diff --git a/samples/cpp/sample_logistic_regression.cpp b/samples/cpp/sample_logistic_regression.cpp
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+///////////////////////////////////////////////////////////////////////////////////////
+// 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;
+}