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Tang1705
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Lib/opencv/sources/samples/cpp/logistic_regression.cpp
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Lib/opencv/sources/samples/cpp/logistic_regression.cpp
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/*//////////////////////////////////////////////////////////////////////////////////////
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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 implementation of the Logistic Regression algorithm in C++ in OpenCV.
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// AUTHOR:
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// Rahul Kavi rahulkavi[at]live[at]com
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//
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// contains a subset of data from the popular Iris Dataset (taken from
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// "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 <opencv2/core.hpp>
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#include <opencv2/ml.hpp>
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#include <opencv2/highgui.hpp>
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using namespace std;
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using namespace cv;
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using namespace cv::ml;
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static void showImage(const Mat &data, int columns, const String &name)
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{
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Mat bigImage;
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for(int i = 0; i < data.rows; ++i)
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{
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bigImage.push_back(data.row(i).reshape(0, columns));
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}
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imshow(name, bigImage.t());
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}
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static float calculateAccuracyPercent(const Mat &original, const Mat &predicted)
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{
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return 100 * (float)countNonZero(original == predicted) / predicted.rows;
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}
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int main()
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{
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const String filename = samples::findFile("data01.xml");
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cout << "**********************************************************************" << endl;
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cout << filename
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<< " 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"
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<< endl;
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cout << "**********************************************************************" << endl;
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Mat data, labels;
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{
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cout << "loading the dataset...";
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FileStorage f;
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if(f.open(filename, FileStorage::READ))
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{
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f["datamat"] >> data;
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f["labelsmat"] >> labels;
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f.release();
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}
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else
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{
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cerr << "file can not be opened: " << filename << endl;
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return 1;
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}
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data.convertTo(data, CV_32F);
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labels.convertTo(labels, CV_32F);
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cout << "read " << data.rows << " rows of data" << endl;
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}
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Mat data_train, data_test;
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Mat labels_train, labels_test;
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for(int i = 0; i < data.rows; i++)
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{
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if(i % 2 == 0)
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{
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data_train.push_back(data.row(i));
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labels_train.push_back(labels.row(i));
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}
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else
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{
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data_test.push_back(data.row(i));
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labels_test.push_back(labels.row(i));
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}
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}
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cout << "training/testing samples count: " << data_train.rows << "/" << data_test.rows << endl;
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// display sample image
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showImage(data_train, 28, "train data");
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showImage(data_test, 28, "test data");
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// simple case with batch gradient
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cout << "training...";
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//! [init]
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Ptr<LogisticRegression> lr1 = LogisticRegression::create();
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lr1->setLearningRate(0.001);
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lr1->setIterations(10);
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lr1->setRegularization(LogisticRegression::REG_L2);
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lr1->setTrainMethod(LogisticRegression::BATCH);
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lr1->setMiniBatchSize(1);
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//! [init]
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lr1->train(data_train, ROW_SAMPLE, labels_train);
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cout << "done!" << endl;
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cout << "predicting...";
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Mat responses;
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lr1->predict(data_test, responses);
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cout << "done!" << endl;
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// show prediction report
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cout << "original vs predicted:" << endl;
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labels_test.convertTo(labels_test, CV_32S);
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cout << labels_test.t() << endl;
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cout << responses.t() << endl;
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cout << "accuracy: " << calculateAccuracyPercent(labels_test, responses) << "%" << endl;
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// save the classfier
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const String saveFilename = "NewLR_Trained.xml";
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cout << "saving the classifier to " << saveFilename << endl;
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lr1->save(saveFilename);
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// load the classifier onto new object
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cout << "loading a new classifier from " << saveFilename << endl;
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Ptr<LogisticRegression> lr2 = StatModel::load<LogisticRegression>(saveFilename);
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// predict using loaded classifier
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cout << "predicting the dataset using the loaded classfier...";
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Mat responses2;
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lr2->predict(data_test, responses2);
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cout << "done!" << endl;
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// calculate accuracy
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cout << labels_test.t() << endl;
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cout << responses2.t() << endl;
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cout << "accuracy: " << calculateAccuracyPercent(labels_test, responses2) << "%" << endl;
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waitKey(0);
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return 0;
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}
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