added program to demonstrate use of logistic regression classifier
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samples/cpp/sample_logistic_regression.cpp
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samples/cpp/sample_logistic_regression.cpp
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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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// 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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// 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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// AUTHOR:
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// Rahul Kavi rahulkavi[at]live[at]com
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//
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#include <iostream>
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#include <opencv2/core/core.hpp>
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#include <opencv2/ml/ml.hpp>
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using namespace std;
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using namespace cv;
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int main()
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{
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Mat data_temp, labels_temp;
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Mat data, labels;
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Mat responses, result;
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FileStorage f;
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cout<<"*****************************************************************************************"<<endl;
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cout<<"\"data01.xml\" contains digits 0 and 1 of 20 samples each, collected on an Android device"<<endl;
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cout<<"Each of the collected images are of size 28 x 28 re-arranged to 1 x 784 matrix"<<endl;
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cout<<"*****************************************************************************************\n\n"<<endl;
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cout<<"loading the dataset\n"<<endl;
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f.open("data01.xml", FileStorage::READ);
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f["datamat"] >> data_temp;
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f["labelsmat"] >> labels_temp;
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data_temp.convertTo(data, CV_32F);
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labels_temp.convertTo(labels, CV_32F);
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cout<<"initializing Logisitc Regression Parameters\n"<<endl;
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CvLR_TrainParams params = CvLR_TrainParams();
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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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CvLR lr_(data, labels, params);
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cout<<"predicting the trained dataset\n"<<endl;
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lr_.predict(data, responses);
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labels.convertTo(labels, CV_32S);
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cout<<"Original Label :: Predicted Label"<<endl;
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result = (labels == responses)/255;
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for(int i=0;i<labels.rows;i++)
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{
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cout<<labels.at<int>(i,0)<<" :: "<< responses.at<int>(i,0)<<endl;
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}
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// calculate accuracy
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cout<<"accuracy: "<<((double)cv::sum(result)[0]/result.rows)*100<<"%\n";
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// save the classfier
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lr_.save("NewLR_Trained.xml");
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// load the classifier onto new object
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CvLR lr2;
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cout<<"loading a new classifier"<<endl;
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lr2.load("NewLR_Trained.xml");
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Mat responses2;
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// predict using loaded classifier
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cout<<"predicting the dataset using the loaded classfier\n"<<endl;
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lr2.predict(data, responses2);
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// calculate accuracy
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result = (labels == responses2)/255;
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cout<<"accuracy using loaded classifier: "<<((double)cv::sum(result)[0]/result.rows)*100<<"%\n";
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return 0;
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}
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