaluator$new(mf = mfn, ve = ve) # The intrinsic evaluation is performed stats <- me$extrinsic_evaluation(lc = 100, fn = vfn) # The evaluation stats are printed print(stats) # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"objects class cloneable method.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"usage-6","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"","code":"ModelEvaluator$clone(deep = FALSE)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"arguments-6","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"deep Whether make deep clone.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelEvaluator.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Evaluates performance of n-gram models — ModelEvaluator","text":"","code":"## ------------------------------------------------ ## Method `ModelEvaluator$compare_performance` ## ------------------------------------------------ # Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be # used. fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"def-model.RDS\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # ModelEvaluator class object is created me <- ModelEvaluator$new(ve = ve) # The performance evaluation is performed me$compare_performance(opts = list( \"save_to\" = NULL, \"dir\" = ed )) # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env() ## ------------------------------------------------ ## Method `ModelEvaluator$evaluate_performance` ## ------------------------------------------------ # Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"def-model.RDS\", \"validate-clean.txt\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The model file name mfn <- paste0(ed, \"/def-model.RDS\") # The validation file name vfn <- paste0(ed, \"/validate-clean.txt\") # ModelEvaluator class object is created me <- ModelEvaluator$new(mf = mfn, ve = ve) # The performance evaluation is performed stats <- me$evaluate_performance(lc = 20, fn = vfn) # The evaluation stats are printed print(stats) #> $m #> [1] 279320 #> #> $t #> [1] 1.018 #> #> $p #> [1] 2297.35 #> #> $a #> [1] 0 #> # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env() ## ------------------------------------------------ ## Method `ModelEvaluator$intrinsic_evaluation` ## ------------------------------------------------ # Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"def-model.RDS\", \"validate-clean.txt\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The model file name mfn <- paste0(ed, \"/def-model.RDS\") # The validation file name vfn <- paste0(ed, \"/validate-clean.txt\") # ModelEvaluator class object is created me <- ModelEvaluator$new(mf = mfn, ve = ve) # The intrinsic evaluation is performed stats <- me$intrinsic_evaluation(lc = 20, fn = vfn) # The evaluation stats are printed print(stats) #> $min #> [1] 282 #> #> $max #> [1] 8248 #> #> $mean #> [1] 2297.35 #> # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env() ## ------------------------------------------------ ## Method `ModelEvaluator$extrinsic_evaluation` ## ------------------------------------------------ # Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"def-model.RDS\", \"validate-clean.txt\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The model file name mfn <- paste0(ed, \"/def-model.RDS\") # The validation file name vfn <- paste0(ed, \"/validate-clean.txt\") # ModelEvaluator class object is created me <- ModelEvaluator$new(mf = mfn, ve = ve) # The intrinsic evaluation is performed stats <- me$extrinsic_evaluation(lc = 100, fn = vfn) # The evaluation stats are printed print(stats) #> $valid #> [1] 1 #> #> $invalid #> [1] 74 #> #> $valid_perc #> [1] 1.333333 #> #> $invalid_perc #> [1] 98.66667 #> # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelGenerator.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates n-gram models from a text file — ModelGenerator","title":"Generates n-gram models from a text file — ModelGenerator","text":"provides method generating n-gram models. n-gram models may customized specifying data cleaning tokenization options.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelGenerator.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generates n-gram models from a text file — ModelGenerator","text":"provides method generates n-gram model. n-gram model may customized specifying data cleaning tokenization options. data cleaning options include removal punctuation, stop words, extra space, non-dictionary words bad words. tokenization options include n-gram number word stemming.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelGenerator.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Generates n-gram models from a text file — ModelGenerator","text":"wordpredictor::Base -> ModelGenerator","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelGenerator.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Generates n-gram models from a text file — ModelGenerator","text":"Inherited methods","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelGenerator.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Generates n-gram models from a text file — ModelGenerator","text":"ModelGenerator$new() ModelGenerator$generate_model() ModelGenerator$clone()","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelGenerator.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Generates n-gram models from a text file — ModelGenerator","text":"initializes current object. used set maximum n-gram number, sample size, input file name, data cleaner options, tokenization options verbose option.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelGenerator.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates n-gram models from a text file — ModelGenerator","text":"","code":"ModelGenerator$new( name = NULL, desc = NULL, fn = NULL, df = NULL, n = 4, ssize = 0.3, dir = \".\", dc_opts = list(), tg_opts = list(), ve = 0 )"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelGenerator.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates n-gram models from a text file — ModelGenerator","text":"name model name. desc model description. fn model file name. df path input text file. short file name present data directory. n n-gram size model. ssize sample size proportion input file. dir directory containing input output files. dc_opts data cleaner options. tg_opts token generator options. ve level detail information messages.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelGenerator.html","id":"method-generate-model-","dir":"Reference","previous_headings":"","what":"Method generate_model()","title":"Generates n-gram models from a text file — ModelGenerator","text":"generates model using parameters passed object's constructor. generates n-gram model file saves model directory.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelGenerator.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates n-gram models from a text file — ModelGenerator","text":"","code":"ModelGenerator$generate_model()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelGenerator.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates n-gram models from a text file — ModelGenerator","text":"","code":"# Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"input.txt\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # ModelGenerator class object is created mg <- ModelGenerator$new( name = \"default-model\", desc = \"1 MB size and default options\", fn = \"def-model.RDS\", df = \"input.txt\", n = 4, ssize = 0.99, dir = ed, dc_opts = list(), tg_opts = list(), ve = ve ) # The n-gram model is generated mg$generate_model() # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelGenerator.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Generates n-gram models from a text file — ModelGenerator","text":"objects class cloneable method.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelGenerator.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates n-gram models from a text file — ModelGenerator","text":"","code":"ModelGenerator$clone(deep = FALSE)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelGenerator.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates n-gram models from a text file — ModelGenerator","text":"deep Whether make deep clone.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelGenerator.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates n-gram models from a text file — ModelGenerator","text":"","code":"## ------------------------------------------------ ## Method `ModelGenerator$generate_model` ## ------------------------------------------------ # Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"input.txt\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # ModelGenerator class object is created mg <- ModelGenerator$new( name = \"default-model\", desc = \"1 MB size and default options\", fn = \"def-model.RDS\", df = \"input.txt\", n = 4, ssize = 0.99, dir = ed, dc_opts = list(), tg_opts = list(), ve = ve ) # The n-gram model is generated mg$generate_model() # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":null,"dir":"Reference","previous_headings":"","what":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"provides method predicting new word given set previous words. also provides method calculating Perplexity score set words. Furthermore provides method calculating probability given word set previous words.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"wordpredictor::Base -> ModelPredictor","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"Inherited methods","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"ModelPredictor$new() ModelPredictor$get_model() ModelPredictor$calc_perplexity() ModelPredictor$predict_word() ModelPredictor$get_word_prob() ModelPredictor$clone()","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"initializes current object. used set model file name verbose options.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"","code":"ModelPredictor$new(mf, ve = 0)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"mf model file name. ve level detail information messages.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"method-get-model-","dir":"Reference","previous_headings":"","what":"Method get_model()","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"Returns Model class object.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"","code":"ModelPredictor$get_model()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"Model class object returned.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"method-calc-perplexity-","dir":"Reference","previous_headings":"","what":"Method calc_perplexity()","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"Perplexity given sentence calculated. word, probability word given previous words calculated. probabilities multiplied inverted. nth root result perplexity, n number words sentence. stem_words tokenization option specified creating given model file, previous words converted stems.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"","code":"ModelPredictor$calc_perplexity(words)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"words list words.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"perplexity given list words.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"","code":"# Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"def-model.RDS\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The model file name mfn <- paste0(ed, \"/def-model.RDS\") # ModelPredictor class object is created mp <- ModelPredictor$new(mf = mfn, ve = ve) # The sentence whoose Perplexity is to be calculated l <- \"last year at this time i was preparing for a trip to rome\" # The line is split in to words w <- strsplit(l, \" \")[[1]] # The Perplexity of the sentence is calculated p <- mp$calc_perplexity(w) # The sentence Perplexity is printed print(p) # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"method-predict-word-","dir":"Reference","previous_headings":"","what":"Method predict_word()","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"Predicts next word given list previous words. checks last n previous words transition probabilities data, n equal 1 - n-gram size model. match, top 3 next words highest probabilities returned. match, last n-1 previous words checked. process continued last word checked. match, empty result returned. given words may optionally stemmed.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"","code":"ModelPredictor$predict_word(words, count = 3, dc = NULL)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"words character vector previous words single vector containing previous word text. count number results return. dc DataCleaner object. given, given words","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"returns-2","dir":"Reference","previous_headings":"","what":"Returns","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"top 3 predicted words along probabilities.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"examples-1","dir":"Reference","previous_headings":"","what":"Examples","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"","code":"# Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"def-model.RDS\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, \"rp\" = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The model file name mfn <- paste0(ed, \"/def-model.RDS\") # ModelPredictor class object is created mp <- ModelPredictor$new(mf = mfn, ve = ve) # The next word is predicted nws <- mp$predict_word(\"today is\", count = 10) # The predicted next words are printed print(nws) # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"method-get-word-prob-","dir":"Reference","previous_headings":"","what":"Method get_word_prob()","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"Calculates probability given word given previous words. last n words converted numeric hash using digest2int function. words ignored. n equal 1 - size n-gram model. hash looked data frame transition probabilities. last word converted number checking position list unique words. hash word position found, probability previous word hash returned. found, hash n-1 previous words taken processed repeated. data found data frame, word probability returned. known back-. word probability found default probability returned. default probability calculated 1/(N+V), N = number words corpus V number dictionary words.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"","code":"ModelPredictor$get_word_prob(word, pw)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"word word whose probability calculated. pw previous words.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"returns-3","dir":"Reference","previous_headings":"","what":"Returns","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"probability word given previous words.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"examples-2","dir":"Reference","previous_headings":"","what":"Examples","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"","code":"# Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"def-model.RDS\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, \"rp\" = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The model file name mfn <- paste0(ed, \"/def-model.RDS\") # ModelPredictor class object is created mp <- ModelPredictor$new(mf = mfn, ve = ve) # The probability that the next word is \"you\" given the prev words # \"how\" and \"are\" prob <- mp$get_word_prob(word = \"you\", pw = c(\"how\", \"are\")) # The probability is printed print(prob) # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"objects class cloneable method.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"","code":"ModelPredictor$clone(deep = FALSE)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"deep Whether make deep clone.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/ModelPredictor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Allows predicting text, calculating word probabilities and Perplexity — ModelPredictor","text":"","code":"## ------------------------------------------------ ## Method `ModelPredictor$calc_perplexity` ## ------------------------------------------------ # Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"def-model.RDS\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The model file name mfn <- paste0(ed, \"/def-model.RDS\") # ModelPredictor class object is created mp <- ModelPredictor$new(mf = mfn, ve = ve) # The sentence whoose Perplexity is to be calculated l <- \"last year at this time i was preparing for a trip to rome\" # The line is split in to words w <- strsplit(l, \" \")[[1]] # The Perplexity of the sentence is calculated p <- mp$calc_perplexity(w) # The sentence Perplexity is printed print(p) #> [1] 1767 # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env() ## ------------------------------------------------ ## Method `ModelPredictor$predict_word` ## ------------------------------------------------ # Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"def-model.RDS\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, \"rp\" = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The model file name mfn <- paste0(ed, \"/def-model.RDS\") # ModelPredictor class object is created mp <- ModelPredictor$new(mf = mfn, ve = ve) # The next word is predicted nws <- mp$predict_word(\"today is\", count = 10) # The predicted next words are printed print(nws) #> $found #> [1] TRUE #> #> $words #> [1] \"a\" \"the\" \"used\" \"better\" \"hard\" \"rare\" \"to\" \"best\" #> [9] \"carved\" \"dry\" #> #> $probs #> [1] 0.17460317 0.11111111 0.06349206 0.03174603 0.03174603 0.03174603 #> [7] 0.03174603 0.01587302 0.01587302 0.01587302 #> # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env() ## ------------------------------------------------ ## Method `ModelPredictor$get_word_prob` ## ------------------------------------------------ # Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"def-model.RDS\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, \"rp\" = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The model file name mfn <- paste0(ed, \"/def-model.RDS\") # ModelPredictor class object is created mp <- ModelPredictor$new(mf = mfn, ve = ve) # The probability that the next word is \"you\" given the prev words # \"how\" and \"are\" prob <- mp$get_word_prob(word = \"you\", pw = c(\"how\", \"are\")) # The probability is printed print(prob) #> [1] 0.0024581 # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/TPGenerator.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates transition probabilities for n-grams — TPGenerator","title":"Generates transition probabilities for n-grams — TPGenerator","text":"provides method generating transition probabilities given n-gram size. also provides method generating combined transition probabilities data n-gram sizes 1 given size. combined transition probabilities data can used implement back-.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TPGenerator.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generates transition probabilities for n-grams — TPGenerator","text":"provides method generating n-gram transition probabilities. reads n-gram frequencies input text file generated TokenGenerator class. parses n-gram prefix, next word, next word frequency next word probability. Maximum Likelihood count used generate next word probabilities. n-gram prefix converted numeric hash using digest2int function. next word replaced position next word list words. transition probabilities data stored dataframe file. Another method provided combines transition probabilities n-grams size 1 given size. combined transition probabilities can saved file data frame. file may regarded completed self contained n-gram model. combining transition probabilities n-grams, back-may used evaluate word probabilities predict next word.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TPGenerator.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Generates transition probabilities for n-grams — TPGenerator","text":"wordpredictor::Base -> TPGenerator","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TPGenerator.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Generates transition probabilities for n-grams — TPGenerator","text":"Inherited methods","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TPGenerator.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Generates transition probabilities for n-grams — TPGenerator","text":"TPGenerator$new() TPGenerator$generate_tp() TPGenerator$generate_tp_for_n() TPGenerator$clone()","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TPGenerator.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Generates transition probabilities for n-grams — TPGenerator","text":"initializes current obj. used set transition probabilities options verbose option.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TPGenerator.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates transition probabilities for n-grams — TPGenerator","text":"","code":"TPGenerator$new(opts = list(), ve = 0)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/TPGenerator.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates transition probabilities for n-grams — TPGenerator","text":"opts options generating transition probabilities. save_tp. data saved. n. n-gram size. dir. directory containing input output files. format. format output. two options. plain. data stored plain text. obj. data stored R obj. ve level detail information messages.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TPGenerator.html","id":"method-generate-tp-","dir":"Reference","previous_headings":"","what":"Method generate_tp()","title":"Generates transition probabilities for n-grams — TPGenerator","text":"first generates transition probabilities n-gram size 1 given size. transition probabilities combined single data frame saved output folder given parameter current object. combining transition probabilities n-gram sizes 1 n, back-can used calculate next word probabilities predict next word.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TPGenerator.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates transition probabilities for n-grams — TPGenerator","text":"","code":"TPGenerator$generate_tp()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/TPGenerator.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates transition probabilities for n-grams — TPGenerator","text":"","code":"# Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"n1.RDS\", \"n2.RDS\", \"n3.RDS\", \"n4.RDS\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The list of output files fns <- c(\"words\", \"model-4\", \"tp2\", \"tp3\", \"tp4\") # The TPGenerator object is created tp <- TPGenerator$new(opts = list(n = 4, dir = ed), ve = ve) # The combined transition probabilities are generated tp$generate_tp() # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/TPGenerator.html","id":"method-generate-tp-for-n-","dir":"Reference","previous_headings":"","what":"Method generate_tp_for_n()","title":"Generates transition probabilities for n-grams — TPGenerator","text":"generates transition probabilities table given n-gram size. first reads n-gram token frequencies input text file. generates data frame whose columns n-gram prefix, next word next word frequency. data frame may saved file plain text R obj. n = 1, list words saved.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TPGenerator.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates transition probabilities for n-grams — TPGenerator","text":"","code":"TPGenerator$generate_tp_for_n(n)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/TPGenerator.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates transition probabilities for n-grams — TPGenerator","text":"n n-gram size tp data generated.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TPGenerator.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Generates transition probabilities for n-grams — TPGenerator","text":"objects class cloneable method.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TPGenerator.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates transition probabilities for n-grams — TPGenerator","text":"","code":"TPGenerator$clone(deep = FALSE)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/TPGenerator.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates transition probabilities for n-grams — TPGenerator","text":"deep Whether make deep clone.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TPGenerator.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates transition probabilities for n-grams — TPGenerator","text":"","code":"## ------------------------------------------------ ## Method `TPGenerator$generate_tp` ## ------------------------------------------------ # Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"n1.RDS\", \"n2.RDS\", \"n3.RDS\", \"n4.RDS\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The list of output files fns <- c(\"words\", \"model-4\", \"tp2\", \"tp3\", \"tp4\") # The TPGenerator object is created tp <- TPGenerator$new(opts = list(n = 4, dir = ed), ve = ve) # The combined transition probabilities are generated tp$generate_tp() # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/TokenGenerator.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates n-grams from text files — TokenGenerator","title":"Generates n-grams from text files — TokenGenerator","text":"generates n-gram tokens along frequencies. data may saved file plain text format R object.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TokenGenerator.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Generates n-grams from text files — TokenGenerator","text":"wordpredictor::Base -> TokenGenerator","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TokenGenerator.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Generates n-grams from text files — TokenGenerator","text":"Inherited methods","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TokenGenerator.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Generates n-grams from text files — TokenGenerator","text":"TokenGenerator$new() TokenGenerator$generate_tokens() TokenGenerator$clone()","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TokenGenerator.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Generates n-grams from text files — TokenGenerator","text":"initializes current obj. used set file name, tokenization options verbose option.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TokenGenerator.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates n-grams from text files — TokenGenerator","text":"","code":"TokenGenerator$new(fn = NULL, opts = list(), ve = 0)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/TokenGenerator.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates n-grams from text files — TokenGenerator","text":"fn path input file. opts options generating n-gram tokens. n. n-gram size. save_ngrams. n-gram data saved. min_freq. n-grams frequency less min_freq ignored. line_count. number lines process time. stem_words. words transformed stems. dir. dir output file saved. format. format output. two options. plain. data stored plain text. obj. data stored R obj. ve level detail information messages.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TokenGenerator.html","id":"method-generate-tokens-","dir":"Reference","previous_headings":"","what":"Method generate_tokens()","title":"Generates n-grams from text files — TokenGenerator","text":"generates n-gram tokens frequencies given file name. tokens may saved text file plain text R object.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TokenGenerator.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates n-grams from text files — TokenGenerator","text":"","code":"TokenGenerator$generate_tokens()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/TokenGenerator.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Generates n-grams from text files — TokenGenerator","text":"data frame containing n-gram tokens along frequencies.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TokenGenerator.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates n-grams from text files — TokenGenerator","text":"","code":"# Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"test-clean.txt\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The n-gram size n <- 4 # The test file name tfn <- paste0(ed, \"/test-clean.txt\") # The n-gram number is set tg_opts <- list(\"n\" = n, \"save_ngrams\" = TRUE, \"dir\" = ed) # The TokenGenerator object is created tg <- TokenGenerator$new(tfn, tg_opts, ve = ve) # The n-gram tokens are generated tg$generate_tokens() # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/TokenGenerator.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Generates n-grams from text files — TokenGenerator","text":"objects class cloneable method.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TokenGenerator.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates n-grams from text files — TokenGenerator","text":"","code":"TokenGenerator$clone(deep = FALSE)"},{"path":"https://pakjiddat.github.io/word-predictor/reference/TokenGenerator.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates n-grams from text files — TokenGenerator","text":"deep Whether make deep clone.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/reference/TokenGenerator.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates n-grams from text files — TokenGenerator","text":"","code":"## ------------------------------------------------ ## Method `TokenGenerator$generate_tokens` ## ------------------------------------------------ # Start of environment setup code # The level of detail in the information messages ve <- 0 # The name of the folder that will contain all the files. It will be # created in the current directory. NULL implies tempdir will be used fn <- NULL # The required files. They are default files that are part of the # package rf <- c(\"test-clean.txt\") # An object of class EnvManager is created em <- EnvManager$new(ve = ve, rp = \"./\") # The required files are downloaded ed <- em$setup_env(rf, fn) # End of environment setup code # The n-gram size n <- 4 # The test file name tfn <- paste0(ed, \"/test-clean.txt\") # The n-gram number is set tg_opts <- list(\"n\" = n, \"save_ngrams\" = TRUE, \"dir\" = ed) # The TokenGenerator object is created tg <- TokenGenerator$new(tfn, tg_opts, ve = ve) # The n-gram tokens are generated tg$generate_tokens() # The test environment is removed. Comment the below line, so the # files generated by the function can be viewed em$td_env()"},{"path":"https://pakjiddat.github.io/word-predictor/reference/wordpredictor-package.html","id":null,"dir":"Reference","previous_headings":"","what":"wordpredictor: Develop Text Prediction Models Based on N-Grams — wordpredictor-package","title":"wordpredictor: Develop Text Prediction Models Based on N-Grams — wordpredictor-package","text":"framework developing n-gram models text prediction. provides data cleaning, data sampling, extracting tokens text, model generation, model evaluation word prediction. information n-gram models work referred : \"Speech Language Processing\" . optimizing R code using R6 classes referred \"Advanced R\" . writing R extensions referred \"R Packages\", .","code":""},{"path":[]},{"path":"https://pakjiddat.github.io/word-predictor/reference/wordpredictor-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"wordpredictor: Develop Text Prediction Models Based on N-Grams — wordpredictor-package","text":"Maintainer: Nadir Latif pakjiddat@gmail.com (ORCID)","code":""},{"path":[]},{"path":"https://pakjiddat.github.io/word-predictor/news/index.html","id":"bug-fixes-0-0-3","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"wordpredictor 0.0.3","text":"Disabled caching R Markdown files, causing problems CRAN checks.","code":""},{"path":"https://pakjiddat.github.io/word-predictor/news/index.html","id":"wordpredictor-002","dir":"Changelog","previous_headings":"","what":"wordpredictor 0.0.2","title":"wordpredictor 0.0.2","text":"CRAN release: 2021-06-19","code":""},{"path":"https://pakjiddat.github.io/word-predictor/news/index.html","id":"bug-fixes-0-0-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"wordpredictor 0.0.2","text":"Fixed small bugs causing problems GitHub actions CRAN checks. Removed custom .Rprofile file causing problems GitHub actions. Updated sample code features.Rmd vignette cause issues R CMD Check MacOs. Removed inst/extdata folder .gitignore since causing problems check-standard workflow GitHub. Removed non-standard characters example data-cleaner.R file causing problems CRAN check “Debian Linux, R-devel, clang”. Issues related bug fixes: #318, #319, #320","code":""},{"path":"https://pakjiddat.github.io/word-predictor/news/index.html","id":"wordpredictor-001","dir":"Changelog","previous_headings":"","what":"wordpredictor 0.0.1","title":"wordpredictor 0.0.1","text":"CRAN release: 2021-06-14 Initial Release.","code":""}] diff --git a/docs/sitemap.xml b/docs/sitemap.xml index bbdcf9c..06265cb 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -1,8 +1,32 @@ + + https://pakjiddat.github.io/word-predictor/404.html + + + https://pakjiddat.github.io/word-predictor/LICENSE-text.html + + + https://pakjiddat.github.io/word-predictor/LICENSE.html + + + https://pakjiddat.github.io/word-predictor/articles/features.html + + + https://pakjiddat.github.io/word-predictor/articles/index.html + + + https://pakjiddat.github.io/word-predictor/articles/overview.html + + + https://pakjiddat.github.io/word-predictor/authors.html + https://pakjiddat.github.io/word-predictor/index.html + + https://pakjiddat.github.io/word-predictor/news/index.html + https://pakjiddat.github.io/word-predictor/reference/Base.html @@ -34,15 +58,15 @@ https://pakjiddat.github.io/word-predictor/reference/TPGenerator.html - https://pakjiddat.github.io/word-predictor/reference/TokenGenerator.html + https://pakjiddat.github.io/word-predictor/reference/TextFileProcessor.html - https://pakjiddat.github.io/word-predictor/reference/wordpredictor-package.html + https://pakjiddat.github.io/word-predictor/reference/TokenGenerator.html - https://pakjiddat.github.io/word-predictor/articles/features.html + https://pakjiddat.github.io/word-predictor/reference/index.html - https://pakjiddat.github.io/word-predictor/articles/overview.html + https://pakjiddat.github.io/word-predictor/reference/wordpredictor-package.html diff --git a/man/figures/README-analyze-ngrams-1-1.png b/man/figures/README-analyze-ngrams-1-1.png index d777f9b516c7ab7a428d69aead8ac686c370e70d..d96ff1123814a1abc7e6d398b581d469934f67d6 100644 GIT binary patch delta 5448 zcmbVQXH-+$w%(i26^Qgsj!KbUq_=qJpn`xP2qL}9l_Cg)L`4As=}lTh2nj_GO+?xe zqy?md3MPtxG$Hh+z}w;6`_3J2oL}!p#>iT8?lsr?=C|fI_fApxtdMMzVy6N-efo4t zk}#93tgM`zoV>hzN`pF+p`oFXk&&^namt}9%-7d9%^8%3p-`x_6fl6RxVX5aqy&e< zm6n#`@puA(P*zq}!$m^_qN@fx1pokG`j@d?5CE{YQ-5Gr0NxD%xYAAZFIb1=EtBrv zpkh-gM+6Svy>TU8ENS6rN>yMr%PUi z&%3HK6Q8Yt2CRt)eb?l=lycqiU#qH83UJs25z9KRYIn6mF;Z1=$1&laO?LcbBhU; zy$O0723FK3CN6EWWk4^R6#v~hHfyKi+79N|Jr z&8TQ|t~&JD969ISbQp{37~SU=`-M!lVPNy%3yj3|y&o}2CyW7nUQj09zyKU<6*m4g zyGn^8rni>9GL4+augxY+)VF>)6=y0otbNJ?8Cv{fNtY@ zHah7?I5LA6KOx?_A{?AI-tx+kXP|MaYjI;F-n8}Vz(G;_&SppKotTyrw4;sF#g3pR zIZJJ2R+yMOyyDg*Dr7;D*%@U=Vf(DWx`W5_x!dNuJF7GrB^Bd33n96b*KuaG%=5Y{ zA!vfFaomI0HzXXHXmw{v&Kbm{YcI-tdE%Mgu^8kyH+Po&e%Dk3JCGhSu^e$^MR(q~ zQTa_}SA7i6U<^@ULOhA|RkMGx6D{@9r;x4b3ErkLJY4T?sRDL#OyE#xpe`J1Ow5n} zPz;+W6CAz8)_jBRylvywm&fhY6>htu6SqwUoEU^l>&&x84V#-Ih|lZm*}+Ajrs*H! 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zXucN>>#(ArRzpPlzyK0VyUClF%yTpzMn*yL}52TD3cwHhDtL7rq@=ZC)gN$C3Gnfy$Tahu%}Sn=d{@CM1kO zQfBn=oA+me$~A`;0T3Hk8y6V9!SpN7pO=sAQ+RlpccB=HA~0F)a;Qt+zI_8%R+R*% zOkjM3nuPlkwg1ha#QwRX} zGPZOkkQJNBu9MZ?NA5N@t1#w|*^@G|ra}-U)(SxCU`SaET11fE;gH5GEEa=#>hi&- u&0g4Dgdx#4fq$7X`#W*`%b4wve$97(>^JYOD1Z@!kdsor`&Poh|Nj8BBwnNd diff --git a/man/wordpredictor-package.Rd b/man/wordpredictor-package.Rd index 776f9e8..32836ee 100644 --- a/man/wordpredictor-package.Rd +++ b/man/wordpredictor-package.Rd @@ -6,14 +6,7 @@ \alias{wordpredictor-package} \title{wordpredictor: Develop Text Prediction Models Based on N-Grams} \description{ -A framework for developing n-gram models for text prediction. - It provides data cleaning, data sampling, extracting tokens from text, - model generation, model evaluation and word prediction. For information on how n-gram models - work we referred to: "Speech and Language Processing" - . For optimizing R code and - using R6 classes we referred to "Advanced R" - . For writing R extensions we referred to - "R Packages", . +A framework for developing n-gram models for text prediction. It provides data cleaning, data sampling, extracting tokens from text, model generation, model evaluation and word prediction. For information on how n-gram models work we referred to: "Speech and Language Processing" . For optimizing R code and using R6 classes we referred to "Advanced R" . For writing R extensions we referred to "R Packages", . } \seealso{ Useful links: diff --git a/vignettes/features.Rmd b/vignettes/features.Rmd index f9dad89..74e3ce4 100644 --- a/vignettes/features.Rmd +++ b/vignettes/features.Rmd @@ -11,14 +11,14 @@ vignette: > knitr::opts_chunk$set( collapse = TRUE, comment = "#>", - fig.path = "man/figures/" + fig.path = "reference/figures/" ) ``` ```{r setup, echo=FALSE, results='hide', message=FALSE} library(wordpredictor) -# If level of verbosity in the information messages +# The level of verbosity in the information messages ve <- 0 #' @description @@ -59,7 +59,7 @@ The first step in generating a n-gram model is data exploration. This involves d It is also important to determine the unwanted words and symbols in the data such as vulgar words, punctuation symbols, non-alphabetical symbols etc. The **wordpredictor** package provides the **DataAnalyzer** class which can be used to find out statistics about the input data. The following example shows how to get statistics on all text files within a folder: -```{r data-exploration, cache=TRUE} +```{r data-exploration, cache=FALSE} # The required files rf <- c( "test.txt", @@ -88,7 +88,7 @@ The next step is to generate training, testing and validation samples from the i The **wordpredictor** package provides the **DataSampler** class which can be used to generate a random sample containing given number of lines. The following example shows how to generate a random sample of size 10 Mb from an input text file: -```{r data-sampling-1, cache=TRUE} +```{r data-sampling-1, cache=FALSE} # The required files rf <- c("input.txt") # The test environment is setup @@ -124,7 +124,7 @@ Usually we need a train data set for generating the n-gram model. A test data se The data in the validation file must be different from the data in the train file. Otherwise it can result in over-fitting of the model. When a model is over-fitted, the model evaluation results will be exaggerated, overly optimistic and unreliable. So care should be taken to ensure that the data in the validation and train files is different. -```{r data-sampling-2, cache=TRUE} +```{r data-sampling-2, cache=FALSE} # The required files rf <- c("input.txt") # The test environment is setup @@ -153,7 +153,7 @@ The next step is to remove unwanted symbols and words from the input text file. The **wordpredictor** package provides the **DataCleaner** class which can be used to remove unwanted words and symbols from text files. The following example shows how to clean a given text file: -```{r data-cleaning, cache=TRUE} +```{r data-cleaning, cache=FALSE} # The required files rf <- c("input.txt") # The test environment is setup @@ -189,7 +189,7 @@ The **clean_file** method reads a certain number of lines at a time, cleans the ### Tokenization The next step is to generate n-gram tokens from the cleaned text file. The **TokenGenerator** class allows generating n-gram tokens of given size from a given input text file. The following example shows how to generate n-grams tokens of size 1,2,3 and 4: -```{r tokenization-1, cache=TRUE} +```{r tokenization-1, cache=FALSE} # The required files rf <- c("test-clean.txt") # The test environment is setup @@ -219,7 +219,7 @@ The **stem_words** option is used to transform n-gram prefix components to their The n-gram token frequencies may be analyzed using the **DataAnalyzer** class. The following example displays the top most occurring 2-gram tokens: -```{r tokenization-2, cache=TRUE, out.width="70%", out.height="70%"} +```{r tokenization-2, cache=FALSE, out.width="70%", out.height="70%"} # The required files rf <- c("n2.RDS") # The test environment is setup @@ -234,11 +234,11 @@ df <- da$plot_n_gram_stats(opts = list( "type" = "top_features", "n" = 10, "save_to" = "png", - "dir" = "./man/figures" + "dir" = "./reference/figures" )) # The output file path -fn <- paste0("./man/figures/top_features.png") +fn <- paste0("./reference/figures/top_features.png") knitr::include_graphics(fn) # The test environment is cleaned up @@ -247,7 +247,7 @@ clean_up(ve) The following example shows the distribution of word frequencies: -```{r tokenization-3, cache=TRUE, out.width="70%", out.height="70%"} +```{r tokenization-3, cache=FALSE, out.width="70%", out.height="70%"} # The required files rf <- c("n2.RDS") # The test environment is setup @@ -262,11 +262,11 @@ df <- da$plot_n_gram_stats(opts = list( "type" = "coverage", "n" = 10, "save_to" = "png", - "dir" = "./man/figures" + "dir" = "./reference/figures" )) # The output file path -fn <- paste0("./man/figures/coverage.png") +fn <- paste0("./reference/figures/coverage.png") knitr::include_graphics(fn) # The test environment is cleaned up @@ -275,7 +275,7 @@ clean_up(ve) The following example returns top 10 2-gram tokens that start with **and_**: -```{r tokenization-4, cache=TRUE} +```{r tokenization-4, cache=FALSE} # The required files rf <- c("n2.RDS") # The test environment is setup @@ -303,7 +303,7 @@ The tp files are then combined into a single file containing tp data for n-grams The following example shows how to generate combined tps for n-grams of size 1, 2, 3 and 4: -```{r transition-probabilities, cache=TRUE} +```{r transition-probabilities, cache=FALSE} # The required files rf <- c("n1.RDS", "n2.RDS", "n3.RDS", "n4.RDS") # The test environment is setup @@ -325,7 +325,7 @@ The final step is to generate a n-gram model file from the files generated in th ### Generating the model in one step All the previous steps may be combined into a single step. The **ModelGenerator** class allows generating the final n-gram model using a single method call. The following example generates a n-gram model using default data cleaning and tokenization options: -```{r generate-model, results='hide', cache=TRUE} +```{r generate-model, results='hide', cache=FALSE} # The required files rf <- c("input.txt") # The test environment is setup @@ -362,7 +362,7 @@ The **wordpredictor** package provides the **ModelEvaluator** class for evaluati The following example performs Intrinsic evaluation. It measures the Perplexity score for each sentence in the **validation.txt** file, that was generated in the data sampling step. It returns the minimum, mean and maximum Perplexity score for each line. -```{r model-evaluation-1, cache=TRUE} +```{r model-evaluation-1, cache=FALSE} # The required files rf <- c("def-model.RDS", "validate-clean.txt") # The test environment is setup @@ -383,7 +383,7 @@ clean_up(ve) The following example performs Extrinsic evaluation. It measures the accuracy score for each sentence in **validation.txt** file. For each sentence the model is used to predict the last word in the sentence given the previous words. If the last word was correctly predicted, then the prediction is considered to be accurate. -```{r model-evaluation-2, cache=TRUE} +```{r model-evaluation-2, cache=FALSE} # The required files rf <- c("def-model.RDS", "validate-clean.txt") # The test environment is setup @@ -406,7 +406,7 @@ clean_up(ve) The n-gram model generated in the previous step can be used to predict the next word given a set of words. The following example shows how to predict the next word. 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zTemXByldAjQdhWnqAaD(ONfpA?dx@MIod;Z-5il^$6vYpHCvIWo^h(M-6;O+N4K8| zgZNh0J5$GsKgHLLkmi#?@*iG(e=_g?$ACPYE=c_dlGvaD@h6Bq{51TMKag~>_E%N; QZ;<~yUHx3vIVCg!0QK-OVgLXD diff --git a/vignettes/overview.Rmd b/vignettes/overview.Rmd index eb78847..3a240dd 100644 --- a/vignettes/overview.Rmd +++ b/vignettes/overview.Rmd @@ -13,14 +13,14 @@ nocite: '@*' knitr::opts_chunk$set( collapse = TRUE, comment = "#>", - fig.path = "man/figures/VIGNETTES-" + fig.path = "reference/figures/" ) ``` ```{r setup, echo=FALSE, results='hide', message=FALSE} library(wordpredictor) -# If level of verbosity in the information messages +# The level of verbosity in the information messages ve <- 0 #' @description @@ -111,7 +111,7 @@ Extrinsic evaluation involves calculating the accuracy score. The model tries to The **ModelGenerator** class allows generating the final n-gram model using a single method call. The following example generates a n-gram model using default data cleaning and tokenization options: -```{r generate-model, results='hide', cache=TRUE} +```{r generate-model, results='hide', cache=FALSE} # The required files rf <- c("input.txt") # The test environment is setup @@ -150,7 +150,7 @@ The **wordpredictor** package provides the **ModelEvaluator** class for evaluati The following example performs intrinsic evaluation. It measures the Perplexity score for each sentence in the **validation.txt** file. It returns the minimum, mean and maximum Perplexity score for each line. -```{r model-evaluation-1, cache=TRUE} +```{r model-evaluation-1, cache=FALSE} # The required files rf <- c("def-model.RDS", "validate-clean.txt") # The test environment is setup @@ -171,7 +171,7 @@ clean_up(ve) The following example performs extrinsic evaluation. It measures the accuracy score for each sentence in **validation.txt** file. For each sentence the model is used to predict the last word in the sentence given the previous words. If the last word was correctly predicted, then the prediction is considered to be accurate. The extrinsic evaluation returns the number of correct and incorrect predictions. -```{r model-evaluation-2, cache=TRUE} +```{r model-evaluation-2, cache=FALSE} # The required files rf <- c("def-model.RDS", "validate-clean.txt") # The test environment is setup @@ -194,7 +194,7 @@ clean_up(ve) The following example shows how to predict the next word. It returns the 3 possible next words along with their probabilities. -```{r predict-word, cache=TRUE} +```{r predict-word, cache=FALSE} # The required files rf <- c("def-model.RDS", "validate-clean.txt") # The test environment is setup diff --git a/vignettes/man/figures/README-tokenization-2-1.png b/vignettes/reference/figures/README-tokenization-2-1.png similarity index 100% rename from vignettes/man/figures/README-tokenization-2-1.png rename to vignettes/reference/figures/README-tokenization-2-1.png diff --git a/vignettes/man/figures/README-tokenization-3-1.png b/vignettes/reference/figures/README-tokenization-3-1.png similarity index 100% rename from vignettes/man/figures/README-tokenization-3-1.png rename to vignettes/reference/figures/README-tokenization-3-1.png diff --git a/vignettes/man/figures/VIGNETTES-tokenization-2-1.png b/vignettes/reference/figures/VIGNETTES-tokenization-2-1.png similarity index 100% rename from vignettes/man/figures/VIGNETTES-tokenization-2-1.png rename 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