--- /dev/null
+///////////////////////////////////////////////////////////////////////////////////////
+// sample_logistic_regression.cpp
+// 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 sample program demostrating classification of digits 0 and 1 using Logistic Regression
+
+// AUTHOR:
+// Rahul Kavi rahulkavi[at]live[at]com
+//
+
+#include <iostream>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/ml/ml.hpp>
+
+using namespace std;
+using namespace cv;
+
+
+int main()
+{
+ Mat data_temp, labels_temp;
+ Mat data, labels;
+ Mat responses, result;
+
+ 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);
+
+ cout<<"initializing Logisitc Regression Parameters\n"<<endl;
+
+ CvLR_TrainParams params = CvLR_TrainParams();
+
+ params.alpha = 0.001;
+ params.num_iters = 10;
+ params.norm = CvLR::REG_L2;
+ params.regularized = 1;
+ params.train_method = CvLR::BATCH;
+
+ cout<<"training Logisitc Regression classifier\n"<<endl;
+
+ CvLR lr_(data, labels, params);
+
+ cout<<"predicting the trained dataset\n"<<endl;
+
+ lr_.predict(data, responses);
+
+ labels.convertTo(labels, CV_32S);
+
+ cout<<"Original Label :: Predicted Label"<<endl;
+ result = (labels == responses)/255;
+ for(int i=0;i<labels.rows;i++)
+ {
+ cout<<labels.at<int>(i,0)<<" :: "<< responses.at<int>(i,0)<<endl;
+ }
+ // calculate accuracy
+ cout<<"accuracy: "<<((double)cv::sum(result)[0]/result.rows)*100<<"%\n";
+
+ // save the classfier
+ lr_.save("NewLR_Trained.xml");
+
+ // load the classifier onto new object
+ CvLR lr2;
+ cout<<"loading a new classifier"<<endl;
+
+ lr2.load("NewLR_Trained.xml");
+
+ Mat responses2;
+
+ // predict using loaded classifier
+ cout<<"predicting the dataset using the loaded classfier\n"<<endl;
+
+ lr2.predict(data, responses2);
+
+ // calculate accuracy
+ result = (labels == responses2)/255;
+ cout<<"accuracy using loaded classifier: "<<((double)cv::sum(result)[0]/result.rows)*100<<"%\n";
+
+ return 0;
+}