From b3431686b5dd4774464d0fc1741f04756ac38e96 Mon Sep 17 00:00:00 2001 From: Sherwin Nedaei Janbesaraei Date: Mon, 28 Nov 2022 23:14:21 +0330 Subject: [PATCH] Develop (#12) * add KernelMatrix,SaveOutput,LoadOutput Function Also, additional documentation for each class and function * Update orsvm.py some minor changes on the following functions: -SaveOutput: - function renamed - variables' names corrected -LoadOutput: - function renamed - some corrections to variable names * Update kernels.py Latest revision on comments, Variable corrections and function descriptions * DEBUG SupportMultipliers fucntion logging.info('** Strictly %s first elements of support vectors are selected!',n) replaced with: logging.info('** Strictly',n,' first elements of support vectors are selected!') * DEBUG on SupportMultipliers function correction on svd int type error * DEBUG wrong variable name corrected * DEBUG _IsScientific(n) fucntion logging error recovered * create PredictWithJson function some misspelling also corrected * binary classification encoding added * Update documentation for hyperparameter * Update documentation for hyperparameter * Update kernels.py Gegenbauer kernel Debug, correction on hyperparameter valid domain * Update kernels.py add rbf kernel class * Update orsvm.py * Update orsvm.py bugs about kernel name resolved * Update orsvm.py checking the Convexity of the kernel matrix * Update kernels.py Comments added to Jacobi class * Update kernels.py Gegenbauer hyper parameter check correction * Update kernels.py add hypereparameter range checking for jacobi kernel * Update orsvm.py log error when x shape is not compatible with y shape * Update orsvm.py log error when n_sample = 0 * Update kernels.py debug for order equal or smaller than 0 in chebyshev * Update kernels.py debug for order smaller or equal to zero in legendre kernel * Update kernels.py * Update orsvm.py log error when T is out of range * Update orsvm.py support vector determiner debug * Update kernels.py log error when form input of chebyshev is not valid * Update kernels.py correction on the warning message of chebyshev form error * Update kernels.py debug when KernelParam1 for Gegenbauer is None * Update orsvm.py log error in kernelMatrix function when input size is zero * Update orsvm.py check y.shape[0]!=0 * Update orsvm.py log error when kernel name is not valid in KernelMatrix function * Update orsvm.py Debug SaveToJson * Update orsvm.py Debug LoadJson * Update orsvm.py Debug predictWithJson.( log error when support multiplier is None or n_sample = 0) * Update kernels.py log error when order<0 for gegenbauer and jacobi * Update orsvm.py syntax error fixed * Update orsvm.py Update documentations * Update kernels.py update documentation * Update kernels.py Check range of gamma in rbf * Update orsvm.py Check C is positive or not * Update kernels.py add default value for psi and omega in jacobi * Update orsvm.py bug about C resolved * Update orsvm.py log bug resolved * Update orsvm.py check type of x ,y (should not contain string or bool column) * Update orsvm.py change order of checking bugs in ModelFit and ModelPredict * Update orsvm.py log error when dataset is different from the dataset it was fitted on in PredictWithJson * Update orsvm.py check range of T in Transform function * Update orsvm.py Correct comments to be more clear * Update __init__.py * Update orsvm.py a little bug in saveJson resolved * Update credits.rst * Update orsvm.py * Update orsvm.py logging format corrected * Update requirements.txt * Update .readthedocs.yaml * Update README.md Link to book added * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update setup.py * Delete docs directory Delete docs directory to replace it with a new format * Create test.txt * Add files via upload Adding Build directory of the docs for readthedocs * Add files via upload Uploading source directory of the docs , for readthedocs * Delete conf.py.bak * Delete index.rst.bak * Delete installation.rst.bak * Add files via upload make files and the requirement file of sphinx * Update README.md * Update README.md badges added Co-authored-by: shakiba <51972244+shakibaam@users.noreply.github.com> --- .readthedocs.yaml | 7 + README.md | 11 +- docs/Makefile | 4 +- docs/_build/doctrees/credits.doctree | Bin 5077 -> 0 bytes docs/_build/doctrees/environment.pickle | Bin 43686 -> 0 bytes docs/_build/doctrees/index.doctree | Bin 8344 -> 0 bytes docs/_build/doctrees/installation.doctree | Bin 7720 -> 0 bytes .../notebooks/Gegenbauer_fitting.ipynb | 181 --- .../notebooks/Chebyshev_fitting.doctree | Bin 14641 -> 0 bytes .../notebooks/Gegenbauer_fitting.doctree | Bin 14476 -> 0 bytes .../notebooks/How_to_use_orsvm.doctree | Bin 64568 -> 0 bytes .../doctrees/notebooks/Jacobi_fitting.doctree | Bin 14466 -> 0 bytes .../notebooks/Legendre_fitting.doctree | Bin 15953 -> 0 bytes docs/_build/doctrees/references.doctree 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source/_notebooks}/Legendre_fitting.ipynb (58%) rename docs/{ => source}/_static/identicons.png (100%) rename docs/{ => source}/conf.py (86%) rename docs/{ => source}/credits.rst (75%) create mode 100644 docs/source/index.rst create mode 100644 docs/source/installation.rst rename docs/{ => source}/references.rst (57%) create mode 100644 docs/test.txt diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 3079d5a..38c4280 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -7,5 +7,12 @@ python: - method: pip path: . + # Build documentation in the docs/ directory with Sphinx +sphinx: + configuration: docs/source/conf.py +# Optionally declare the Python requirements required to build your docs +python: + install: + - requirements: docs/requirements.txt diff --git a/README.md b/README.md index 39c9b0d..f95b57f 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,20 @@ -![LOGO](docs/_static/identicons.png) +![LOGO](docs/source/_static//identicons.png) + +![PyPI - License](https://img.shields.io/pypi/l/orsvm) +![PyPI](https://img.shields.io/pypi/v/orsvm) +![PyPI - Status](https://img.shields.io/pypi/status/orsvm) # ORSVM -ORSVM is a free software package which provides a SVM classifier with some novel orthogonal polynomial kernels. +`ORSVM` is a free software package which provides a SVM classifier with some novel orthogonal polynomial kernels. This library provides a complete path of using the SVM classifier from normalization to calculation of SVM equation and the final evaluation. In order to classify the dataset with ORSVM, there is a need to normalize the dataset whether using normal or fractional kernels. ORSVM library needs numpy and cvxopt libraries to be installed. Arrays, matrices and linear algebraic functions have been used repeatedly from numpy and the heart of SVM algorithm which is finding the Support Vectors is done by use of a convex quadratic solver from cvxopt library which is in turn a free python package for convex optimization. + For a comprehensive introduction to fractional orthogonal kernel function and the use cases in SVM, refer to [Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines](https://link.springer.com/book/9789811965524) book. - A suitable guide on this package is available at http://cvxopt.org about installation and how to use. +A suitable guide on `cvxopt` package is available at http://cvxopt.org about installation and how to use. ## Install diff --git a/docs/Makefile b/docs/Makefile index d4bb2cb..d0c3cbf 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -5,8 +5,8 @@ # from the environment for the first two. 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a/docs/_build/doctrees/nbsphinx/notebooks/Gegenbauer_fitting.ipynb +++ /dev/null @@ -1,181 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "bb869c88-ef30-45d5-9a2e-9390567752b4", - "metadata": {}, - "source": [ - "# Fit data-set using Gegenbauer Kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "97d70d86-8249-492d-8526-54bbdde9bedd", - "metadata": {}, - "outputs": [], - "source": [ - "import orsvm\n", - "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "id": "6c3209e4-b26d-47e6-82f7-6f17a2d74da7", - "metadata": {}, - "source": [ - "## Load data-set" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "fdce3d48-c2a3-4d1b-8add-85d3134f86c9", - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_csv(r'D:\\Dev\\Orthogonal_Polynomials\\Datasets\\monks\\1_monks.train',\n", - " names=['label','col1','col2','col3',\n", - " 'col4','col5','col6'],index_col=False)\n", - "\n", - "df.loc[df.label==0, ['label']] = -1 # map \"0\" to \"-1\"\n", - "y_train=df['label'].to_numpy() # convert y_train to numpy array\n", - "df.drop('label', axis=1, inplace=True) # drop the class label\n", - "X_train=df.to_numpy() # convert x_train to numpy array\n", - "\n", - "\n", - "# load test-set\n", - "df = pd.read_csv(r'D:\\Dev\\Orthogonal_Polynomials\\Datasets\\monks\\1_monks.test',\n", - " names=['label','col1','col2','col3',\n", - " 'col4','col5','col6'],index_col=False)\n", - "df.loc[df.label==0, ['label']] = -1\n", - "y_test=df['label'].to_numpy()\n", - "df.drop('label', axis=1, inplace=True)\n", - "X_test=df.to_numpy()" - ] - }, - { - "cell_type": "markdown", - "id": "3f56b9ad-4638-45ae-b6b9-fa06397a6d01", - "metadata": {}, - "source": [ - "## Initiate kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "288fd499-86ea-4522-9fc3-4e523cb30ab9", - "metadata": {}, - "outputs": [], - "source": [ - "# Create an object from Model class of ORSVM\n", - "obj=orsvm.Model(kernel=\"Gegenbauer\",order=4,param1=0.5,T=0.3) \n" - ] - }, - { - "cell_type": "markdown", - "id": "bd6a9696-dcdd-4988-a896-23d975610396", - "metadata": {}, - "source": [ - "## Fit the model and Capture paramaters" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "b88fa0b9-bbe1-461e-9cfc-4089d3f15bbd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "********** 20/09/2021 15:02:03 **********\n", - "** OSVM kernel: Gegenbauer\n", - "** Order: 4\n", - "** Fractional mode, transition : 0.3\n", - "** Avegage support vector determiner selected!\n", - "** sv threshold: 10^ -7\n" - ] - } - ], - "source": [ - "# fit the model and Capture parameters\n", - "Weights, SupportVectors, Bias, KernelInstance = obj.ModelFit(X_train,y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "a46823c3-87ff-4b2a-b25a-227a1d5dfc0b", - "metadata": {}, - "source": [ - "## Inspect model's accuracy" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "a633cc21-c12e-41db-9282-98706168cd63", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "** Accuracy score: 0.9467592592592593\n", - "** Classification Report:\n", - " precision recall f1-score support\n", - "\n", - " -1 0.95 0.94 0.95 216\n", - " 1 0.94 0.95 0.95 216\n", - "\n", - " accuracy 0.95 432\n", - " macro avg 0.95 0.95 0.95 432\n", - "weighted avg 0.95 0.95 0.95 432\n", - "\n", - "** Confusion Matrix:\n", - " [[203 13]\n", - " [ 10 206]]\n" - ] - }, - { - "data": { - "text/plain": [ - "0.9467592592592593" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Model Prediction function\n", - "obj.ModelPredict(X_test,y_test,Bias,KernelInstance)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - 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It is part of ongoing scientific work at the Shahid Beheshti University and Institut for research in fundumental sciences, in collaboration with Prof. Jamal Amani Rad. -When using this package or part of the code for your own research, we ask you to cite us (using the Zenodo DOI below) and to always perform a parameter recovery, to make sure that everything is working as it should. Even though we tested all functions, we cannot assure that it will work perfectly on all platforms and data formats. Therefore, feedback on usage and issues is especially welcomed at this stage. -If you have any questions or would like to contribute, you can write us at amir.h.hadian@gmail.com. \ No newline at end of file diff --git a/docs/_build/html/_sources/index.rst.txt b/docs/_build/html/_sources/index.rst.txt deleted file mode 100644 index 32a54ec..0000000 --- a/docs/_build/html/_sources/index.rst.txt +++ /dev/null @@ -1,51 +0,0 @@ -.. ORSVM documentation master file, created by - sphinx-quickstart on Mon Sep 20 11:31:05 2021. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Welcome to ORSVM's documentation! -================================= - -`orsvm` is a free software package which provides a **SVM** classifier with some novel orthogonal polynomial kernels. This library provides a complete path of using the SVM classifier from **normalization** to calculation of **SVM equation** and the final **evaluation**. -Convext optimization is done using `cvxopt`_, which solves teh conext SVM's equation and returns the support vectors as result. - -.. _cvxopt: https://cvxopt.org/ - -.. image:: _static/identicons.png - -.. toctree:: - :maxdepth: 2 - :caption: Contents: - -.. toctree:: - :maxdepth: 2 - :caption: Getting started: - - installation - references - credits - -.. toctree:: - :maxdepth: 1 - :caption: How to: - - notebooks/How_to_use_orsvm.ipynb - -.. toctree:: - :maxdepth: 1 - :caption: Examples: - - notebooks/Chebyshev_fitting - notebooks/Legendre_fitting - notebooks/Gegenbauer_fitting - notebooks/Jacobi_fitting - - - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/_build/html/_sources/installation.rst.txt b/docs/_build/html/_sources/installation.rst.txt deleted file mode 100644 index 7fd1275..0000000 --- a/docs/_build/html/_sources/installation.rst.txt +++ /dev/null @@ -1,35 +0,0 @@ -ORSVM is a free software package which provides a SVM classifier with some novel orthogonal polynomial kernels. This library provides a complete chain of using the SVM classifier from normalization to calculation of SVM equation and the final evaluation. But please note that, there are some necessary steps before normalization, that should be handled for every data-set, such as duplicate checking, Null values or outlier handling, or even dimensionality reduction or whatever enhancements that may apply to a data-set. These steps are out of scope of SVM algorithm, thereupon, orsvm package. Instead, normalization step which is a must before sending data points into kernels, is handled directly in orsvm, by calling the relevant function. - - - -Installation -============ - -You can install the osvm package using:: -``pip install orsvm``, - -or get it directly from `GitHub`_ and install it locally using:: - -``pip install orsvm.zip``. - - -.. _Github: https://gitlab.com/mohammad.akhavan75/osvm - -Dependencies ------------- -Following dependencies will b eautomatically installed through `` pip install orsvm``. Still one can install it separately. - -- numpy -- cvxopt -- sk_learn -- pandas - - -Conda environment (suggested) ------------------------------ - -If you have Andaconda or miniconda installed and you would like to create a separate environment for the osvm package, do the following:: - - conda create --n ORSVM python=3.8 - conda activate ORSVM - pip install orsvm diff --git a/docs/_build/html/_sources/notebooks/Gegenbauer_fitting.ipynb.txt b/docs/_build/html/_sources/notebooks/Gegenbauer_fitting.ipynb.txt deleted file mode 100644 index 392f7b4..0000000 --- a/docs/_build/html/_sources/notebooks/Gegenbauer_fitting.ipynb.txt +++ /dev/null @@ -1,181 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "bb869c88-ef30-45d5-9a2e-9390567752b4", - "metadata": {}, - "source": [ - "# Fit data-set using Gegenbauer Kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "97d70d86-8249-492d-8526-54bbdde9bedd", - "metadata": {}, - "outputs": [], - "source": [ - "import orsvm\n", - "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "id": "6c3209e4-b26d-47e6-82f7-6f17a2d74da7", - "metadata": {}, - "source": [ - "## Load data-set" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "fdce3d48-c2a3-4d1b-8add-85d3134f86c9", - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_csv(r'D:\\Dev\\Orthogonal_Polynomials\\Datasets\\monks\\1_monks.train',\n", - " names=['label','col1','col2','col3',\n", - " 'col4','col5','col6'],index_col=False)\n", - "\n", - "df.loc[df.label==0, ['label']] = -1 # map \"0\" to \"-1\"\n", - "y_train=df['label'].to_numpy() # convert y_train to numpy array\n", - "df.drop('label', axis=1, inplace=True) # drop the class label\n", - "X_train=df.to_numpy() # convert x_train to numpy array\n", - "\n", - "\n", - "# load test-set\n", - "df = pd.read_csv(r'D:\\Dev\\Orthogonal_Polynomials\\Datasets\\monks\\1_monks.test',\n", - " names=['label','col1','col2','col3',\n", - " 'col4','col5','col6'],index_col=False)\n", - "df.loc[df.label==0, ['label']] = -1\n", - "y_test=df['label'].to_numpy()\n", - "df.drop('label', axis=1, inplace=True)\n", - "X_test=df.to_numpy()" - ] - }, - { - "cell_type": "markdown", - "id": "3f56b9ad-4638-45ae-b6b9-fa06397a6d01", - "metadata": {}, - "source": [ - "## Initiate kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "288fd499-86ea-4522-9fc3-4e523cb30ab9", - "metadata": {}, - "outputs": [], - "source": [ - "# Create an object from Model class of ORSVM\n", - "obj=orsvm.Model(kernel=\"Gegenbauer\",order=4,param1=0.5,T=0.3) \n" - ] - }, - { - "cell_type": "markdown", - "id": "bd6a9696-dcdd-4988-a896-23d975610396", - "metadata": {}, - "source": [ - "## Fit the model and Capture paramaters" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "b88fa0b9-bbe1-461e-9cfc-4089d3f15bbd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "********** 20/09/2021 15:02:03 **********\n", - "** OSVM kernel: Gegenbauer\n", - "** Order: 4\n", - "** Fractional mode, transition : 0.3\n", - "** Avegage support vector determiner selected!\n", - "** sv threshold: 10^ -7\n" - ] - } - ], - "source": [ - "# fit the model and Capture parameters\n", - "Weights, SupportVectors, Bias, KernelInstance = obj.ModelFit(X_train,y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "a46823c3-87ff-4b2a-b25a-227a1d5dfc0b", - "metadata": {}, - "source": [ - "## Inspect model's accuracy" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "a633cc21-c12e-41db-9282-98706168cd63", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "** Accuracy score: 0.9467592592592593\n", - "** Classification Report:\n", - " precision recall f1-score support\n", - "\n", - " -1 0.95 0.94 0.95 216\n", - " 1 0.94 0.95 0.95 216\n", - "\n", - " accuracy 0.95 432\n", - " macro avg 0.95 0.95 0.95 432\n", - "weighted avg 0.95 0.95 0.95 432\n", - "\n", - "** Confusion Matrix:\n", - " [[203 13]\n", - " [ 10 206]]\n" - ] - }, - { - "data": { - "text/plain": [ - "0.9467592592592593" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Model Prediction function\n", - "obj.ModelPredict(X_test,y_test,Bias,KernelInstance)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/_build/html/_static/css/badge_only.css b/docs/_build/html/_static/css/badge_only.css deleted file mode 100644 index e380325..0000000 --- a/docs/_build/html/_static/css/badge_only.css +++ /dev/null @@ -1 +0,0 @@ -.fa:before{-webkit-font-smoothing:antialiased}.clearfix{*zoom:1}.clearfix:after,.clearfix:before{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-style:normal;font-weight:400;src:url(fonts/fontawesome-webfont.eot?674f50d287a8c48dc19ba404d20fe713?#iefix) format("embedded-opentype"),url(fonts/fontawesome-webfont.woff2?af7ae505a9eed503f8b8e6982036873e) format("woff2"),url(fonts/fontawesome-webfont.woff?fee66e712a8a08eef5805a46892932ad) format("woff"),url(fonts/fontawesome-webfont.ttf?b06871f281fee6b241d60582ae9369b9) format("truetype"),url(fonts/fontawesome-webfont.svg?912ec66d7572ff821749319396470bde#FontAwesome) format("svg")}.fa:before{font-family:FontAwesome;font-style:normal;font-weight:400;line-height:1}.fa:before,a .fa{text-decoration:inherit}.fa:before,a .fa,li .fa{display:inline-block}li .fa-large:before{width:1.875em}ul.fas{list-style-type:none;margin-left:2em;text-indent:-.8em}ul.fas li .fa{width:.8em}ul.fas li .fa-large:before{vertical-align:baseline}.fa-book:before,.icon-book:before{content:"\f02d"}.fa-caret-down:before,.icon-caret-down:before{content:"\f0d7"}.fa-caret-up:before,.icon-caret-up:before{content:"\f0d8"}.fa-caret-left:before,.icon-caret-left:before{content:"\f0d9"}.fa-caret-right:before,.icon-caret-right:before{content:"\f0da"}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;z-index:400}.rst-versions a{color:#2980b9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27ae60}.rst-versions .rst-current-version:after{clear:both;content:"";display:block}.rst-versions .rst-current-version .fa{color:#fcfcfc}.rst-versions .rst-current-version .fa-book,.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#e74c3c;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#f1c40f;color:#000}.rst-versions.shift-up{height:auto;max-height:100%;overflow-y:scroll}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:grey;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:1px solid #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px;max-height:90%}.rst-versions.rst-badge .fa-book,.rst-versions.rst-badge .icon-book{float:none;line-height:30px}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book,.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge>.rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width:768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}} \ No newline at end of file diff --git a/docs/_build/html/_static/doctools.js b/docs/_build/html/_static/doctools.js deleted file mode 100644 index 8cbf1b1..0000000 --- a/docs/_build/html/_static/doctools.js +++ /dev/null @@ -1,323 +0,0 @@ -/* - 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// [C]VC[V] is m=1 - var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 - var s_v = "^(" + C + ")?" + v; // vowel in stem - - this.stemWord = function (w) { - var stem; - var suffix; - var firstch; - var origword = w; - - if (w.length < 3) - return w; - - var re; - var re2; - var re3; - var re4; - - firstch = w.substr(0,1); - if (firstch == "y") - w = firstch.toUpperCase() + w.substr(1); - - // Step 1a - re = /^(.+?)(ss|i)es$/; - re2 = /^(.+?)([^s])s$/; - - if (re.test(w)) - w = w.replace(re,"$1$2"); - else if (re2.test(w)) - w = w.replace(re2,"$1$2"); - - // Step 1b - re = /^(.+?)eed$/; - re2 = /^(.+?)(ed|ing)$/; - if (re.test(w)) { - var fp = re.exec(w); - re = new RegExp(mgr0); - if (re.test(fp[1])) { - re = /.$/; - w = w.replace(re,""); - } - } - else if (re2.test(w)) { - var fp = re2.exec(w); - stem = fp[1]; - re2 = new RegExp(s_v); - if (re2.test(stem)) { - w = stem; - re2 = /(at|bl|iz)$/; - re3 = new RegExp("([^aeiouylsz])\\1$"); - re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); - if (re2.test(w)) - w = w + "e"; - else if (re3.test(w)) { - re = /.$/; - w = w.replace(re,""); - } - else if (re4.test(w)) - w = w + "e"; - } - } - - // Step 1c - re = /^(.+?)y$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(s_v); - if (re.test(stem)) - w = stem + "i"; - } - - // Step 2 - re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - suffix = fp[2]; - re = new RegExp(mgr0); - if (re.test(stem)) - w = stem + step2list[suffix]; - } - - // Step 3 - re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - suffix = fp[2]; - re = new RegExp(mgr0); - if (re.test(stem)) - w = stem + step3list[suffix]; - } - - // Step 4 - re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; - re2 = /^(.+?)(s|t)(ion)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(mgr1); - if (re.test(stem)) - w = stem; - } - else if (re2.test(w)) { - var fp = re2.exec(w); - stem = fp[1] + fp[2]; - re2 = new RegExp(mgr1); - if (re2.test(stem)) - w = stem; - } - - // Step 5 - re = /^(.+?)e$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(mgr1); - re2 = new RegExp(meq1); - re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); - if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) - w = stem; - } - re = /ll$/; - re2 = new RegExp(mgr1); - if (re.test(w) && re2.test(w)) { - re = /.$/; - w = w.replace(re,""); - } - - // and turn initial Y back to y - if (firstch == "y") - w = firstch.toLowerCase() + w.substr(1); - return w; - } -} - - - - -var splitChars = (function() { - var result = {}; - var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648, - 1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702, - 2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971, - 2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345, - 3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761, - 3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823, - 4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125, - 8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695, - 11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587, - 43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141]; - var i, j, start, end; - for (i = 0; i < singles.length; i++) { - result[singles[i]] = true; - } - var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709], - [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161], - [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568], - [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807], - [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047], - [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383], - [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450], - [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547], - [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673], - [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820], - [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946], - [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023], - [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173], - [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332], - [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481], - [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718], - [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791], - [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095], - [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205], - [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687], - [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968], - [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869], - [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102], - [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271], - [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592], - [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822], - [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167], - [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959], - [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143], - [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318], - [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483], - [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101], - [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567], - [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292], - [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444], - [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783], - [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311], - [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511], - [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774], - [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071], - [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263], - [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519], - [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647], - [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967], - [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295], - [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274], - [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007], - [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381], - [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]]; - for (i = 0; i < ranges.length; i++) { - start = ranges[i][0]; - end = ranges[i][1]; - for (j = start; j <= end; j++) { - result[j] = true; - } - } - return result; -})(); - -function splitQuery(query) { - var result = []; - var start = -1; - for (var i = 0; i < query.length; i++) { - if (splitChars[query.charCodeAt(i)]) { - if (start !== -1) { - result.push(query.slice(start, i)); - start = -1; - } - } else if (start === -1) { - start = i; - } - } - if (start !== -1) { - result.push(query.slice(start)); - } - return result; -} - - diff --git a/docs/_build/html/_static/searchtools.js b/docs/_build/html/_static/searchtools.js deleted file mode 100644 index 58ff35c..0000000 --- a/docs/_build/html/_static/searchtools.js +++ /dev/null @@ -1,528 +0,0 @@ -/* - * searchtools.js - * ~~~~~~~~~~~~~~~~ - * - * Sphinx JavaScript utilities for the full-text search. - * - * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -if (!Scorer) { - /** - * Simple result scoring code. - */ - var Scorer = { - // Implement the following function to further tweak the score for each result - // The function takes a result array [filename, title, anchor, descr, score] - // and returns the new score. - /* - score: function(result) { - return result[4]; - }, - */ - - // query matches the full name of an object - objNameMatch: 11, - // or matches in the last dotted part of the object name - objPartialMatch: 6, - // Additive scores depending on the priority of the object - objPrio: {0: 15, // used to be importantResults - 1: 5, // used to be objectResults - 2: -5}, // used to be unimportantResults - // Used when the priority is not in the mapping. - objPrioDefault: 0, - - // query found in title - title: 15, - partialTitle: 7, - // query found in terms - term: 5, - partialTerm: 2 - }; -} - -if (!splitQuery) { - function splitQuery(query) { - return query.split(/\s+/); - } -} - -/** - * Search Module - */ -var Search = { - - _index : null, - _queued_query : null, - _pulse_status : -1, - - htmlToText : function(htmlString) { - var virtualDocument = document.implementation.createHTMLDocument('virtual'); - var htmlElement = $(htmlString, virtualDocument); - htmlElement.find('.headerlink').remove(); - docContent = htmlElement.find('[role=main]')[0]; - if(docContent === undefined) { - console.warn("Content block not found. Sphinx search tries to obtain it " + - "via '[role=main]'. Could you check your theme or template."); - return ""; - } - return docContent.textContent || docContent.innerText; - }, - - init : function() { - var params = $.getQueryParameters(); - if (params.q) { - var query = params.q[0]; - $('input[name="q"]')[0].value = query; - this.performSearch(query); - } - }, - - loadIndex : function(url) { - $.ajax({type: "GET", url: url, data: null, - dataType: "script", cache: true, - complete: function(jqxhr, textstatus) { - if (textstatus != "success") { - document.getElementById("searchindexloader").src = url; - } - }}); - }, - - setIndex : function(index) { - var q; - this._index = index; - if ((q = this._queued_query) !== null) { - this._queued_query = null; - Search.query(q); - } - }, - - hasIndex : function() { - return this._index !== null; - }, - - deferQuery : function(query) { - this._queued_query = query; - }, - - stopPulse : function() { - this._pulse_status = 0; - }, - - startPulse : function() { - if (this._pulse_status >= 0) - return; - function pulse() { - var i; - Search._pulse_status = (Search._pulse_status + 1) % 4; - var dotString = ''; - for (i = 0; i < Search._pulse_status; i++) - dotString += '.'; - Search.dots.text(dotString); - if (Search._pulse_status > -1) - window.setTimeout(pulse, 500); - } - pulse(); - }, - - /** - * perform a search for something (or wait until index is loaded) - */ - performSearch : function(query) { - // create the required interface elements - this.out = $('#search-results'); - this.title = $('

' + _('Searching') + '

').appendTo(this.out); - this.dots = $('').appendTo(this.title); - this.status = $('

 

').appendTo(this.out); - this.output = $('