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Reworked ML logistic regression implementation, initial version

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This commit is contained in:
Maksim Shabunin
2014-08-14 19:01:45 +04:00
parent 71770eb790
commit 3e26086f82
4 changed files with 214 additions and 312 deletions
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149
samples/cpp/logistic_regression.cpp
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@@ -1,4 +1,4 @@
///////////////////////////////////////////////////////////////////////////////////////
/*//////////////////////////////////////////////////////////////////////////////////////
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
// By downloading, copying, installing or using the software you agree to this license.
@@ -11,7 +11,8 @@
// 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")
// 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.
@@ -24,7 +25,6 @@
// # Logistic Regression ALGORITHM
// License Agreement
// For Open Source Computer Vision Library
@@ -54,7 +54,7 @@
// 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.
// the use of this software, even if advised of the possibility of such damage.*/
#include <iostream>
@@ -62,42 +62,45 @@
#include <opencv2/ml/ml.hpp>
#include <opencv2/highgui/highgui.hpp>
using namespace std;
using namespace cv;
using namespace cv::ml;
int main()
{
Mat data_temp, labels_temp;
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;
Mat responses, result;
FileStorage fs1, fs2;
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++)
for(int i = 0; i < data.rows; i++)
{
if(i%2 ==0)
if(i % 2 == 0)
{
data_train.push_back(data.row(i));
labels_train.push_back(labels.row(i));
@@ -108,66 +111,66 @@ int main()
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;
cout << "training/testing samples count: " << data_train.rows << "/" << data_test.rows << 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();
// 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());
imshow("digit 0", img_disp1);
imshow("digit 1", img_disp2);
Mat responses, result;
cout<<"initializing Logisitc Regression Parameters\n"<<endl;
// 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);
// LogisticRegressionParams params1 = LogisticRegressionParams(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
LogisticRegressionParams params1 = LogisticRegressionParams(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);
// however mini batch gradient descent parameters with slower learning rate(below) can be used to get higher accuracy than with parameters mentioned above
// LogisticRegressionParams params1 = LogisticRegressionParams(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<<"training Logisitc Regression classifier\n"<<endl;
cout << "predicting...";
lr1->predict(data_test, responses);
cout << "done!" << endl;
LogisticRegression lr1(data_train, labels_train, params1);
lr1.predict(data_test, responses);
// show prediction report
cout << "original vs predicted:" << endl;
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;
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
fs1.open("NewLR_Trained.xml",FileStorage::WRITE);
lr1.write(fs1);
fs1.release();
cout << "saving the classifier" << endl;
const String saveFilename = "NewLR_Trained.xml";
lr1->save(saveFilename);
// load the classifier onto new object
LogisticRegressionParams params2 = LogisticRegressionParams();
LogisticRegression lr2(params2);
cout<<"loading a new classifier"<<endl;
fs2.open("NewLR_Trained.xml",FileStorage::READ);
FileNode fn2 = fs2.root();
lr2.read(fn2);
fs2.release();
Mat responses2;
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\n"<<endl;
lr2.predict(data_test, responses2);
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);
cout << "accuracy using loaded classifier: "
<< 100 * (float)cv::countNonZero(labels_test == responses2) / responses2.rows << "%"
<< endl;
waitKey(0);
return 0;
}