Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

added test for logistic regression

Browse Source
pull/3119/head
Rahul Kavi 11 years ago committed by Maksim Shabunin
parent 3bf6c3c2aa
commit 3039ed7682
1 changed files with 345 additions and 0 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 345
      modules/ml/test/test_lr.cpp

345
modules/ml/test/test_lr.cpp
Unescape View File

@ -0,0 +1,345 @@
///////////////////////////////////////////////////////////////////////////////////////
// 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 "test_precomp.hpp"
using namespace std;
using namespace cv;
static bool calculateError( const Mat& _p_labels, const Mat& _o_labels, float& error)
{
error = 0.0f;
float accuracy = 0.0f;
Mat _p_labels_temp;
Mat _o_labels_temp;
_p_labels.convertTo(_p_labels_temp, CV_32S);
_o_labels.convertTo(_o_labels_temp, CV_32S);
CV_Assert(_p_labels_temp.total() == _o_labels_temp.total());
CV_Assert(_p_labels_temp.rows == _o_labels_temp.rows);
Mat result = (_p_labels_temp == _o_labels_temp)/255;
accuracy = (float)cv::sum(result)[0]/result.rows;
error = 1 - accuracy;
return true;
}
//--------------------------------------------------------------------------------------------
class CV_LRTest : public cvtest::BaseTest
{
public:
CV_LRTest() {}
protected:
virtual void run( int start_from );
};
void CV_LRTest::run( int /*start_from*/ )
{
// initialize varibles from the popular Iris Dataset
Mat data = (Mat_<double>(150, 4)<<
5.1,3.5,1.4,0.2, 4.9,3.0,1.4,0.2, 4.7,3.2,1.3,0.2, 4.6,3.1,1.5,0.2,
5.0,3.6,1.4,0.2, 5.4,3.9,1.7,0.4, 4.6,3.4,1.4,0.3, 5.0,3.4,1.5,0.2, 4.4,2.9,1.4,0.2, 4.9,3.1,1.5,0.1,
5.4,3.7,1.5,0.2, 4.8,3.4,1.6,0.2, 4.8,3.0,1.4,0.1, 4.3,3.0,1.1,0.1, 5.8,4.0,1.2,0.2, 5.7,4.4,1.5,0.4,
5.4,3.9,1.3,0.4, 5.1,3.5,1.4,0.3, 5.7,3.8,1.7,0.3, 5.1,3.8,1.5,0.3, 5.4,3.4,1.7,0.2, 5.1,3.7,1.5,0.4,
4.6,3.6,1.0,0.2, 5.1,3.3,1.7,0.5, 4.8,3.4,1.9,0.2, 5.0,3.0,1.6,0.2, 5.0,3.4,1.6,0.4,
5.2,3.5,1.5,0.2, 5.2,3.4,1.4,0.2, 4.7,3.2,1.6,0.2, 4.8,3.1,1.6,0.2, 5.4,3.4,1.5,0.4,
5.2,4.1,1.5,0.1, 5.5,4.2,1.4,0.2, 4.9,3.1,1.5,0.1, 5.0,3.2,1.2,0.2, 5.5,3.5,1.3,0.2,
4.9,3.1,1.5,0.1, 4.4,3.0,1.3,0.2, 5.1,3.4,1.5,0.2, 5.0,3.5,1.3,0.3, 4.5,2.3,1.3,0.3,
4.4,3.2,1.3,0.2, 5.0,3.5,1.6,0.6, 5.1,3.8,1.9,0.4, 4.8,3.0,1.4,0.3, 5.1,3.8,1.6,0.2,
4.6,3.2,1.4,0.2, 5.3,3.7,1.5,0.2, 5.0,3.3,1.4,0.2, 7.0,3.2,4.7,1.4, 6.4,3.2,4.5,1.5,
6.9,3.1,4.9,1.5, 5.5,2.3,4.0,1.3, 6.5,2.8,4.6,1.5, 5.7,2.8,4.5,1.3, 6.3,3.3,4.7,1.6,
4.9,2.4,3.3,1.0, 6.6,2.9,4.6,1.3, 5.2,2.7,3.9,1.4, 5.0,2.0,3.5,1.0, 5.9,3.0,4.2,1.5,
6.0,2.2,4.0,1.0, 6.1,2.9,4.7,1.4, 5.6,2.9,3.6,1.3, 6.7,3.1,4.4,1.4, 5.6,3.0,4.5,1.5,
5.8,2.7,4.1,1.0, 6.2,2.2,4.5,1.5, 5.6,2.5,3.9,1.1, 5.9,3.2,4.8,1.8, 6.1,2.8,4.0,1.3,
6.3,2.5,4.9,1.5, 6.1,2.8,4.7,1.2, 6.4,2.9,4.3,1.3, 6.6,3.0,4.4,1.4, 6.8,2.8,4.8,1.4,
6.7,3.0,5.0,1.7, 6.0,2.9,4.5,1.5, 5.7,2.6,3.5,1.0, 5.5,2.4,3.8,1.1, 5.5,2.4,3.7,1.0,
5.8,2.7,3.9,1.2, 6.0,2.7,5.1,1.6, 5.4,3.0,4.5,1.5, 6.0,3.4,4.5,1.6, 6.7,3.1,4.7,1.5,
6.3,2.3,4.4,1.3, 5.6,3.0,4.1,1.3, 5.5,2.5,4.0,1.3, 5.5,2.6,4.4,1.2, 6.1,3.0,4.6,1.4,
5.8,2.6,4.0,1.2, 5.0,2.3,3.3,1.0, 5.6,2.7,4.2,1.3, 5.7,3.0,4.2,1.2, 5.7,2.9,4.2,1.3,
6.2,2.9,4.3,1.3, 5.1,2.5,3.0,1.1, 5.7,2.8,4.1,1.3, 6.3,3.3,6.0,2.5, 5.8,2.7,5.1,1.9,
7.1,3.0,5.9,2.1, 6.3,2.9,5.6,1.8, 6.5,3.0,5.8,2.2, 7.6,3.0,6.6,2.1, 4.9,2.5,4.5,1.7,
7.3,2.9,6.3,1.8, 6.7,2.5,5.8,1.8, 7.2,3.6,6.1,2.5, 6.5,3.2,5.1,2.0, 6.4,2.7,5.3,1.9,
6.8,3.0,5.5,2.1, 5.7,2.5,5.0,2.0, 5.8,2.8,5.1,2.4, 6.4,3.2,5.3,2.3, 6.5,3.0,5.5,1.8,
7.7,3.8,6.7,2.2, 7.7,2.6,6.9,2.3, 6.0,2.2,5.0,1.5, 6.9,3.2,5.7,2.3, 5.6,2.8,4.9,2.0,
7.7,2.8,6.7,2.0, 6.3,2.7,4.9,1.8, 6.7,3.3,5.7,2.1, 7.2,3.2,6.0,1.8, 6.2,2.8,4.8,1.8,
6.1,3.0,4.9,1.8, 6.4,2.8,5.6,2.1, 7.2,3.0,5.8,1.6, 7.4,2.8,6.1,1.9, 7.9,3.8,6.4,2.0,
6.4,2.8,5.6,2.2, 6.3,2.8,5.1,1.5, 6.1,2.6,5.6,1.4, 7.7,3.0,6.1,2.3, 6.3,3.4,5.6,2.4,
6.4,3.1,5.5,1.8, 6.0,3.0,4.8,1.8, 6.9,3.1,5.4,2.1, 6.7,3.1,5.6,2.4, 6.9,3.1,5.1,2.3,
5.8,2.7,5.1,1.9, 6.8,3.2,5.9,2.3, 6.7,3.3,5.7,2.5, 6.7,3.0,5.2,2.3, 6.3,2.5,5.0,1.9,
6.5,3.0,5.2,2.0, 6.2,3.4,5.4,2.3, 5.9,3.0,5.1,1.8);
Mat labels = (Mat_<int>(150, 1)<< 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3);
CvLR_TrainParams params = CvLR_TrainParams();
Mat responses1, responses2;
float error = 0.0f;
CvLR_TrainParams params1 = CvLR_TrainParams();
CvLR_TrainParams params2 = CvLR_TrainParams();
params1.alpha = 1.0;
params1.num_iters = 10001;
params1.norm = CvLR::REG_L2;
// params1.debug = 1;
params1.regularized = 1;
params1.train_method = CvLR::BATCH;
params1.minibatchsize = 10;
// run LR classifier train classifier
data.convertTo(data, CV_32FC1);
labels.convertTo(labels, CV_32FC1);
CvLR lr1(data, labels, params1);
// predict using the same data
lr1.predict(data, responses1);
int test_code = cvtest::TS::OK;
// calculate error
if(!calculateError(responses1, labels, error))
{
ts->printf(cvtest::TS::LOG, "Bad prediction labels\n" );
test_code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if(error > 0.05f)
{
ts->printf(cvtest::TS::LOG, "Bad accuracy of (%f)\n", error);
test_code = cvtest::TS::FAIL_BAD_ACCURACY;
}
params2.alpha = 1.0;
params2.num_iters = 9000;
params2.norm = CvLR::REG_L2;
// params2.debug = 1;
params2.regularized = 1;
params2.train_method = CvLR::MINI_BATCH;
params2.minibatchsize = 10;
// now train using mini batch gradient descent
CvLR lr2(data, labels, params2);
lr2.predict(data, responses2);
responses2.convertTo(responses2, CV_32S);
//calculate error
if(!calculateError(responses2, labels, error))
{
ts->printf(cvtest::TS::LOG, "Bad prediction labels\n" );
test_code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if(error > 0.06f)
{
ts->printf(cvtest::TS::LOG, "Bad accuracy of (%f)\n", error);
test_code = cvtest::TS::FAIL_BAD_ACCURACY;
}
ts->set_failed_test_info(test_code);
}
//--------------------------------------------------------------------------------------------
class CV_LRTest_SaveLoad : public cvtest::BaseTest
{
public:
CV_LRTest_SaveLoad(){}
protected:
virtual void run(int start_from);
};
void CV_LRTest_SaveLoad::run( int /*start_from*/ )
{
int code = cvtest::TS::OK;
// initialize varibles from the popular Iris Dataset
Mat data = (Mat_<double>(150, 4)<<
5.1,3.5,1.4,0.2, 4.9,3.0,1.4,0.2, 4.7,3.2,1.3,0.2, 4.6,3.1,1.5,0.2,
5.0,3.6,1.4,0.2, 5.4,3.9,1.7,0.4, 4.6,3.4,1.4,0.3, 5.0,3.4,1.5,0.2, 4.4,2.9,1.4,0.2, 4.9,3.1,1.5,0.1,
5.4,3.7,1.5,0.2, 4.8,3.4,1.6,0.2, 4.8,3.0,1.4,0.1, 4.3,3.0,1.1,0.1, 5.8,4.0,1.2,0.2, 5.7,4.4,1.5,0.4,
5.4,3.9,1.3,0.4, 5.1,3.5,1.4,0.3, 5.7,3.8,1.7,0.3, 5.1,3.8,1.5,0.3, 5.4,3.4,1.7,0.2, 5.1,3.7,1.5,0.4,
4.6,3.6,1.0,0.2, 5.1,3.3,1.7,0.5, 4.8,3.4,1.9,0.2, 5.0,3.0,1.6,0.2, 5.0,3.4,1.6,0.4,
5.2,3.5,1.5,0.2, 5.2,3.4,1.4,0.2, 4.7,3.2,1.6,0.2, 4.8,3.1,1.6,0.2, 5.4,3.4,1.5,0.4,
5.2,4.1,1.5,0.1, 5.5,4.2,1.4,0.2, 4.9,3.1,1.5,0.1, 5.0,3.2,1.2,0.2, 5.5,3.5,1.3,0.2,
4.9,3.1,1.5,0.1, 4.4,3.0,1.3,0.2, 5.1,3.4,1.5,0.2, 5.0,3.5,1.3,0.3, 4.5,2.3,1.3,0.3,
4.4,3.2,1.3,0.2, 5.0,3.5,1.6,0.6, 5.1,3.8,1.9,0.4, 4.8,3.0,1.4,0.3, 5.1,3.8,1.6,0.2,
4.6,3.2,1.4,0.2, 5.3,3.7,1.5,0.2, 5.0,3.3,1.4,0.2, 7.0,3.2,4.7,1.4, 6.4,3.2,4.5,1.5,
6.9,3.1,4.9,1.5, 5.5,2.3,4.0,1.3, 6.5,2.8,4.6,1.5, 5.7,2.8,4.5,1.3, 6.3,3.3,4.7,1.6,
4.9,2.4,3.3,1.0, 6.6,2.9,4.6,1.3, 5.2,2.7,3.9,1.4, 5.0,2.0,3.5,1.0, 5.9,3.0,4.2,1.5,
6.0,2.2,4.0,1.0, 6.1,2.9,4.7,1.4, 5.6,2.9,3.6,1.3, 6.7,3.1,4.4,1.4, 5.6,3.0,4.5,1.5,
5.8,2.7,4.1,1.0, 6.2,2.2,4.5,1.5, 5.6,2.5,3.9,1.1, 5.9,3.2,4.8,1.8, 6.1,2.8,4.0,1.3,
6.3,2.5,4.9,1.5, 6.1,2.8,4.7,1.2, 6.4,2.9,4.3,1.3, 6.6,3.0,4.4,1.4, 6.8,2.8,4.8,1.4,
6.7,3.0,5.0,1.7, 6.0,2.9,4.5,1.5, 5.7,2.6,3.5,1.0, 5.5,2.4,3.8,1.1, 5.5,2.4,3.7,1.0,
5.8,2.7,3.9,1.2, 6.0,2.7,5.1,1.6, 5.4,3.0,4.5,1.5, 6.0,3.4,4.5,1.6, 6.7,3.1,4.7,1.5,
6.3,2.3,4.4,1.3, 5.6,3.0,4.1,1.3, 5.5,2.5,4.0,1.3, 5.5,2.6,4.4,1.2, 6.1,3.0,4.6,1.4,
5.8,2.6,4.0,1.2, 5.0,2.3,3.3,1.0, 5.6,2.7,4.2,1.3, 5.7,3.0,4.2,1.2, 5.7,2.9,4.2,1.3,
6.2,2.9,4.3,1.3, 5.1,2.5,3.0,1.1, 5.7,2.8,4.1,1.3, 6.3,3.3,6.0,2.5, 5.8,2.7,5.1,1.9,
7.1,3.0,5.9,2.1, 6.3,2.9,5.6,1.8, 6.5,3.0,5.8,2.2, 7.6,3.0,6.6,2.1, 4.9,2.5,4.5,1.7,
7.3,2.9,6.3,1.8, 6.7,2.5,5.8,1.8, 7.2,3.6,6.1,2.5, 6.5,3.2,5.1,2.0, 6.4,2.7,5.3,1.9,
6.8,3.0,5.5,2.1, 5.7,2.5,5.0,2.0, 5.8,2.8,5.1,2.4, 6.4,3.2,5.3,2.3, 6.5,3.0,5.5,1.8,
7.7,3.8,6.7,2.2, 7.7,2.6,6.9,2.3, 6.0,2.2,5.0,1.5, 6.9,3.2,5.7,2.3, 5.6,2.8,4.9,2.0,
7.7,2.8,6.7,2.0, 6.3,2.7,4.9,1.8, 6.7,3.3,5.7,2.1, 7.2,3.2,6.0,1.8, 6.2,2.8,4.8,1.8,
6.1,3.0,4.9,1.8, 6.4,2.8,5.6,2.1, 7.2,3.0,5.8,1.6, 7.4,2.8,6.1,1.9, 7.9,3.8,6.4,2.0,
6.4,2.8,5.6,2.2, 6.3,2.8,5.1,1.5, 6.1,2.6,5.6,1.4, 7.7,3.0,6.1,2.3, 6.3,3.4,5.6,2.4,
6.4,3.1,5.5,1.8, 6.0,3.0,4.8,1.8, 6.9,3.1,5.4,2.1, 6.7,3.1,5.6,2.4, 6.9,3.1,5.1,2.3,
5.8,2.7,5.1,1.9, 6.8,3.2,5.9,2.3, 6.7,3.3,5.7,2.5, 6.7,3.0,5.2,2.3, 6.3,2.5,5.0,1.9,
6.5,3.0,5.2,2.0, 6.2,3.4,5.4,2.3, 5.9,3.0,5.1,1.8);
Mat labels = (Mat_<int>(150, 1)<< 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3);
CvLR_TrainParams params = CvLR_TrainParams();
Mat responses1, responses2;
Mat learnt_mat1, learnt_mat2;
Mat pred_result1, comp_learnt_mats;
float errorCount = 0.0;
CvLR_TrainParams params1 = CvLR_TrainParams();
CvLR_TrainParams params2 = CvLR_TrainParams();
params1.alpha = 1.0;
params1.num_iters = 10001;
params1.norm = CvLR::REG_L2;
// params1.debug = 1;
params1.regularized = 1;
params1.train_method = CvLR::BATCH;
params1.minibatchsize = 10;
data.convertTo(data, CV_32FC1);
labels.convertTo(labels, CV_32FC1);
// run LR classifier train classifier
CvLR lr1(data, labels, params1);
CvLR lr2;
learnt_mat1 = lr1.get_learnt_mat();
lr1.predict(data, responses1);
// now save the classifier
// Write out
string filename = cv::tempfile(".xml");
try
{
lr1.save(filename.c_str());
}
catch(...)
{
ts->printf(cvtest::TS::LOG, "Crash in write method.\n" );
ts->set_failed_test_info(cvtest::TS::FAIL_EXCEPTION);
}
try
{
lr2.load(filename.c_str());
}
catch(...)
{
ts->printf(cvtest::TS::LOG, "Crash in read method.\n");
ts->set_failed_test_info(cvtest::TS::FAIL_EXCEPTION);
}
lr2.predict(data, responses2);
learnt_mat2 = lr2.get_learnt_mat();
// compare difference in prediction outputs before and after loading from disk
pred_result1 = (responses1 == responses2)/255;
// compare difference in learnt matrices before and after loading from disk
comp_learnt_mats = (learnt_mat1 == learnt_mat2);
comp_learnt_mats = comp_learnt_mats.reshape(1, comp_learnt_mats.rows*comp_learnt_mats.cols);
comp_learnt_mats.convertTo(comp_learnt_mats, CV_32S);
comp_learnt_mats = comp_learnt_mats/255;
// compare difference in prediction outputs and stored inputs
// check if there is any difference between computed learnt mat and retreived mat
errorCount += 1 - (float)cv::sum(pred_result1)[0]/pred_result1.rows;
errorCount += 1 - (float)cv::sum(comp_learnt_mats)[0]/comp_learnt_mats.rows;
if(errorCount>0)
{
ts->printf( cvtest::TS::LOG, "Different prediction results before writing and after reading (errorCount=%d).\n", errorCount );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
remove( filename.c_str() );
ts->set_failed_test_info( code );
}
TEST(ML_LR, accuracy) { CV_LRTest test; test.safe_run(); }
TEST(ML_LR, save_load) { CV_LRTest_SaveLoad test; test.safe_run(); }
Write Preview
Loading…
Cancel
Save