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updated logistic regression sample program

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Rahul Kavi
2013-10-04 08:30:10 -04:00
committed by Maksim Shabunin
parent 6c74439d56
commit b8ea21b2e5
1 changed files with 50 additions and 13 deletions
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samples/cpp/logistic_regression.cpp Normal file
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///////////////////////////////////////////////////////////////////////////////////////
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
// This is a implementation of the Logistic Regression algorithm in C++ in OpenCV.
// AUTHOR:
// Rahul Kavi rahulkavi[at]live[at]com
//
// contains a subset of data from the popular Iris Dataset (taken from "http://archive.ics.uci.edu/ml/datasets/Iris")
// # You are free to use, change, or redistribute the code in any way you wish for
// # non-commercial purposes, but please maintain the name of the original author.
// # This code comes with no warranty of any kind.
// #
// # You are free to use, change, or redistribute the code in any way you wish for
// # non-commercial purposes, but please maintain the name of the original author.
// # This code comes with no warranty of any kind.
// # Logistic Regression ALGORITHM
// License Agreement
// For Open Source Computer Vision Library
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * 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.
// * The name of the copyright holders may not 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 Intel Corporation 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.
#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/ml/ml.hpp>
#include <opencv2/highgui/highgui.hpp>
using namespace std;
using namespace cv;
int main()
{
Mat data_temp, labels_temp;
Mat data, labels;
Mat data_train, data_test;
Mat labels_train, labels_test;
Mat responses, result;
FileStorage f;
cout<<"*****************************************************************************************"<<endl;
cout<<"\"data01.xml\" contains digits 0 and 1 of 20 samples each, collected on an Android device"<<endl;
cout<<"Each of the collected images are of size 28 x 28 re-arranged to 1 x 784 matrix"<<endl;
cout<<"*****************************************************************************************\n\n"<<endl;
cout<<"loading the dataset\n"<<endl;
f.open("data01.xml", FileStorage::READ);
f["datamat"] >> data_temp;
f["labelsmat"] >> labels_temp;
data_temp.convertTo(data, CV_32F);
labels_temp.convertTo(labels, CV_32F);
for(int i =0;i<data.rows;i++)
{
if(i%2 ==0)
{
data_train.push_back(data.row(i));
labels_train.push_back(labels.row(i));
}
else
{
data_test.push_back(data.row(i));
labels_test.push_back(labels.row(i));
}
}
cout<<"training samples per class: "<<data_train.rows/2<<endl;
cout<<"testing samples per class: "<<data_test.rows/2<<endl;
// display sample image
Mat img_disp1 = data_train.row(2).reshape(0,28).t();
Mat img_disp2 = data_train.row(18).reshape(0,28).t();
imshow("digit 0", img_disp1);
imshow("digit 1", img_disp2);
cout<<"initializing Logisitc Regression Parameters\n"<<endl;
LogisticRegressionParams params = LogisticRegressionParams(0.001, 10, LogisticRegression::REG_L2, 1, LogisticRegression::BATCH, 1);
cout<<"training Logisitc Regression classifier\n"<<endl;
LogisticRegression lr_(data_train, labels_train, params);
lr_.predict(data_test, responses);
labels_test.convertTo(labels_test, CV_32S);
cout<<"Original Label :: Predicted Label"<<endl;
result = (labels_test == responses)/255;
for(int i=0;i<labels_test.rows;i++)
{
cout<<labels_test.at<int>(i,0)<<" :: "<< responses.at<int>(i,0)<<endl;
}
// calculate accuracy
cout<<"accuracy: "<<((double)cv::sum(result)[0]/result.rows)*100<<"%\n";
cout<<"saving the classifier"<<endl;
// save the classfier
lr_.save("NewLR_Trained.xml");
// load the classifier onto new object
LogisticRegression lr2;
cout<<"loading a new classifier"<<endl;
lr2.load("NewLR_Trained.xml");
Mat responses2;
// predict using loaded classifier
cout<<"predicting the dataset using the loaded classfier\n"<<endl;
lr2.predict(data_test, responses2);
// calculate accuracy
cout<<"accuracy using loaded classifier: "<<100 * (float)cv::countNonZero(labels_test == responses2)/responses2.rows<<"%"<<endl;
waitKey(0);
return 0;
}