updated logistic regression sample program
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committed by
Maksim Shabunin

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///////////////////////////////////////////////////////////////////////////////////////
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///////////////////////////////////////////////////////////////////////////////////////
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// sample_logistic_regression.cpp
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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// By downloading, copying, installing or using the software you agree to this license.
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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// copy or use the software.
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// This is a sample program demostrating classification of digits 0 and 1 using Logistic Regression
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// This is a implementation of the Logistic Regression algorithm in C++ in OpenCV.
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// AUTHOR:
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// AUTHOR:
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// Rahul Kavi rahulkavi[at]live[at]com
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// Rahul Kavi rahulkavi[at]live[at]com
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//
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//
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// contains a subset of data from the popular Iris Dataset (taken from "http://archive.ics.uci.edu/ml/datasets/Iris")
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// # You are free to use, change, or redistribute the code in any way you wish for
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// # non-commercial purposes, but please maintain the name of the original author.
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// # This code comes with no warranty of any kind.
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// #
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// # You are free to use, change, or redistribute the code in any way you wish for
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// # non-commercial purposes, but please maintain the name of the original author.
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// # This code comes with no warranty of any kind.
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// # Logistic Regression ALGORITHM
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// License Agreement
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// For Open Source Computer Vision Library
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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#include <iostream>
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#include <iostream>
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#include <opencv2/core/core.hpp>
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#include <opencv2/core/core.hpp>
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@@ -76,17 +120,11 @@ int main()
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cout<<"initializing Logisitc Regression Parameters\n"<<endl;
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cout<<"initializing Logisitc Regression Parameters\n"<<endl;
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CvLR_TrainParams params = CvLR_TrainParams();
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LogisticRegressionParams params = LogisticRegressionParams(0.001, 10, LogisticRegression::REG_L2, 1, LogisticRegression::BATCH, 1);
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params.alpha = 0.001;
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params.num_iters = 10;
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params.norm = CvLR::REG_L2;
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params.regularized = 1;
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params.train_method = CvLR::BATCH;
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cout<<"training Logisitc Regression classifier\n"<<endl;
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cout<<"training Logisitc Regression classifier\n"<<endl;
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CvLR lr_(data_train, labels_train, params);
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LogisticRegression lr_(data_train, labels_train, params);
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lr_.predict(data_test, responses);
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lr_.predict(data_test, responses);
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labels_test.convertTo(labels_test, CV_32S);
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labels_test.convertTo(labels_test, CV_32S);
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@@ -106,7 +144,7 @@ int main()
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lr_.save("NewLR_Trained.xml");
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lr_.save("NewLR_Trained.xml");
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// load the classifier onto new object
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// load the classifier onto new object
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CvLR lr2;
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LogisticRegression lr2;
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cout<<"loading a new classifier"<<endl;
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cout<<"loading a new classifier"<<endl;
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lr2.load("NewLR_Trained.xml");
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lr2.load("NewLR_Trained.xml");
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@@ -119,8 +157,7 @@ int main()
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lr2.predict(data_test, responses2);
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lr2.predict(data_test, responses2);
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// calculate accuracy
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// calculate accuracy
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result = (labels_test == responses2)/255;
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cout<<"accuracy using loaded classifier: "<<100 * (float)cv::countNonZero(labels_test == responses2)/responses2.rows<<"%"<<endl;
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cout<<"accuracy using loaded classifier: "<<((double)cv::sum(result)[0]/result.rows)*100<<"%\n";
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waitKey(0);
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waitKey(0);
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return 0;
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return 0;
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