From 431198ef12eed410f01d32e65fdc0e25b55cc641 Mon Sep 17 00:00:00 2001 From: Tyler Date: Tue, 28 Nov 2023 21:48:34 +0000 Subject: [PATCH] adding to docs --- docs/source/Univariate.rst | 113 ++++++++++++++++++ .../univariate_examples/discrete_example.py | 2 +- .../univariate_fitter_example.py | 2 +- media/univariate_discrete_example_figure1.png | Bin 0 -> 96705 bytes media/univariate_fitter_example_figure1.png | Bin 0 -> 100719 bytes 5 files changed, 115 insertions(+), 2 deletions(-) create mode 100644 media/univariate_discrete_example_figure1.png create mode 100644 media/univariate_fitter_example_figure1.png diff --git a/docs/source/Univariate.rst b/docs/source/Univariate.rst index 6626108..c70d86f 100644 --- a/docs/source/Univariate.rst +++ b/docs/source/Univariate.rst @@ -153,3 +153,116 @@ We can then easily reload our saved model:: loaded_fitted_gamma = load('gamma.pickle') + + +Discrete Example +--------------------- +Here we use the poisson distribution, though all methods and attributes are generalized. +We see this works in exactly the same way as continuous distributions.:: + + import numpy as np + import pandas as pd + + # generating random variables + from sklarpy.univariate import poisson + + num_generate: int = 10000 + poisson_rvs: np.ndarray = poisson.rvs((num_generate, ), (4,)) + rvs_df: pd.DataFrame = pd.DataFrame(poisson_rvs, columns=['rvs'], dtype=int) + + # fitting a poisson distribution to a dataframe of rvs + fitted_poisson = poisson.fit(rvs_df) + + # we can easily retrieve the fitted parameters + fitted_poisson_params: tuple = fitted_poisson.params + print(fitted_poisson_params) + +.. code-block:: text: + + (3.992,) + +We can also print a summary of our fit:: + + summary: pd.DataFrame = fitted_poisson.summary + print(summary) + +.. code-block:: text: + + summary + Parametric/Non-Parametric Parametric + Discrete/Continuous discrete + Distribution poisson + #Params 1 + param0 3.985 + Support (0, inf) + Fitted Domain (0, 12) + chi-square statistic 7.059903 + chi-square p-value 1.0 + chi-square @ 10% True + chi-square @ 5% True + chi-square @ 1% True + Likelihood 0.0 + Log-Likelihood -2100.955867 + AIC 4203.911734 + BIC 4208.819489 + Sum of Squared Error 0.044802 + #Fitted Data Points 1000 + +And plot our fitted distribution:: + + fitted_poisson.plot() + +.. image:: https://github.com/tfm000/sklarpy/blob/docs/readthedocs/media/univariate_discrete_example_figure1.png?raw=true + :alt: poisson plot + :align: center + +And save:: + + fitted_poisson.save() + + +We can then easily reload our saved model:: + + from sklarpy import load + + loaded_fitted_poisson = load('poisson.pickle') + +UnivariateFitter Example +------------------------- +Here we use the UnivariateFitter object to fit a distribution to a dataset.:: + + import numpy as np + + # generating random variables + from sklarpy.univariate import normal + + num_generate: int = 10000 + # generating a 1d array of N(1, 1) random variables + normal_rvs1: np.ndarray = normal.rvs((num_generate,), (1, 1)) + # generating a 1d array of N(2, 3) random variables + normal_rvs2: np.ndarray = normal.rvs((num_generate,), (0, 3)) + rvs = normal_rvs1 * normal_rvs2 + + # applying UnivariateFitter to our product of normal random variables + from sklarpy.univariate import UnivariateFitter + + ufitter: UnivariateFitter = UnivariateFitter(rvs) + ufitter.fit() + + # finding our best fit + best_fit = ufitter.get_best(significant=False) + best_fit.plot() + +.. image:: https://github.com/tfm000/sklarpy/blob/docs/readthedocs/media/univariate_fitter_example_figure1.png?raw=true + :alt: poisson plot + :align: center + +We can also save our UnivariateFitter object:: + + ufitter.save() + +We can then easily reload this:: + from sklarpy import load + + loaded_ufitter = load('UnivariateFitter.pickle') + loaded_best_fit = loaded_ufitter.get_best(significant=False) diff --git a/examples/univariate_examples/discrete_example.py b/examples/univariate_examples/discrete_example.py index e29b1a3..409cc8b 100644 --- a/examples/univariate_examples/discrete_example.py +++ b/examples/univariate_examples/discrete_example.py @@ -8,7 +8,7 @@ # generating random variables from sklarpy.univariate import poisson -num_generate: int = 10000 +num_generate: int = 100 poisson_rvs: np.ndarray = poisson.rvs((num_generate, ), (4,)) rvs_df: pd.DataFrame = pd.DataFrame(poisson_rvs, columns=['rvs'], dtype=int) diff --git a/examples/univariate_examples/univariate_fitter_example.py b/examples/univariate_examples/univariate_fitter_example.py index 9b88fa1..9ceb4c4 100644 --- a/examples/univariate_examples/univariate_fitter_example.py +++ b/examples/univariate_examples/univariate_fitter_example.py @@ -5,7 +5,7 @@ # generating random variables from sklarpy.univariate import normal -num_generate: int = 10000 +num_generate: int = 1000 # generating a 1d array of N(1, 1) random variables normal_rvs1: np.ndarray = normal.rvs((num_generate,), (1, 1)) # generating a 1d array of N(2, 3) random 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