Metadata-Version: 2.1
Name: libsvm
Version: 3.23.0.4
Summary: Pre-built LibSVM packages for Python.
Home-page: https://github.com/ocampor/libsvm
Author: Ricardo Ocampo
Author-email: me@ocampor.ai
License: Apache 2.0
Description: .. -*- mode: rst -*-
        
        |Travis|_ |PyPi|_
        
        .. |Travis| image:: https://travis-ci.com/ocampor/libsvm.svg?branch=master
        .. _Travis: https://travis-ci.com/ocampor/libsvm
        
        .. |PyPi| image:: https://img.shields.io/pypi/dm/libsvm?color=blue   :alt: PyPI - Downloads
        .. _PyPi: https://pypi.org/project/libsvm/
        
        LibSVM
        ======
        
        Description
        -----------
        
        Pre-built LibSVM packages for Python.
        
        What is LibSVM?
        ---------------
        
        Crated by Chih-Chung Chang and Chih-Jen Lin, LIBSVM is an integrated
        software for support vector classification, (C-SVC, nu-SVC), regression
        (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It
        supports multi-class classification.
        
        Original Links
        --------------
        
        -  Repository: https://github.com/cjlin1/libsvm
        -  Library website: https://www.csie.ntu.edu.tw/~cjlin/libsvm
        
        Purpose of this package
        -----------------------
        
        The idea behind this package is to use the same code as in
        https://github.com/cjlin1/libsvm using the very convenient pip command
        
        How to install
        --------------
        
        ::
        
           pip install libsvm
        
        Example
        -------
        
        1. Download https://github.com/cjlin1/libsvm/blob/master/heart_scale
           file.
        2. Run the following commands
        
        ::
        
           >>> from libsvm.svmutil import *
           >>> y, x = svm_read_problem('path/to/heart_scale')
           >>> m = svm_train(y[:200], x[:200], '-c 4')
           *.*
           optimization finished, #iter = 257
           nu = 0.351161
           obj = -225.628984, rho = 0.636110
           nSV = 91, nBSV = 49
           Total nSV = 91
           >>> p_label, p_acc, p_val = svm_predict(y[200:], x[200:], m)
           Accuracy = 84.2857% (59/70) (classification)
        
        Windows
        -------
        
        The package contains a pre-built Windows binary that is only compatible with 64 bits architecture; therefore,
        32 bits architecture is not compatible.
        
        Cygwin
        ______
        
        In case that you want to install this package using Cygwin, you have to make sure that the
        following packages are installed:
        
        1. gcc-g++ >= 7.0.0
        2. python38
        3. python38-devel
        4. python38-pip
        
        Some good tutorials to install Cygwin packages are the following:
        
        - https://wiki.usask.ca/display/MESH/Running+Python+from+the+Cygwin+Terminal
        - https://www.davidbaumgold.com/tutorials/set-up-python-windows/#installing-cygwin
        
        Copyright
        ---------
        
        Copyright (c) 2000-2018 Chih-Chung Chang and Chih-Jen Lin All rights
        reserved.
        
        Redistribution and use in source and binary forms, with or without
        modification, are permitted provided that the following conditions are
        met:
        
        1. Redistributions of source code must retain the above copyright
           notice, this list of conditions and the following disclaimer.
        
        2. Redistributions in binary form must reproduce the above copyright
           notice, this list of conditions and the following disclaimer in the
           documentation and/or other materials provided with the distribution.
        
        3. Neither name of copyright holders nor the names of its contributors
           may be used to endorse or promote products derived from this software
           without specific prior written permission.
        
        THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
        "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
        LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
        PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR
        CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
        EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
        PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
        PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
        LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
        NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
        SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
        
        Maintainer
        ----------
        
        -  Ricardo Ocampo `me@ocampor.ai`_
        
        .. _me@ocampor.ai: me@ocampor.ai
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.5
Provides-Extra: dev
