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diff --git a/python-distfit.spec b/python-distfit.spec new file mode 100644 index 0000000..f4b3230 --- /dev/null +++ b/python-distfit.spec @@ -0,0 +1,846 @@ +%global _empty_manifest_terminate_build 0 +Name: python-distfit +Version: 1.6.10 +Release: 1 +Summary: distfit is a python library for probability density fitting. +License: MIT License +URL: https://erdogant.github.io/distfit +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b6/f9/cf5ea4b3912f22db6593254c0ef4a0e68eb20116502c112954afa3d6f539/distfit-1.6.10.tar.gz +BuildArch: noarch + +Requires: python3-packaging +Requires: python3-matplotlib +Requires: python3-numpy +Requires: python3-pandas +Requires: python3-tqdm +Requires: python3-statsmodels +Requires: python3-scipy +Requires: python3-pypickle +Requires: python3-colourmap + +%description +<p align="center"> + <a href="https://erdogant.github.io/pca/"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/logo.png" width="600" /> + </a> +</p> + +[](https://img.shields.io/pypi/pyversions/distfit) +[](https://pypi.org/project/distfit/) +[](https://erdogant.github.io/distfit/) +[](https://github.com/erdogant/distfit/) +[](https://pepy.tech/project/distfit) +[](https://pepy.tech/project/distfit) +[](https://github.com/erdogant/distfit/blob/master/LICENSE) +[](https://github.com/erdogant/distfit/network) +[](https://github.com/erdogant/distfit/issues) +[](http://www.repostatus.org/#active) +[](https://zenodo.org/badge/latestdoi/231843440) +[](https://erdogant.github.io/distfit/pages/html/Documentation.html#medium-blog) +[](https://erdogant.github.io/distfit/pages/html/Documentation.html#colab-notebook) +[](https://erdogant.github.io/distfit/pages/html/Documentation.html#) +<!---[](https://www.buymeacoffee.com/erdogant)--> +<!---[](https://erdogant.github.io/donate/?currency=USD&amount=5)--> + +# +### [Read the Medium Blog for more information](https://erdogant.github.io/distfit/pages/html/Documentation.html#medium-blog) +# + +``distfit`` is a python package for probability density fitting of univariate distributions for random variables. +With the random variable as an input, distfit can find the best fit for parametric, non-parametric, and discrete distributions. + +* For the parametric approach, the distfit library can determine the best fit across 89 theoretical distributions. + To score the fit, one of the scoring statistics for the good-of-fitness test can be used used, such as RSS/SSE, Wasserstein, + Kolmogorov-Smirnov (KS), or Energy. After finding the best-fitted theoretical distribution, the loc, scale, + and arg parameters are returned, such as mean and standard deviation for normal distribution. + +* For the non-parametric approach, the distfit library contains two methods, the quantile and percentile method. + Both methods assume that the data does not follow a specific probability distribution. In the case of the quantile method, + the quantiles of the data are modeled whereas for the percentile method, the percentiles are modeled. + +* In case the dataset contains discrete values, the distift library contains the option for discrete fitting. + The best fit is then derived using the binomial distribution. + +# +**⭐️ Star this repo if you like it ⭐️** +# + +### [Documentation pages](https://erdogant.github.io/distfit/) + +On the [documentation pages](https://erdogant.github.io/distfit/) you can find detailed information about the ``distfit`` library with many examples. + +# + + +### Installation + +##### Install distfit from PyPI +```bash +pip install distfit +``` + +##### Install from github source (beta version) +```bash + install git+https://github.com/erdogant/distfit +``` + +##### Check version +```python +import distfit +print(distfit.__version__) +``` + +##### The following functions are available after installation: + +```python +# Import library +from distfit import distfit + +dfit = distfit() # Initialize +dfit.fit_transform(X) # Fit distributions on empirical data X +dfit.predict(y) # Predict the probability of the resonse variables +dfit.plot() # Plot the best fitted distribution (y is included if prediction is made) +``` + +<hr> + +### Examples + +# + +##### [Example: Quick start to find best fit for your input data](https://erdogant.github.io/distfit/pages/html/Examples.html#) + +```python + +# [distfit] >INFO> fit +# [distfit] >INFO> transform +# [distfit] >INFO> [norm ] [0.00 sec] [RSS: 0.00108326] [loc=-0.048 scale=1.997] +# [distfit] >INFO> [expon ] [0.00 sec] [RSS: 0.404237] [loc=-6.897 scale=6.849] +# [distfit] >INFO> [pareto ] [0.00 sec] [RSS: 0.404237] [loc=-536870918.897 scale=536870912.000] +# [distfit] >INFO> [dweibull ] [0.06 sec] [RSS: 0.0115552] [loc=-0.031 scale=1.722] +# [distfit] >INFO> [t ] [0.59 sec] [RSS: 0.00108349] [loc=-0.048 scale=1.997] +# [distfit] >INFO> [genextreme] [0.17 sec] [RSS: 0.00300806] [loc=-0.806 scale=1.979] +# [distfit] >INFO> [gamma ] [0.05 sec] [RSS: 0.00108459] [loc=-1862.903 scale=0.002] +# [distfit] >INFO> [lognorm ] [0.32 sec] [RSS: 0.00121597] [loc=-110.597 scale=110.530] +# [distfit] >INFO> [beta ] [0.10 sec] [RSS: 0.00105629] [loc=-16.364 scale=32.869] +# [distfit] >INFO> [uniform ] [0.00 sec] [RSS: 0.287339] [loc=-6.897 scale=14.437] +# [distfit] >INFO> [loggamma ] [0.12 sec] [RSS: 0.00109042] [loc=-370.746 scale=55.722] +# [distfit] >INFO> Compute confidence intervals [parametric] +# [distfit] >INFO> Compute significance for 9 samples. +# [distfit] >INFO> Multiple test correction method applied: [fdr_bh]. +# [distfit] >INFO> Create PDF plot for the parametric method. +# [distfit] >INFO> Mark 5 significant regions +# [distfit] >INFO> Estimated distribution: beta [loc:-16.364265, scale:32.868811] +``` + +<p align="left"> + <a href="https://erdogant.github.io/distfit/pages/html/Examples.html#make-predictions"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/example_figP4c.png" width="450" /> + </a> +</p> + + +# + +##### [Example: Plot summary of the tested distributions](https://erdogant.github.io/distfit/pages/html/Examples.html#plot-rss) + +After we have a fitted model, we can make some predictions using the theoretical distributions. +After making some predictions, we can plot again but now the predictions are automatically included. + +<p align="left"> + <a href="https://erdogant.github.io/distfit/pages/html/Examples.html#plot-rss"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/fig1_summary.png" width="450" /> + </a> +</p> + +# + +##### [Example: Make predictions using the fitted distribution](https://erdogant.github.io/distfit/pages/html/Examples.html#make-predictions) + + +<p align="left"> + <a href="https://erdogant.github.io/distfit/pages/html/Examples.html#make-predictions"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/example_figP1a.png" width="450" /> + </a> +</p> + + + +# + +##### [Example: Test for one specific distributions](https://erdogant.github.io/distfit/pages/html/Examples.html#fit-for-one-specific-distribution) + +The full list of distributions is listed here: https://erdogant.github.io/distfit/pages/html/Parametric.html + +<p align="left"> + <a href="https://erdogant.github.io/distfit/pages/html/Examples.html#fit-for-one-specific-distribution"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/example_figP3b.png" width="450" /> + </a> +</p> + + +# + +##### [Example: Test for multiple distributions](https://erdogant.github.io/distfit/pages/html/Examples.html#fit-for-multiple-distributions) + +The full list of distributions is listed here: https://erdogant.github.io/distfit/pages/html/Parametric.html + +<p align="left"> + <a href="https://erdogant.github.io/distfit/pages/html/Examples.html#fit-for-multiple-distributions"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/example_figP2b.png" width="450" /> + </a> +</p> + + +# + + +##### [Example: Fit discrete distribution](https://erdogant.github.io/distfit/pages/html/Discrete.html) + + +```python +from scipy.stats import binom +# Generate random numbers + +# Set parameters for the test-case +n = 8 +p = 0.5 + +# Generate 10000 samples of the distribution of (n, p) +X = binom(n, p).rvs(10000) +print(X) + +# [5 1 4 5 5 6 2 4 6 5 4 4 4 7 3 4 4 2 3 3 4 4 5 1 3 2 7 4 5 2 3 4 3 3 2 3 5 +# 4 6 7 6 2 4 3 3 5 3 5 3 4 4 4 7 5 4 5 3 4 3 3 4 3 3 6 3 3 5 4 4 2 3 2 5 7 +# 5 4 8 3 4 3 5 4 3 5 5 2 5 6 7 4 5 5 5 4 4 3 4 5 6 2...] + +# Import distfit +from distfit import distfit + +# Initialize for discrete distribution fitting +dfit = distfit(method='discrete') + +# Run distfit to and determine whether we can find the parameters from the data. +dfit.fit_transform(X) + +# [distfit] >fit.. +# [distfit] >transform.. +# [distfit] >Fit using binomial distribution.. +# [distfit] >[binomial] [SSE: 7.79] [n: 8] [p: 0.499959] [chi^2: 1.11] +# [distfit] >Compute confidence interval [discrete] + +``` +<p align="left"> + <a href="https://erdogant.github.io/distfit/pages/html/Discrete.html"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/binomial_plot.png" width="450" /> + </a> +</p> + +# + +##### [Example: Make predictions on unseen data for discrete distribution](https://erdogant.github.io/distfit/pages/html/Discrete.html#make-predictions) + + +<p align="left"> + <a href="https://erdogant.github.io/distfit/pages/html/Discrete.html#make-predictions"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/binomial_plot_predict.png" width="450" /> + </a> +</p> + + +# + + +##### [Example: Generate samples based on the fitted distribution](https://erdogant.github.io/distfit/pages/html/Generate.html) + +<hr> + +### Contributors +Setting up and maintaining distfit has been possible thanks to users and contributors. Thanks: + +<p align="left"> + <a href="https://github.com/erdogant/distfit/graphs/contributors"> + <img src="https://contrib.rocks/image?repo=erdogant/distfit" /> + </a> +</p> + + +### Citation +Please cite ``distfit`` in your publications if this is useful for your research. See column right for citation information. + +### Maintainer +* Erdogan Taskesen, github: [erdogant](https://github.com/erdogant) +* Contributions are welcome. +* If you wish to buy me a <a href="https://erdogant.github.io/donate/?currency=USD&amount=5">Coffee</a> for this work, it is very appreciated :) + + + + +%package -n python3-distfit +Summary: distfit is a python library for probability density fitting. +Provides: python-distfit +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-distfit +<p align="center"> + <a href="https://erdogant.github.io/pca/"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/logo.png" width="600" /> + </a> +</p> + +[](https://img.shields.io/pypi/pyversions/distfit) +[](https://pypi.org/project/distfit/) +[](https://erdogant.github.io/distfit/) +[](https://github.com/erdogant/distfit/) +[](https://pepy.tech/project/distfit) +[](https://pepy.tech/project/distfit) +[](https://github.com/erdogant/distfit/blob/master/LICENSE) +[](https://github.com/erdogant/distfit/network) +[](https://github.com/erdogant/distfit/issues) +[](http://www.repostatus.org/#active) +[](https://zenodo.org/badge/latestdoi/231843440) +[](https://erdogant.github.io/distfit/pages/html/Documentation.html#medium-blog) +[](https://erdogant.github.io/distfit/pages/html/Documentation.html#colab-notebook) +[](https://erdogant.github.io/distfit/pages/html/Documentation.html#) +<!---[](https://www.buymeacoffee.com/erdogant)--> +<!---[](https://erdogant.github.io/donate/?currency=USD&amount=5)--> + +# +### [Read the Medium Blog for more information](https://erdogant.github.io/distfit/pages/html/Documentation.html#medium-blog) +# + +``distfit`` is a python package for probability density fitting of univariate distributions for random variables. +With the random variable as an input, distfit can find the best fit for parametric, non-parametric, and discrete distributions. + +* For the parametric approach, the distfit library can determine the best fit across 89 theoretical distributions. + To score the fit, one of the scoring statistics for the good-of-fitness test can be used used, such as RSS/SSE, Wasserstein, + Kolmogorov-Smirnov (KS), or Energy. After finding the best-fitted theoretical distribution, the loc, scale, + and arg parameters are returned, such as mean and standard deviation for normal distribution. + +* For the non-parametric approach, the distfit library contains two methods, the quantile and percentile method. + Both methods assume that the data does not follow a specific probability distribution. In the case of the quantile method, + the quantiles of the data are modeled whereas for the percentile method, the percentiles are modeled. + +* In case the dataset contains discrete values, the distift library contains the option for discrete fitting. + The best fit is then derived using the binomial distribution. + +# +**⭐️ Star this repo if you like it ⭐️** +# + +### [Documentation pages](https://erdogant.github.io/distfit/) + +On the [documentation pages](https://erdogant.github.io/distfit/) you can find detailed information about the ``distfit`` library with many examples. + +# + + +### Installation + +##### Install distfit from PyPI +```bash +pip install distfit +``` + +##### Install from github source (beta version) +```bash + install git+https://github.com/erdogant/distfit +``` + +##### Check version +```python +import distfit +print(distfit.__version__) +``` + +##### The following functions are available after installation: + +```python +# Import library +from distfit import distfit + +dfit = distfit() # Initialize +dfit.fit_transform(X) # Fit distributions on empirical data X +dfit.predict(y) # Predict the probability of the resonse variables +dfit.plot() # Plot the best fitted distribution (y is included if prediction is made) +``` + +<hr> + +### Examples + +# + +##### [Example: Quick start to find best fit for your input data](https://erdogant.github.io/distfit/pages/html/Examples.html#) + +```python + +# [distfit] >INFO> fit +# [distfit] >INFO> transform +# [distfit] >INFO> [norm ] [0.00 sec] [RSS: 0.00108326] [loc=-0.048 scale=1.997] +# [distfit] >INFO> [expon ] [0.00 sec] [RSS: 0.404237] [loc=-6.897 scale=6.849] +# [distfit] >INFO> [pareto ] [0.00 sec] [RSS: 0.404237] [loc=-536870918.897 scale=536870912.000] +# [distfit] >INFO> [dweibull ] [0.06 sec] [RSS: 0.0115552] [loc=-0.031 scale=1.722] +# [distfit] >INFO> [t ] [0.59 sec] [RSS: 0.00108349] [loc=-0.048 scale=1.997] +# [distfit] >INFO> [genextreme] [0.17 sec] [RSS: 0.00300806] [loc=-0.806 scale=1.979] +# [distfit] >INFO> [gamma ] [0.05 sec] [RSS: 0.00108459] [loc=-1862.903 scale=0.002] +# [distfit] >INFO> [lognorm ] [0.32 sec] [RSS: 0.00121597] [loc=-110.597 scale=110.530] +# [distfit] >INFO> [beta ] [0.10 sec] [RSS: 0.00105629] [loc=-16.364 scale=32.869] +# [distfit] >INFO> [uniform ] [0.00 sec] [RSS: 0.287339] [loc=-6.897 scale=14.437] +# [distfit] >INFO> [loggamma ] [0.12 sec] [RSS: 0.00109042] [loc=-370.746 scale=55.722] +# [distfit] >INFO> Compute confidence intervals [parametric] +# [distfit] >INFO> Compute significance for 9 samples. +# [distfit] >INFO> Multiple test correction method applied: [fdr_bh]. +# [distfit] >INFO> Create PDF plot for the parametric method. +# [distfit] >INFO> Mark 5 significant regions +# [distfit] >INFO> Estimated distribution: beta [loc:-16.364265, scale:32.868811] +``` + +<p align="left"> + <a href="https://erdogant.github.io/distfit/pages/html/Examples.html#make-predictions"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/example_figP4c.png" width="450" /> + </a> +</p> + + +# + +##### [Example: Plot summary of the tested distributions](https://erdogant.github.io/distfit/pages/html/Examples.html#plot-rss) + +After we have a fitted model, we can make some predictions using the theoretical distributions. +After making some predictions, we can plot again but now the predictions are automatically included. + +<p align="left"> + <a href="https://erdogant.github.io/distfit/pages/html/Examples.html#plot-rss"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/fig1_summary.png" width="450" /> + </a> +</p> + +# + +##### [Example: Make predictions using the fitted distribution](https://erdogant.github.io/distfit/pages/html/Examples.html#make-predictions) + + +<p align="left"> + <a href="https://erdogant.github.io/distfit/pages/html/Examples.html#make-predictions"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/example_figP1a.png" width="450" /> + </a> +</p> + + + +# + +##### [Example: Test for one specific distributions](https://erdogant.github.io/distfit/pages/html/Examples.html#fit-for-one-specific-distribution) + +The full list of distributions is listed here: https://erdogant.github.io/distfit/pages/html/Parametric.html + +<p align="left"> + <a href="https://erdogant.github.io/distfit/pages/html/Examples.html#fit-for-one-specific-distribution"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/example_figP3b.png" width="450" /> + </a> +</p> + + +# + +##### [Example: Test for multiple distributions](https://erdogant.github.io/distfit/pages/html/Examples.html#fit-for-multiple-distributions) + +The full list of distributions is listed here: https://erdogant.github.io/distfit/pages/html/Parametric.html + +<p align="left"> + <a href="https://erdogant.github.io/distfit/pages/html/Examples.html#fit-for-multiple-distributions"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/example_figP2b.png" width="450" /> + </a> +</p> + + +# + + +##### [Example: Fit discrete distribution](https://erdogant.github.io/distfit/pages/html/Discrete.html) + + +```python +from scipy.stats import binom +# Generate random numbers + +# Set parameters for the test-case +n = 8 +p = 0.5 + +# Generate 10000 samples of the distribution of (n, p) +X = binom(n, p).rvs(10000) +print(X) + +# [5 1 4 5 5 6 2 4 6 5 4 4 4 7 3 4 4 2 3 3 4 4 5 1 3 2 7 4 5 2 3 4 3 3 2 3 5 +# 4 6 7 6 2 4 3 3 5 3 5 3 4 4 4 7 5 4 5 3 4 3 3 4 3 3 6 3 3 5 4 4 2 3 2 5 7 +# 5 4 8 3 4 3 5 4 3 5 5 2 5 6 7 4 5 5 5 4 4 3 4 5 6 2...] + +# Import distfit +from distfit import distfit + +# Initialize for discrete distribution fitting +dfit = distfit(method='discrete') + +# Run distfit to and determine whether we can find the parameters from the data. +dfit.fit_transform(X) + +# [distfit] >fit.. +# [distfit] >transform.. +# [distfit] >Fit using binomial distribution.. +# [distfit] >[binomial] [SSE: 7.79] [n: 8] [p: 0.499959] [chi^2: 1.11] +# [distfit] >Compute confidence interval [discrete] + +``` +<p align="left"> + <a href="https://erdogant.github.io/distfit/pages/html/Discrete.html"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/binomial_plot.png" width="450" /> + </a> +</p> + +# + +##### [Example: Make predictions on unseen data for discrete distribution](https://erdogant.github.io/distfit/pages/html/Discrete.html#make-predictions) + + +<p align="left"> + <a href="https://erdogant.github.io/distfit/pages/html/Discrete.html#make-predictions"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/binomial_plot_predict.png" width="450" /> + </a> +</p> + + +# + + +##### [Example: Generate samples based on the fitted distribution](https://erdogant.github.io/distfit/pages/html/Generate.html) + +<hr> + +### Contributors +Setting up and maintaining distfit has been possible thanks to users and contributors. Thanks: + +<p align="left"> + <a href="https://github.com/erdogant/distfit/graphs/contributors"> + <img src="https://contrib.rocks/image?repo=erdogant/distfit" /> + </a> +</p> + + +### Citation +Please cite ``distfit`` in your publications if this is useful for your research. See column right for citation information. + +### Maintainer +* Erdogan Taskesen, github: [erdogant](https://github.com/erdogant) +* Contributions are welcome. +* If you wish to buy me a <a href="https://erdogant.github.io/donate/?currency=USD&amount=5">Coffee</a> for this work, it is very appreciated :) + + + + +%package help +Summary: Development documents and examples for distfit +Provides: python3-distfit-doc +%description help +<p align="center"> + <a href="https://erdogant.github.io/pca/"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/logo.png" width="600" /> + </a> +</p> + +[](https://img.shields.io/pypi/pyversions/distfit) +[](https://pypi.org/project/distfit/) +[](https://erdogant.github.io/distfit/) +[](https://github.com/erdogant/distfit/) +[](https://pepy.tech/project/distfit) +[](https://pepy.tech/project/distfit) +[](https://github.com/erdogant/distfit/blob/master/LICENSE) +[](https://github.com/erdogant/distfit/network) +[](https://github.com/erdogant/distfit/issues) +[](http://www.repostatus.org/#active) +[](https://zenodo.org/badge/latestdoi/231843440) +[](https://erdogant.github.io/distfit/pages/html/Documentation.html#medium-blog) +[](https://erdogant.github.io/distfit/pages/html/Documentation.html#colab-notebook) +[](https://erdogant.github.io/distfit/pages/html/Documentation.html#) +<!---[](https://www.buymeacoffee.com/erdogant)--> +<!---[](https://erdogant.github.io/donate/?currency=USD&amount=5)--> + +# +### [Read the Medium Blog for more information](https://erdogant.github.io/distfit/pages/html/Documentation.html#medium-blog) +# + +``distfit`` is a python package for probability density fitting of univariate distributions for random variables. +With the random variable as an input, distfit can find the best fit for parametric, non-parametric, and discrete distributions. + +* For the parametric approach, the distfit library can determine the best fit across 89 theoretical distributions. + To score the fit, one of the scoring statistics for the good-of-fitness test can be used used, such as RSS/SSE, Wasserstein, + Kolmogorov-Smirnov (KS), or Energy. After finding the best-fitted theoretical distribution, the loc, scale, + and arg parameters are returned, such as mean and standard deviation for normal distribution. + +* For the non-parametric approach, the distfit library contains two methods, the quantile and percentile method. + Both methods assume that the data does not follow a specific probability distribution. In the case of the quantile method, + the quantiles of the data are modeled whereas for the percentile method, the percentiles are modeled. + +* In case the dataset contains discrete values, the distift library contains the option for discrete fitting. + The best fit is then derived using the binomial distribution. + +# +**⭐️ Star this repo if you like it ⭐️** +# + +### [Documentation pages](https://erdogant.github.io/distfit/) + +On the [documentation pages](https://erdogant.github.io/distfit/) you can find detailed information about the ``distfit`` library with many examples. + +# + + +### Installation + +##### Install distfit from PyPI +```bash +pip install distfit +``` + +##### Install from github source (beta version) +```bash + install git+https://github.com/erdogant/distfit +``` + +##### Check version +```python +import distfit +print(distfit.__version__) +``` + +##### The following functions are available after installation: + +```python +# Import library +from distfit import distfit + +dfit = distfit() # Initialize +dfit.fit_transform(X) # Fit distributions on empirical data X +dfit.predict(y) # Predict the probability of the resonse variables +dfit.plot() # Plot the best fitted distribution (y is included if prediction is made) +``` + +<hr> + +### Examples + +# + +##### [Example: Quick start to find best fit for your input data](https://erdogant.github.io/distfit/pages/html/Examples.html#) + +```python + +# [distfit] >INFO> fit +# [distfit] >INFO> transform +# [distfit] >INFO> [norm ] [0.00 sec] [RSS: 0.00108326] [loc=-0.048 scale=1.997] +# [distfit] >INFO> [expon ] [0.00 sec] [RSS: 0.404237] [loc=-6.897 scale=6.849] +# [distfit] >INFO> [pareto ] [0.00 sec] [RSS: 0.404237] [loc=-536870918.897 scale=536870912.000] +# [distfit] >INFO> [dweibull ] [0.06 sec] [RSS: 0.0115552] [loc=-0.031 scale=1.722] +# [distfit] >INFO> [t ] [0.59 sec] [RSS: 0.00108349] [loc=-0.048 scale=1.997] +# [distfit] >INFO> [genextreme] [0.17 sec] [RSS: 0.00300806] [loc=-0.806 scale=1.979] +# [distfit] >INFO> [gamma ] [0.05 sec] [RSS: 0.00108459] [loc=-1862.903 scale=0.002] +# [distfit] >INFO> [lognorm ] [0.32 sec] [RSS: 0.00121597] [loc=-110.597 scale=110.530] +# [distfit] >INFO> [beta ] [0.10 sec] [RSS: 0.00105629] [loc=-16.364 scale=32.869] +# [distfit] >INFO> [uniform ] [0.00 sec] [RSS: 0.287339] [loc=-6.897 scale=14.437] +# [distfit] >INFO> [loggamma ] [0.12 sec] [RSS: 0.00109042] [loc=-370.746 scale=55.722] +# [distfit] >INFO> Compute confidence intervals [parametric] +# [distfit] >INFO> Compute significance for 9 samples. +# [distfit] >INFO> Multiple test correction method applied: [fdr_bh]. +# [distfit] >INFO> Create PDF plot for the parametric method. +# [distfit] >INFO> Mark 5 significant regions +# [distfit] >INFO> Estimated distribution: beta [loc:-16.364265, scale:32.868811] +``` + +<p align="left"> + <a href="https://erdogant.github.io/distfit/pages/html/Examples.html#make-predictions"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/example_figP4c.png" width="450" /> + </a> +</p> + + +# + +##### [Example: Plot summary of the tested distributions](https://erdogant.github.io/distfit/pages/html/Examples.html#plot-rss) + +After we have a fitted model, we can make some predictions using the theoretical distributions. +After making some predictions, we can plot again but now the predictions are automatically included. + +<p align="left"> + <a href="https://erdogant.github.io/distfit/pages/html/Examples.html#plot-rss"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/fig1_summary.png" width="450" /> + </a> +</p> + +# + +##### [Example: Make predictions using the fitted distribution](https://erdogant.github.io/distfit/pages/html/Examples.html#make-predictions) + + +<p align="left"> + <a href="https://erdogant.github.io/distfit/pages/html/Examples.html#make-predictions"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/example_figP1a.png" width="450" /> + </a> +</p> + + + +# + +##### [Example: Test for one specific distributions](https://erdogant.github.io/distfit/pages/html/Examples.html#fit-for-one-specific-distribution) + +The full list of distributions is listed here: https://erdogant.github.io/distfit/pages/html/Parametric.html + +<p align="left"> + <a href="https://erdogant.github.io/distfit/pages/html/Examples.html#fit-for-one-specific-distribution"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/example_figP3b.png" width="450" /> + </a> +</p> + + +# + +##### [Example: Test for multiple distributions](https://erdogant.github.io/distfit/pages/html/Examples.html#fit-for-multiple-distributions) + +The full list of distributions is listed here: https://erdogant.github.io/distfit/pages/html/Parametric.html + +<p align="left"> + <a href="https://erdogant.github.io/distfit/pages/html/Examples.html#fit-for-multiple-distributions"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/example_figP2b.png" width="450" /> + </a> +</p> + + +# + + +##### [Example: Fit discrete distribution](https://erdogant.github.io/distfit/pages/html/Discrete.html) + + +```python +from scipy.stats import binom +# Generate random numbers + +# Set parameters for the test-case +n = 8 +p = 0.5 + +# Generate 10000 samples of the distribution of (n, p) +X = binom(n, p).rvs(10000) +print(X) + +# [5 1 4 5 5 6 2 4 6 5 4 4 4 7 3 4 4 2 3 3 4 4 5 1 3 2 7 4 5 2 3 4 3 3 2 3 5 +# 4 6 7 6 2 4 3 3 5 3 5 3 4 4 4 7 5 4 5 3 4 3 3 4 3 3 6 3 3 5 4 4 2 3 2 5 7 +# 5 4 8 3 4 3 5 4 3 5 5 2 5 6 7 4 5 5 5 4 4 3 4 5 6 2...] + +# Import distfit +from distfit import distfit + +# Initialize for discrete distribution fitting +dfit = distfit(method='discrete') + +# Run distfit to and determine whether we can find the parameters from the data. +dfit.fit_transform(X) + +# [distfit] >fit.. +# [distfit] >transform.. +# [distfit] >Fit using binomial distribution.. +# [distfit] >[binomial] [SSE: 7.79] [n: 8] [p: 0.499959] [chi^2: 1.11] +# [distfit] >Compute confidence interval [discrete] + +``` +<p align="left"> + <a href="https://erdogant.github.io/distfit/pages/html/Discrete.html"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/binomial_plot.png" width="450" /> + </a> +</p> + +# + +##### [Example: Make predictions on unseen data for discrete distribution](https://erdogant.github.io/distfit/pages/html/Discrete.html#make-predictions) + + +<p align="left"> + <a href="https://erdogant.github.io/distfit/pages/html/Discrete.html#make-predictions"> + <img src="https://github.com/erdogant/distfit/blob/master/docs/figs/binomial_plot_predict.png" width="450" /> + </a> +</p> + + +# + + +##### [Example: Generate samples based on the fitted distribution](https://erdogant.github.io/distfit/pages/html/Generate.html) + +<hr> + +### Contributors +Setting up and maintaining distfit has been possible thanks to users and contributors. Thanks: + +<p align="left"> + <a href="https://github.com/erdogant/distfit/graphs/contributors"> + <img src="https://contrib.rocks/image?repo=erdogant/distfit" /> + </a> +</p> + + +### Citation +Please cite ``distfit`` in your publications if this is useful for your research. See column right for citation information. + +### Maintainer +* Erdogan Taskesen, github: [erdogant](https://github.com/erdogant) +* Contributions are welcome. +* If you wish to buy me a <a href="https://erdogant.github.io/donate/?currency=USD&amount=5">Coffee</a> for this work, it is very appreciated :) + + + + +%prep +%autosetup -n distfit-1.6.10 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-distfit -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.6.10-1 +- Package Spec generated |