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346 lines
16 KiB
C++
346 lines
16 KiB
C++
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///////////////////////////////////////////////////////////////////////////////////////
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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// This is a implementation of the Logistic Regression algorithm in C++ in OpenCV.
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// AUTHOR:
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// Rahul Kavi rahulkavi[at]live[at]com
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//
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// contains a subset of data from the popular Iris Dataset (taken from "http://archive.ics.uci.edu/ml/datasets/Iris")
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// # You are free to use, change, or redistribute the code in any way you wish for
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// # non-commercial purposes, but please maintain the name of the original author.
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// # This code comes with no warranty of any kind.
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// #
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// # You are free to use, change, or redistribute the code in any way you wish for
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// # non-commercial purposes, but please maintain the name of the original author.
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// # This code comes with no warranty of any kind.
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// # Logistic Regression ALGORITHM
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// License Agreement
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// For Open Source Computer Vision Library
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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#include "test_precomp.hpp"
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using namespace std;
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using namespace cv;
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static bool calculateError( const Mat& _p_labels, const Mat& _o_labels, float& error)
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{
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error = 0.0f;
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float accuracy = 0.0f;
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Mat _p_labels_temp;
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Mat _o_labels_temp;
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_p_labels.convertTo(_p_labels_temp, CV_32S);
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_o_labels.convertTo(_o_labels_temp, CV_32S);
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CV_Assert(_p_labels_temp.total() == _o_labels_temp.total());
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CV_Assert(_p_labels_temp.rows == _o_labels_temp.rows);
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Mat result = (_p_labels_temp == _o_labels_temp)/255;
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accuracy = (float)cv::sum(result)[0]/result.rows;
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error = 1 - accuracy;
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return true;
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}
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//--------------------------------------------------------------------------------------------
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class CV_LRTest : public cvtest::BaseTest
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{
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public:
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CV_LRTest() {}
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protected:
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virtual void run( int start_from );
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};
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void CV_LRTest::run( int /*start_from*/ )
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{
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// initialize varibles from the popular Iris Dataset
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Mat data = (Mat_<double>(150, 4)<<
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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6.5,3.0,5.2,2.0, 6.2,3.4,5.4,2.3, 5.9,3.0,5.1,1.8);
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Mat labels = (Mat_<int>(150, 1)<< 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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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,
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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,
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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,
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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,
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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,
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3, 3, 3, 3, 3);
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CvLR_TrainParams params = CvLR_TrainParams();
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Mat responses1, responses2;
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float error = 0.0f;
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CvLR_TrainParams params1 = CvLR_TrainParams();
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CvLR_TrainParams params2 = CvLR_TrainParams();
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params1.alpha = 1.0;
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params1.num_iters = 10001;
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params1.norm = CvLR::REG_L2;
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// params1.debug = 1;
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params1.regularized = 1;
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params1.train_method = CvLR::BATCH;
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params1.minibatchsize = 10;
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// run LR classifier train classifier
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data.convertTo(data, CV_32FC1);
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labels.convertTo(labels, CV_32FC1);
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CvLR lr1(data, labels, params1);
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// predict using the same data
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lr1.predict(data, responses1);
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int test_code = cvtest::TS::OK;
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// calculate error
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if(!calculateError(responses1, labels, error))
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{
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ts->printf(cvtest::TS::LOG, "Bad prediction labels\n" );
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test_code = cvtest::TS::FAIL_INVALID_OUTPUT;
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}
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else if(error > 0.05f)
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{
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ts->printf(cvtest::TS::LOG, "Bad accuracy of (%f)\n", error);
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test_code = cvtest::TS::FAIL_BAD_ACCURACY;
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}
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params2.alpha = 1.0;
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params2.num_iters = 9000;
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params2.norm = CvLR::REG_L2;
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// params2.debug = 1;
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params2.regularized = 1;
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params2.train_method = CvLR::MINI_BATCH;
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params2.minibatchsize = 10;
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// now train using mini batch gradient descent
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CvLR lr2(data, labels, params2);
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lr2.predict(data, responses2);
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responses2.convertTo(responses2, CV_32S);
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//calculate error
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if(!calculateError(responses2, labels, error))
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{
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ts->printf(cvtest::TS::LOG, "Bad prediction labels\n" );
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test_code = cvtest::TS::FAIL_INVALID_OUTPUT;
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}
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else if(error > 0.06f)
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{
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ts->printf(cvtest::TS::LOG, "Bad accuracy of (%f)\n", error);
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test_code = cvtest::TS::FAIL_BAD_ACCURACY;
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}
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ts->set_failed_test_info(test_code);
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}
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//--------------------------------------------------------------------------------------------
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class CV_LRTest_SaveLoad : public cvtest::BaseTest
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{
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public:
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CV_LRTest_SaveLoad(){}
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protected:
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virtual void run(int start_from);
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};
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void CV_LRTest_SaveLoad::run( int /*start_from*/ )
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{
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int code = cvtest::TS::OK;
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// initialize varibles from the popular Iris Dataset
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Mat data = (Mat_<double>(150, 4)<<
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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(); }
|