/*//////////////////////////////////////////////////////////////////////////////////////
// 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
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// Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
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// are permitted provided that the following conditions are met:
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#include <iostream>
#include <opencv2/core.hpp>
#include <opencv2/ml.hpp>
#include <opencv2/highgui.hpp>
using namespace std;
using namespace cv;
using namespace cv::ml;
static void showImage(const Mat &data, int columns, const String &name)
{
Mat bigImage;
for(int i = 0; i < data.rows; ++i)
{
bigImage.push_back(data.row(i).reshape(0, columns));
}
imshow(name, bigImage.t());
}
static float calculateAccuracyPercent(const Mat &original, const Mat &predicted)
{
return 100 * (float)countNonZero(original == predicted) / predicted.rows;
}
int main()
{
const String filename = "../data/data01.xml";
cout << "**********************************************************************" << endl;
cout << filename
<< " 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 << "**********************************************************************" << endl;
Mat data, labels;
{
cout << "loading the dataset...";
FileStorage f;
if(f.open(filename, FileStorage::READ))
{
f["datamat"] >> data;
f["labelsmat"] >> labels;
f.release();
}
else
{
cerr << "file can not be opened: " << filename << endl;
return 1;
}
data.convertTo(data, CV_32F);
labels.convertTo(labels, CV_32F);
cout << "read " << data.rows << " rows of data" << endl;
}
Mat data_train, data_test;
Mat labels_train, labels_test;
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/testing samples count: " << data_train.rows << "/" << data_test.rows << endl;
// display sample image
showImage(data_train, 28, "train data");
showImage(data_test, 28, "test data");
// simple case with batch gradient
cout << "training...";
//! [init]
Ptr<LogisticRegression> lr1 = LogisticRegression::create();
lr1->setLearningRate(0.001);
lr1->setIterations(10);
lr1->setRegularization(LogisticRegression::REG_L2);
lr1->setTrainMethod(LogisticRegression::BATCH);
lr1->setMiniBatchSize(1);
//! [init]
lr1->train(data_train, ROW_SAMPLE, labels_train);
cout << "done!" << endl;
cout << "predicting...";
Mat responses;
lr1->predict(data_test, responses);
cout << "done!" << endl;
// show prediction report
cout << "original vs predicted:" << endl;
labels_test.convertTo(labels_test, CV_32S);
cout << labels_test.t() << endl;
cout << responses.t() << endl;
cout << "accuracy: " << calculateAccuracyPercent(labels_test, responses) << "%" << endl;
// save the classfier
const String saveFilename = "NewLR_Trained.xml";
cout << "saving the classifier to " << saveFilename << endl;
lr1->save(saveFilename);
// load the classifier onto new object
cout << "loading a new classifier from " << saveFilename << endl;
Ptr<LogisticRegression> lr2 = StatModel::load<LogisticRegression>(saveFilename);
// predict using loaded classifier
cout << "predicting the dataset using the loaded classfier...";
Mat responses2;
lr2->predict(data_test, responses2);
cout << "done!" << endl;
// calculate accuracy
cout << labels_test.t() << endl;
cout << responses2.t() << endl;
cout << "accuracy: " << calculateAccuracyPercent(labels_test, responses2) << "%" << endl;
waitKey(0);
return 0;
}