updated logistic regression sample program

diff --git a/samples/cpp/logistic_regression.cpp b/samples/cpp/logistic_regression.cpp
new file mode 100644 (file)
index 0000000..71b71af
--- /dev/null
+++ b/samples/cpp/logistic_regression.cpp
@@ -0,0 +1,164 @@
+///////////////////////////////////////////////////////////////////////////////////////
+// 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 implementation of the Logistic Regression algorithm in C++ in OpenCV.
+
+// AUTHOR:
+// 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")
+
+// # 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.
+// # This code comes with no warranty of any kind.
+
+// #
+// # 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.
+// # This code comes with no warranty of any kind.
+
+// # Logistic Regression ALGORITHM
+
+
+//                           License Agreement
+//                For Open Source Computer Vision Library
+
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+
+//   * Redistributions of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+
+//   * Redistributions in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// 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.
+
+#include <iostream>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/ml/ml.hpp>
+#include <opencv2/highgui/highgui.hpp>
+
+
+using namespace std;
+using namespace cv;
+
+
+int main()
+{
+    Mat data_temp, labels_temp;
+    Mat data, labels;
+
+    Mat data_train, data_test;
+    Mat labels_train, labels_test;
+
+    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);
+
+    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;
+
+    LogisticRegressionParams params = LogisticRegressionParams(0.001, 10, LogisticRegression::REG_L2, 1, LogisticRegression::BATCH, 1);
+
+    cout<<"training Logisitc Regression classifier\n"<<endl;
+
+    LogisticRegression lr_(data_train, labels_train, params);
+    lr_.predict(data_test, responses);
+    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;
+
+    // save the classfier
+    lr_.save("NewLR_Trained.xml");
+
+    // load the classifier onto new object
+    LogisticRegression 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_test, responses2);
+
+    // calculate accuracy
+    cout<<"accuracy using loaded classifier: "<<100 * (float)cv::countNonZero(labels_test == responses2)/responses2.rows<<"%"<<endl;
+    waitKey(0);
+
+    return 0;
+}

diff --git a/samples/cpp/sample_logistic_regression.cpp b/samples/cpp/sample_logistic_regression.cpp
deleted file mode 100644 (file)
index 36fd882..0000000
--- a/samples/cpp/sample_logistic_regression.cpp
+++ /dev/null
@@ -1,127 +0,0 @@
-///////////////////////////////////////////////////////////////////////////////////////
-// 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>
-#include <opencv2/highgui/highgui.hpp>
-
-
-using namespace std;
-using namespace cv;
-
-
-int main()
-{
-    Mat data_temp, labels_temp;
-    Mat data, labels;
-
-    Mat data_train, data_test;
-    Mat labels_train, labels_test;
-
-    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);
-
-    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;
-
-    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_train, labels_train, params);
-    lr_.predict(data_test, responses);
-    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;
-
-    // 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_test, responses2);
-
-    // calculate accuracy
-    result = (labels_test == responses2)/255;
-    cout<<"accuracy using loaded classifier: "<<((double)cv::sum(result)[0]/result.rows)*100<<"%\n";
-    waitKey(0);
-
-    return 0;
-}