opencv/samples/cpp/logistic_regression.cpp

/*//////////////////////////////////////////////////////////////////////////////////////
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//  By downloading, copying, installing or using the software you agree to this license.
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// 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.
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// #
// # 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

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#include <iostream>

#include <opencv2/core/core.hpp>
#include <opencv2/ml/ml.hpp>
#include <opencv2/highgui/highgui.hpp>

using namespace std;
using namespace cv;
using namespace cv::ml;

int main()
{
    const String filename = "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" << endl;
        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
//    Mat bigImage;
//    for(int i = 0; i < data_train.rows; ++i)
//    {
//        bigImage.push_back(data_train.row(i).reshape(0, 28));
//    }
//    imshow("digits", bigImage.t());

    Mat responses, result;

//    LogisticRegression::Params params = LogisticRegression::Params(
//        0.001, 10, LogisticRegression::BATCH, LogisticRegression::REG_L2, 1, 1);
    // params1 (above) with batch gradient performs better than mini batch
    // gradient below with same parameters
    LogisticRegression::Params params = LogisticRegression::Params(
        0.001, 10, LogisticRegression::MINI_BATCH, LogisticRegression::REG_L2, 1, 1);

    // however mini batch gradient descent parameters with slower learning
    // rate(below) can be used to get higher accuracy than with parameters
    // mentioned above
//    LogisticRegression::Params params = LogisticRegression::Params(
//        0.000001, 10, LogisticRegression::MINI_BATCH, LogisticRegression::REG_L2, 1, 1);

    cout << "training...";
    Ptr<StatModel> lr1 = LogisticRegression::create(params);
    lr1->train(data_train, ROW_SAMPLE, labels_train);
    cout << "done!" << endl;

    cout << "predicting...";
    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;
    result = (labels_test == responses) / 255;
    cout << "accuracy: " << ((double)cv::sum(result)[0] / result.rows) * 100 << "%\n";

    // save the classfier
    cout << "saving the classifier" << endl;
    const String saveFilename = "NewLR_Trained.xml";
    lr1->save(saveFilename);

    // load the classifier onto new object
    cout << "loading a new classifier" << endl;
    Ptr<LogisticRegression> lr2 = StatModel::load<LogisticRegression>(saveFilename);

    // predict using loaded classifier
    cout << "predicting the dataset using the loaded classfier" << endl;
    Mat responses2;
    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;
}