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updated logistic regression sample program

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This commit is contained in:
Rahul Kavi
2013-08-07 20:39:46 -04:00
committed by Maksim Shabunin
parent 6ae43a2243
commit 3a6466d2e1
1 changed files with 45 additions and 13 deletions
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58
samples/cpp/sample_logistic_regression.cpp
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@@ -16,6 +16,8 @@
#include <opencv2/core/core.hpp> #include <opencv2/core/core.hpp>
#include <opencv2/ml/ml.hpp> #include <opencv2/ml/ml.hpp>
#include <opencv2/highgui/highgui.hpp>
using namespace std; using namespace std;
using namespace cv; using namespace cv;
@@ -25,6 +27,10 @@ int main()
{ {
Mat data_temp, labels_temp; Mat data_temp, labels_temp;
Mat data, labels; Mat data, labels;
Mat data_train, data_test;
Mat labels_train, labels_test;
Mat responses, result; Mat responses, result;
FileStorage f; FileStorage f;
@@ -44,6 +50,32 @@ int main()
data_temp.convertTo(data, CV_32F); data_temp.convertTo(data, CV_32F);
labels_temp.convertTo(labels, CV_32F); labels_temp.convertTo(labels, CV_32F);
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 samples per class: "<<data_train.rows/2<<endl;
cout<<"testing samples per class: "<<data_test.rows/2<<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();
imshow("digit 0", img_disp1);
imshow("digit 1", img_disp2);
cout<<"initializing Logisitc Regression Parameters\n"<<endl; cout<<"initializing Logisitc Regression Parameters\n"<<endl;
CvLR_TrainParams params = CvLR_TrainParams(); CvLR_TrainParams params = CvLR_TrainParams();
@@ -56,22 +88,21 @@ int main()
cout<<"training Logisitc Regression classifier\n"<<endl; cout<<"training Logisitc Regression classifier\n"<<endl;
CvLR lr_(data, labels, params); CvLR lr_(data_train, labels_train, params);
lr_.predict(data_test, responses);
cout<<"predicting the trained dataset\n"<<endl; labels_test.convertTo(labels_test, CV_32S);
lr_.predict(data, responses);
labels.convertTo(labels, CV_32S);
cout<<"Original Label :: Predicted Label"<<endl; cout<<"Original Label :: Predicted Label"<<endl;
result = (labels == responses)/255; result = (labels_test == responses)/255;
for(int i=0;i<labels.rows;i++)
for(int i=0;i<labels_test.rows;i++)
{ {
cout<<labels.at<int>(i,0)<<" :: "<< responses.at<int>(i,0)<<endl; cout<<labels_test.at<int>(i,0)<<" :: "<< responses.at<int>(i,0)<<endl;
} }
// calculate accuracy // calculate accuracy
cout<<"accuracy: "<<((double)cv::sum(result)[0]/result.rows)*100<<"%\n"; cout<<"accuracy: "<<((double)cv::sum(result)[0]/result.rows)*100<<"%\n";
cout<<"saving the classifier"<<endl;
// save the classfier // save the classfier
lr_.save("NewLR_Trained.xml"); lr_.save("NewLR_Trained.xml");
@@ -87,11 +118,12 @@ int main()
// predict using loaded classifier // predict using loaded classifier
cout<<"predicting the dataset using the loaded classfier\n"<<endl; cout<<"predicting the dataset using the loaded classfier\n"<<endl;
lr2.predict(data, responses2); lr2.predict(data_test, responses2);
// calculate accuracy // calculate accuracy
result = (labels == responses2)/255; result = (labels_test == responses2)/255;
cout<<"accuracy using loaded classifier: "<<((double)cv::sum(result)[0]/result.rows)*100<<"%\n"; cout<<"accuracy using loaded classifier: "<<((double)cv::sum(result)[0]/result.rows)*100<<"%\n";
waitKey(0);
return 0; return 0;
} }