Metadata-Version: 2.0
Name: FukuML
Version: 0.2.7
Summary: Simple machine learning library
Home-page: https://github.com/fukuball/fuku-ml
Author: Fukuball Lin
Author-email: fukuball@gmail.com
License: The MIT License (MIT)
Requires-Dist: numpy (==1.10.4)
Requires-Dist: scipy (==0.17.0)
Requires-Dist: scikit-learn (==0.17.1)
Requires-Dist: cvxopt (==1.1.8)

Copyright (c) 2016 fukuball

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

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.


Description: FukuML
        =========
        
        .. image:: https://travis-ci.org/fukuball/fuku-ml.svg?branch=master
            :target: https://travis-ci.org/fukuball/fuku-ml
        
        .. image:: https://codecov.io/github/fukuball/fuku-ml/coverage.svg?branch=master
            :target: https://codecov.io/github/fukuball/fuku-ml?branch=master
        
        .. image:: https://badge.fury.io/py/FukuML.svg
            :target: https://badge.fury.io/py/FukuML
        
        .. image:: https://api.codacy.com/project/badge/grade/afc87eff27ab47d6b960ea7b3088c469
            :target: https://www.codacy.com/app/fukuball/fuku-ml
        
        .. image:: https://img.shields.io/badge/made%20with-%e2%9d%a4-ff69b4.svg
            :target: http://www.fukuball.com
        
        Simple machine learning library / 簡單易用的機器學習套件
        
        Installation
        ============
        
        .. code-block:: bash
        
            $ pip install FukuML
        
        Tutorial
        ============
        
        - Lesson 1: `Perceptron Binary Classification Learning Algorithm`_
        
        - Appendix 1: `Play With Your Own Dataset`_
        
        .. _Perceptron Binary Classification Learning Algorithm: https://github.com/fukuball/FukuML-Tutorial/blob/master/Perceptron%20Binary%20Classification%20Learning%20Algorithm%20Tutorial.ipynb
        
        .. _Play With Your Own Dataset: https://github.com/fukuball/FukuML-Tutorial/blob/master/Play%20With%20Your%20Own%20Dataset%20Tutorial.ipynb
        
        Algorithm
        ============
        
        - Perceptron Binary Classification Learning Algorithm
        
        - Perceptron Multi Classification Learning Algorithm
        
        - Pocket Perceptron Binary Classification Learning Algorithm
        
        - Pocket Perceptron Multi Classification Learning Algorithm
        
        - Linear Regression Learning Algorithm
        
        - Linear Regression Binary Classification Learning Algorithm
        
        - Linear Regression Multi Classification Learning Algorithm
        
        - Ridge Regression Learning Algorithm
        
        - Ridge Regression Binary Classification Learning Algorithm
        
        - Ridge Regression Multi Classification Learning Algorithm
        
        - Kernel Ridge Regression Learning Algorithm
        
        - Kernel Ridge Regression Binary Classification Learning Algorithm
        
        - Logistic Regression Learning Algorithm
        
        - Logistic Regression Binary Classification Learning Algorithm
        
        - Logistic Regression One vs All Multi Classification Learning Algorithm
        
        - Logistic Regression One vs One Multi Classification Learning Algorithm
        
        - L2 Regularized Logistic Regression Learning Algorithm
        
        - L2 Regularized Logistic Regression Binary Classification Learning Algorithm
        
        - Kernel Logistic Regression Learning Algorithm
        
        - Primal Hard Margin Support Vector Machine Binary Classification Learning Algorithm
        
        - Dual Hard Margin Support Vector Machine Binary Classification Learning Algorithm
        
        - Polynomial Kernel Support Vector Machine Binary Classification Learning Algorithm
        
        - Gaussian Kernel Support Vector Machine Binary Classification Learning Algorithm
        
        - Soft Polynomial Kernel Support Vector Machine Binary Classification Learning Algorithm
        
        - Soft Gaussian Kernel Support Vector Machine Binary Classification Learning Algorithm
        
        - Polynomial Kernel Support Vector Machine Multi Classification Learning Algorithm
        
        - Gaussian Kernel Support Vector Machine Multi Classification Learning Algorithm
        
        - Soft Polynomial Kernel Support Vector Machine Multi Classification Learning Algorithm
        
        - Soft Gaussian Kernel Support Vector Machine Multi Classification Learning Algorithm
        
        - Probabilistic Support Vector Machine Learning Algorithm
        
        - Least Squares Support Vector Machine Learning Algorithm
        
        - Decision Stump Binary Classification Learning Algorithm
        
        - Decision Tree Classification Learning Algorithm
        
        - Decision Tree Regression Learning Algorithm
        
        - Linear Regression Accelerator
        
        - Polynomial Feature Transform
        
        - Legendre Feature Transform
        
        - 10 Fold Cross Validation
        
        Usage
        ============
        
        .. code-block:: py
        
            >>> import numpy as np
            # we need numpy as a base libray
        
            >>> import FukuML.PLA as pla
            # import FukuML.PLA to do Perceptron Learning
        
            >>> your_input_data_file = '/path/to/your/data/file'
            # assign your input data file, please check the data format: https://github.com/fukuball/fuku-ml/blob/master/FukuML/dataset/pla_binary_train.dat
        
            >>> pla_bc = pla.BinaryClassifier()
            # new a PLA binary classifier
        
            >>> pla_bc.load_train_data(your_input_data_file)
            # load train data
        
            >>> pla_bc.set_param()
            # set parameter
        
            >>> pla_bc.init_W()
            # init the W
        
            >>> W = pla_bc.train()
            # train by Perceptron Learning Algorithm to find best W
        
            >>> test_data = 'Each feature of data x separated with spaces. And the ground truth y put in the end of line separated by a space'
            # assign test data, format like this '0.97681 0.10723 0.64385 ........ 0.29556 1'
        
            >>> prediction = pla_bc.prediction(test_data)
            # prediction by trained W
        
            >>> print prediction['input_data_x']
            # print test data x
        
            >>> print prediction['input_data_y']
            # print test data y
        
            >>> print prediction['prediction']
            # print the prediction, will find out prediction is the same as pla_bc.test_data_y
        
        For detail, please check https://github.com/fukuball/fuku-ml/blob/master/doc/sample_code.rst
        
        License
        =========
        The MIT License (MIT)
        
        Copyright (c) 2016 fukuball
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        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.
Keywords: Machine Learning,Perceptron Learning Algorithm,PLA,Pocket Perceptron Learning Algorithm,Pocket PLA,Linear Regression,Logistic Regression,Ridge Regression,Binay Classifier,Multi Classifier
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Web Environment
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Machine Learning :: Perceptron Learning Algorithm
Classifier: Machine Learning :: Pocket Perceptron Learning Algorithm
Classifier: Machine Learning :: Linear Regression Learning Algorithm
Classifier: Machine Learning :: Logistic Regression Learning Algorithm
Classifier: Machine Learning :: Ridge Regression Learning Algorithm
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Topic :: Education
Classifier: Topic :: Software Development :: Machine Learning
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Machine Learning
Classifier: Topic :: Utilities
