{
Mat data_temp, labels_temp;
Mat data, labels;
-
+
Mat data_train, data_test;
Mat labels_train, labels_test;
cout<<"training samples per class: "<<data_train.rows/2<<endl;
cout<<"testing samples per class: "<<data_test.rows/2<<endl;
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+
// 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);
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-
cout<<"initializing Logisitc Regression Parameters\n"<<endl;
CvLR_TrainParams params = CvLR_TrainParams();
CvLR lr_(data_train, labels_train, params);
lr_.predict(data_test, responses);
labels_test.convertTo(labels_test, CV_32S);
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+
cout<<"Original Label :: Predicted Label"<<endl;
result = (labels_test == responses)/255;
{
cout<<labels_test.at<int>(i,0)<<" :: "<< responses.at<int>(i,0)<<endl;
}
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+
// calculate accuracy
cout<<"accuracy: "<<((double)cv::sum(result)[0]/result.rows)*100<<"%\n";
cout<<"saving the classifier"<<endl;