Lauren Lee McCarthy
💻 📖
-* You can use the latest version (0.9.8) by adding it to the head section of your HTML document: +* You can use the latest version (0.10.6) by adding it to the head section of your HTML document: -**v0.9.8** +**v0.10.6** - +
@@ -93,6 +101,14 @@ Before getting started with ml5.js, review our [Code of Conduct](https://github. + + + + + + + + @@ -130,11 +146,11 @@ For example: ## Resources -- [Getting Started](https://ml5js.org/getting-started/) -- [API Reference](https://ml5js.org/reference/) +- [Getting Started](https://learn.ml5js.org/) +- [API Reference](https://learn.ml5js.org/#/reference/index) - [Examples](https://github.com/ml5js/ml5-library/tree/main/examples) - [Community](https://ml5js.org/community) -- [FAQ](https://ml5js.org/getting-started/faq/) +- [FAQ](https://learn.ml5js.org/#/faq) ## Standalone Examples @@ -321,6 +337,15 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
+ Press n or j to go to the next uncovered block, b, p or k for the previous block. +
+ +| File | ++ | Statements | ++ | Branches | ++ | Functions | ++ | Lines | ++ |
|---|---|---|---|---|---|---|---|---|---|
| index.js | +
+
+ |
+ 69.34% | +95/137 | +63.63% | +21/33 | +63.63% | +14/22 | +70% | +91/130 | +
+ Press n or j to go to the next uncovered block, b, p or k for the previous block. +
+ +| 1 +2 +3 +4 +5 +6 +7 +8 +9 +10 +11 +12 +13 +14 +15 +16 +17 +18 +19 +20 +21 +22 +23 +24 +25 +26 +27 +28 +29 +30 +31 +32 +33 +34 +35 +36 +37 +38 +39 +40 +41 +42 +43 +44 +45 +46 +47 +48 +49 +50 +51 +52 +53 +54 +55 +56 +57 +58 +59 +60 +61 +62 +63 +64 +65 +66 +67 +68 +69 +70 +71 +72 +73 +74 +75 +76 +77 +78 +79 +80 +81 +82 +83 +84 +85 +86 +87 +88 +89 +90 +91 +92 +93 +94 +95 +96 +97 +98 +99 +100 +101 +102 +103 +104 +105 +106 +107 +108 +109 +110 +111 +112 +113 +114 +115 +116 +117 +118 +119 +120 +121 +122 +123 +124 +125 +126 +127 +128 +129 +130 +131 +132 +133 +134 +135 +136 +137 +138 +139 +140 +141 +142 +143 +144 +145 +146 +147 +148 +149 +150 +151 +152 +153 +154 +155 +156 +157 +158 +159 +160 +161 +162 +163 +164 +165 +166 +167 +168 +169 +170 +171 +172 +173 +174 +175 +176 +177 +178 +179 +180 +181 +182 +183 +184 +185 +186 +187 +188 +189 +190 +191 +192 +193 +194 +195 +196 +197 +198 +199 +200 +201 +202 +203 +204 +205 +206 +207 +208 +209 +210 +211 +212 +213 +214 +215 +216 +217 +218 +219 +220 +221 +222 +223 +224 +225 +226 +227 +228 +229 +230 +231 +232 +233 +234 +235 +236 +237 +238 +239 +240 +241 +242 +243 +244 +245 +246 +247 +248 +249 +250 +251 +252 +253 +254 +255 +256 +257 +258 +259 +260 +261 +262 +263 +264 +265 +266 +267 +268 +269 +270 +271 +272 +273 +274 +275 +276 +277 +278 +279 +280 +281 +282 +283 +284 +285 +286 +287 +288 +289 +290 +291 +292 +293 +294 +295 +296 +297 +298 +299 +300 +301 +302 +303 +304 +305 +306 +307 +308 +309 +310 +311 +312 +313 +314 +315 | + + + + + + + + + + + + + + + + +1x +1x +1x + + + + + + + + + + + + + + + +1x + + + + + + +1x +1x +1x +1x + + + + + +1x +1x +1x +1x +1x + + + + + + +1x + + + + + + + + + + + + + + + + +1x +1x +1x +8x +4x +2x +2x + +2x + +4x +2x +1x + +1x + + +2x + + +1x +1x +1x + + + +1x +1x +1x +1x +1x + + + + + + +1x +1x +1x + +1x +2x +108x + + + + + + + +2x + + +1x +2x +2x +2x + + +1x + + + +2x +2x +2x +2x +2x +2x +2x + + +2x +2x +2x + +2x +26x + + +2x +2x + +2x +54x +54x +54x + +54x +54x +54x + + + + +54x +54x + +54x +54x +54x +54x +54x +54x + +54x +24x + +30x +30x + + + +2x +2x +1861x +30x +30x + + +2x +2x + + + + + + + + + +2x + + + + + + + + + + + + + + + + + + + + + + +2x +2x + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +1x + + + | // Copyright (c) 2018 ml5
+//
+// This software is released under the MIT License.
+// https://opensource.org/licenses/MIT
+
+/* eslint prefer-destructuring: ["error", {AssignmentExpression: {array: false}}] */
+/* eslint no-await-in-loop: "off" */
+/*
+A LSTM Generator: Run inference mode for a pre-trained LSTM.
+*/
+
+import * as tf from "@tensorflow/tfjs";
+import axios from "axios";
+import sampleFromDistribution from "./../utils/sample";
+import CheckpointLoader from "../utils/checkpointLoader";
+import callCallback from "../utils/callcallback";
+
+const regexCell = /cell_[0-9]|lstm_[0-9]/gi;
+const regexWeights = /weights|weight|kernel|kernels|w/gi;
+const regexFullyConnected = /softmax/gi;
+
+class CharRNN {
+ /**
+ * Create a CharRNN.
+ * @param {String} modelPath - The path to the trained charRNN model.
+ * @param {function} callback - Optional. A callback to be called once
+ * the model has loaded. If no callback is provided, it will return a
+ * promise that will be resolved once the model has loaded.
+ */
+ constructor(modelPath, callback) {
+ /**
+ * Boolean value that specifies if the model has loaded.
+ * @type {boolean}
+ * @public
+ */
+ this.ready = false;
+
+ /**
+ * The pre-trained charRNN model.
+ * @type {model}
+ * @public
+ */
+ this.model = {};
+ this.cellsAmount = 0;
+ this.cells = [];
+ this.zeroState = { c: [], h: [] };
+ /**
+ * The vocabulary size (or total number of possible characters).
+ * @type {c: Array, h: Array}
+ * @public
+ */
+ this.state = { c: [], h: [] };
+ this.vocab = {};
+ this.vocabSize = 0;
+ this.probabilities = [];
+ this.defaults = {
+ seed: "a", // TODO: use no seed by default
+ length: 20,
+ temperature: 0.5,
+ stateful: false,
+ };
+
+ this.ready = callCallback(this.loadCheckpoints(modelPath), callback);
+ // this.then = this.ready.then.bind(this.ready);
+ }
+
+ resetState() {
+ this.state = this.zeroState;
+ }
+
+ setState(state) {
+ this.state = state;
+ }
+
+ getState() {
+ return this.state;
+ }
+
+ async loadCheckpoints(path) {
+ const reader = new CheckpointLoader(path);
+ const vars = await reader.getAllVariables();
+ Object.keys(vars).forEach(key => {
+ if (key.match(regexCell)) {
+ if (key.match(regexWeights)) {
+ this.model[`Kernel_${key.match(/[0-9]/)[0]}`] = vars[key];
+ this.cellsAmount += 1;
+ } else {
+ this.model[`Bias_${key.match(/[0-9]/)[0]}`] = vars[key];
+ }
+ } else if (key.match(regexFullyConnected)) {
+ if (key.match(regexWeights)) {
+ this.model.fullyConnectedWeights = vars[key];
+ } else {
+ this.model.fullyConnectedBiases = vars[key];
+ }
+ } else {
+ this.model[key] = vars[key];
+ }
+ });
+ await this.loadVocab(path);
+ await this.initCells();
+ return this;
+ }
+
+ async loadVocab(path) {
+ try {
+ const { data } = await axios.get(`${path}/vocab.json`);
+ this.vocab = data;
+ this.vocabSize = Object.keys(data).length;
+ return this.vocab;
+ } catch (err) {
+ return err;
+ }
+ }
+
+ async initCells() {
+ this.cells = [];
+ this.zeroState = { c: [], h: [] };
+ const forgetBias = tf.tensor(1.0);
+
+ const lstm = i => {
+ const cell = (DATA, C, H) =>
+ tf.basicLSTMCell(
+ forgetBias,
+ this.model[`Kernel_${i}`],
+ this.model[`Bias_${i}`],
+ DATA,
+ C,
+ H,
+ );
+ return cell;
+ };
+
+ for (let i = 0; i < this.cellsAmount; i += 1) {
+ this.zeroState.c.push(tf.zeros([1, this.model[`Bias_${i}`].shape[0] / 4]));
+ this.zeroState.h.push(tf.zeros([1, this.model[`Bias_${i}`].shape[0] / 4]));
+ this.cells.push(lstm(i));
+ }
+
+ this.state = this.zeroState;
+ }
+
+ async generateInternal(options) {
+ await this.ready;
+ const seed = options.seed || this.defaults.seed;
+ const length = +options.length || this.defaults.length;
+ const temperature = +options.temperature || this.defaults.temperature;
+ const stateful = options.stateful || this.defaults.stateful;
+ if (!stateful) {
+ this.state = this.zeroState;
+ }
+
+ const results = [];
+ const userInput = Array.from(seed);
+ const encodedInput = [];
+
+ userInput.forEach(char => {
+ encodedInput.push(this.vocab[char]);
+ });
+
+ let input = encodedInput[0];
+ let probabilitiesNormalized = []; // will contain final probabilities (normalized)
+
+ for (let i = 0; i < userInput.length + length + -1; i += 1) {
+ const onehotBuffer = await tf.buffer([1, this.vocabSize]);
+ onehotBuffer.set(1.0, 0, input);
+ const onehot = onehotBuffer.toTensor();
+ let output;
+ if (this.model.embedding) {
+ const embedded = tf.matMul(onehot, this.model.embedding);
+ output = tf.multiRNNCell(this.cells, embedded, this.state.c, this.state.h);
+ } else E{
+ output = tf.multiRNNCell(this.cells, onehot, this.state.c, this.state.h);
+ }
+
+ this.state.c = output[0];
+ this.state.h = output[1];
+
+ const outputH = this.state.h[1];
+ const weightedResult = tf.matMul(outputH, this.model.fullyConnectedWeights);
+ const logits = tf.add(weightedResult, this.model.fullyConnectedBiases);
+ const divided = tf.div(logits, tf.tensor(temperature));
+ const probabilities = tf.exp(divided);
+ probabilitiesNormalized = await tf.div(probabilities, tf.sum(probabilities)).data();
+
+ if (i < userInput.length - 1) {
+ input = encodedInput[i + 1];
+ } else {
+ input = sampleFromDistribution(probabilitiesNormalized);
+ results.push(input);
+ }
+ }
+
+ let generated = "";
+ results.forEach(char => {
+ const mapped = Object.keys(this.vocab).find(key => this.vocab[key] === char);
+ if (mapped) {
+ generated += mapped;
+ }
+ });
+ this.probabilities = probabilitiesNormalized;
+ return {
+ sample: generated,
+ state: this.state,
+ };
+ }
+
+ /**
+ * Reset the model state.
+ */
+ reset() {
+ this.state = this.zeroState;
+ }
+
+ /**
+ * @typedef {Object} options
+ * @property {String} seed
+ * @property {number} length
+ * @property {number} temperature
+ */
+
+ // stateless
+ /**
+ * Generates content in a stateless manner, based on some initial text
+ * (known as a "seed"). Returns a string.
+ * @param {options} options - An object specifying the input parameters of
+ * seed, length and temperature. Default length is 20, temperature is 0.5
+ * and seed is a random character from the model. The object should look like
+ * this:
+ * @param {function} callback - Optional. A function to be called when the model
+ * has generated content. If no callback is provided, it will return a promise
+ * that will be resolved once the model has generated new content.
+ */
+ async generate(options, callback) {
+ this.reset();
+ return callCallback(this.generateInternal(options), callback);
+ }
+
+ // stateful
+ /**
+ * Predict the next character based on the model's current state.
+ * @param {number} temp
+ * @param {function} callback - Optional. A function to be called when the
+ * model finished adding the seed. If no callback is provided, it will
+ * return a promise that will be resolved once the prediction has been generated.
+ */
+ async predict(temp, callback) {
+ let probabilitiesNormalized = [];
+ const temperature = temp > 0 ? temp : 0.1;
+ const outputH = this.state.h[1];
+ const weightedResult = tf.matMul(outputH, this.model.fullyConnectedWeights);
+ const logits = tf.add(weightedResult, this.model.fullyConnectedBiases);
+ const divided = tf.div(logits, tf.tensor(temperature));
+ const probabilities = tf.exp(divided);
+ probabilitiesNormalized = await tf.div(probabilities, tf.sum(probabilities)).data();
+
+ const sample = sampleFromDistribution(probabilitiesNormalized);
+ const result = Object.keys(this.vocab).find(key => this.vocab[key] === sample);
+ this.probabilities = probabilitiesNormalized;
+ if (callback) {
+ callback(result);
+ }
+ /* eslint max-len: ["error", { "code": 180 }] */
+ const pm = Object.keys(this.vocab).map(c => ({
+ char: c,
+ probability: this.probabilities[this.vocab[c]],
+ }));
+ return {
+ sample: result,
+ probabilities: pm,
+ };
+ }
+
+ /**
+ * Feed a string of characters to the model state.
+ * @param {String} inputSeed - A string to feed the charRNN model state.
+ * @param {function} callback - Optional. A function to be called when
+ * the model finished adding the seed. If no callback is provided, it
+ * will return a promise that will be resolved once seed has been fed.
+ */
+ async feed(inputSeed, callback) {
+ await this.ready;
+ const seed = Array.from(inputSeed);
+ const encodedInput = [];
+
+ seed.forEach(char => {
+ encodedInput.push(this.vocab[char]);
+ });
+
+ let input = encodedInput[0];
+ for (let i = 0; i < seed.length; i += 1) {
+ const onehotBuffer = await tf.buffer([1, this.vocabSize]);
+ onehotBuffer.set(1.0, 0, input);
+ const onehot = onehotBuffer.toTensor();
+ let output;
+ if (this.model.embedding) {
+ const embedded = tf.matMul(onehot, this.model.embedding);
+ output = tf.multiRNNCell(this.cells, embedded, this.state.c, this.state.h);
+ } else {
+ output = tf.multiRNNCell(this.cells, onehot, this.state.c, this.state.h);
+ }
+ this.state.c = output[0];
+ this.state.h = output[1];
+ input = encodedInput[i];
+ }
+ if (callback) {
+ callback();
+ }
+ }
+}
+
+const charRNN = (modelPath = "./", callback) => new CharRNN(modelPath, callback);
+
+export default charRNN;
+ |
2. go to the latest draft and click the edit button
-3. click: publish the release
-
-
+3. click: publish the release -- this will trigger a github workflow that will:
+ * get the latest tag version
+ * update the package.json
+ * update the readme with the latest version (pulled from the package.json)
+ * run npm install
+ * add, commit, and push those changes to `main`
+ * build the library
+ * and publish to npm
***
diff --git a/examples/CONTRIBUTING.md b/examples/CONTRIBUTING.md
index d7353f77a..f12f2d8d2 100644
--- a/examples/CONTRIBUTING.md
+++ b/examples/CONTRIBUTING.md
@@ -24,7 +24,7 @@ Please refer to [#1](https://github.com/ml5js/ml5-examples/issues/1) (thanks [@d
## Organizing Examples of different features
-If you're interested to make example, you can organize them as heirarchy like so:
+If you're interested to make example, you can organize them as hierarchy like so:
```
/FeatureName
diff --git a/examples/d3/KMeans/KMeans_GaussianClusterDemo/data/gaussian2d_3clusters.csv b/examples/d3/DBSCAN/DBSCAN_Cluster/data/gaussian2d_1.55clusters.csv
similarity index 99%
rename from examples/d3/KMeans/KMeans_GaussianClusterDemo/data/gaussian2d_3clusters.csv
rename to examples/d3/DBSCAN/DBSCAN_Cluster/data/gaussian2d_1.55clusters.csv
index 3976c0e52..072b7adac 100644
--- a/examples/d3/KMeans/KMeans_GaussianClusterDemo/data/gaussian2d_3clusters.csv
+++ b/examples/d3/DBSCAN/DBSCAN_Cluster/data/gaussian2d_1.55clusters.csv
@@ -1,4 +1,4 @@
-x1,x2
+x,y
-7.849704268160314,-8.674808581953744
-8.69664575596503,-7.692212927859845
-1.0359915243020554,6.827713353515913
diff --git a/examples/d3/KMeans/KMeans_GaussianClusterDemo/data/gaussian2d_4clusters.csv b/examples/d3/DBSCAN/DBSCAN_Cluster/data/gaussian2d_1.56clusters.csv
similarity index 99%
rename from examples/d3/KMeans/KMeans_GaussianClusterDemo/data/gaussian2d_4clusters.csv
rename to examples/d3/DBSCAN/DBSCAN_Cluster/data/gaussian2d_1.56clusters.csv
index ae1b83fa1..789ee683f 100644
--- a/examples/d3/KMeans/KMeans_GaussianClusterDemo/data/gaussian2d_4clusters.csv
+++ b/examples/d3/DBSCAN/DBSCAN_Cluster/data/gaussian2d_1.56clusters.csv
@@ -1,4 +1,4 @@
-x1,x2
+x,y
-10.718663580210745,9.900472871349784
-7.9399489058067685,10.718096194668659
-9.199206305612545,6.923022960440686
diff --git a/examples/d3/KMeans/KMeans_GaussianClusterDemo/data/gaussian2d_2clusters.csv b/examples/d3/DBSCAN/DBSCAN_Cluster/data/gaussian2d_2clusters.csv
similarity index 99%
rename from examples/d3/KMeans/KMeans_GaussianClusterDemo/data/gaussian2d_2clusters.csv
rename to examples/d3/DBSCAN/DBSCAN_Cluster/data/gaussian2d_2clusters.csv
index 9abaa0cd6..d25fe19cf 100644
--- a/examples/d3/KMeans/KMeans_GaussianClusterDemo/data/gaussian2d_2clusters.csv
+++ b/examples/d3/DBSCAN/DBSCAN_Cluster/data/gaussian2d_2clusters.csv
@@ -1,4 +1,4 @@
-x1,x2
+x,y
2.498895550686414,1.1683342316471301
5.073222450036444,0.8623713271387572
4.672790880324094,2.6418773621498803
diff --git a/examples/d3/KMeans/KMeans_GaussianClusterDemo/data/gaussian2d_circleclusters.csv b/examples/d3/DBSCAN/DBSCAN_Cluster/data/gaussian2d_circleclusters.csv
similarity index 99%
rename from examples/d3/KMeans/KMeans_GaussianClusterDemo/data/gaussian2d_circleclusters.csv
rename to examples/d3/DBSCAN/DBSCAN_Cluster/data/gaussian2d_circleclusters.csv
index efbf5fb70..e22dddb59 100644
--- a/examples/d3/KMeans/KMeans_GaussianClusterDemo/data/gaussian2d_circleclusters.csv
+++ b/examples/d3/DBSCAN/DBSCAN_Cluster/data/gaussian2d_circleclusters.csv
@@ -1,4 +1,4 @@
-x1,x2
+x,y
-0.9914878645939103,0.12004373871846165
-0.8064234011393798,-0.5891152116668836
-0.4265373027932866,-0.6749570357883385
diff --git a/examples/d3/KMeans/KMeans_GaussianClusterDemo/data/gaussian2d_moonclusters.csv b/examples/d3/DBSCAN/DBSCAN_Cluster/data/gaussian2d_moonclusters.csv
similarity index 99%
rename from examples/d3/KMeans/KMeans_GaussianClusterDemo/data/gaussian2d_moonclusters.csv
rename to examples/d3/DBSCAN/DBSCAN_Cluster/data/gaussian2d_moonclusters.csv
index ad01d28a1..32dc2344d 100644
--- a/examples/d3/KMeans/KMeans_GaussianClusterDemo/data/gaussian2d_moonclusters.csv
+++ b/examples/d3/DBSCAN/DBSCAN_Cluster/data/gaussian2d_moonclusters.csv
@@ -1,4 +1,4 @@
-x1,x2
+x,y
2.001164955520126,0.43029947571735944
-0.6744557747817002,0.7365153394030783
0.13616835175111489,1.0001429333017973
diff --git a/examples/d3/DBSCAN/DBSCAN_Cluster/index.html b/examples/d3/DBSCAN/DBSCAN_Cluster/index.html
new file mode 100644
index 000000000..876aa1575
--- /dev/null
+++ b/examples/d3/DBSCAN/DBSCAN_Cluster/index.html
@@ -0,0 +1,22 @@
+
+
+ DBSCAN is a density-based clustering non-parametric algorithm: given a set of points in some space, it groups together points that are closely packed together (points with many nearby neighbors), + marking as outliers points that lie alone in low-density regions (whose nearest neighbors are too far away). + DBSCAN is one of the most common clustering algorithms and also most cited in scientific literature. +
+