///////////////////////////////////////////////////////////////////////////////////////
-// 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
+// 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>
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;
+ LogisticRegressionParams params = LogisticRegressionParams(0.001, 10, LogisticRegression::REG_L2, 1, LogisticRegression::BATCH, 1);
cout<<"training Logisitc Regression classifier\n"<<endl;
- CvLR lr_(data_train, labels_train, params);
+ LogisticRegression lr_(data_train, labels_train, params);
lr_.predict(data_test, responses);
labels_test.convertTo(labels_test, CV_32S);
lr_.save("NewLR_Trained.xml");
// load the classifier onto new object
- CvLR lr2;
+ LogisticRegression lr2;
cout<<"loading a new classifier"<<endl;
lr2.load("NewLR_Trained.xml");
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";
+ cout<<"accuracy using loaded classifier: "<<100 * (float)cv::countNonZero(labels_test == responses2)/responses2.rows<<"%"<<endl;
waitKey(0);
return 0;