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319 lines
9.2 KiB
C++
319 lines
9.2 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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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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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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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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//
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// * Redistribution's 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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//
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// * Redistribution's 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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//
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// * The name of Intel Corporation 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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//
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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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//
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//M*/
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#include "test_precomp.hpp"
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#include "opencv2/highgui.hpp"
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using namespace cv;
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using namespace cv::ml;
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using cv::ml::SVMSGD;
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using cv::ml::TrainData;
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class CV_SVMSGDTrainTest : public cvtest::BaseTest
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{
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public:
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enum TrainDataType
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{
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UNIFORM_SAME_SCALE,
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UNIFORM_DIFFERENT_SCALES
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};
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CV_SVMSGDTrainTest(const Mat &_weights, float shift, TrainDataType type, double precision = 0.01);
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private:
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virtual void run( int start_from );
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static float decisionFunction(const Mat &sample, const Mat &weights, float shift);
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void makeData(int samplesCount, const Mat &weights, float shift, RNG &rng, Mat &samples, Mat & responses);
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void generateSameBorders(int featureCount);
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void generateDifferentBorders(int featureCount);
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TrainDataType type;
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double precision;
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std::vector<std::pair<float,float> > borders;
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cv::Ptr<TrainData> data;
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cv::Mat testSamples;
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cv::Mat testResponses;
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static const int TEST_VALUE_LIMIT = 500;
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};
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void CV_SVMSGDTrainTest::generateSameBorders(int featureCount)
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{
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float lowerLimit = -TEST_VALUE_LIMIT;
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float upperLimit = TEST_VALUE_LIMIT;
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for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
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{
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borders.push_back(std::pair<float,float>(lowerLimit, upperLimit));
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}
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}
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void CV_SVMSGDTrainTest::generateDifferentBorders(int featureCount)
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{
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float lowerLimit = -TEST_VALUE_LIMIT;
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float upperLimit = TEST_VALUE_LIMIT;
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cv::RNG rng(0);
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for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
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{
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int crit = rng.uniform(0, 2);
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if (crit > 0)
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{
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borders.push_back(std::pair<float,float>(lowerLimit, upperLimit));
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}
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else
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{
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borders.push_back(std::pair<float,float>(lowerLimit/1000, upperLimit/1000));
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}
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}
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}
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float CV_SVMSGDTrainTest::decisionFunction(const Mat &sample, const Mat &weights, float shift)
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{
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return static_cast<float>(sample.dot(weights)) + shift;
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}
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void CV_SVMSGDTrainTest::makeData(int samplesCount, const Mat &weights, float shift, RNG &rng, Mat &samples, Mat & responses)
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{
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int featureCount = weights.cols;
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samples.create(samplesCount, featureCount, CV_32FC1);
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for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
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{
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rng.fill(samples.col(featureIndex), RNG::UNIFORM, borders[featureIndex].first, borders[featureIndex].second);
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}
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responses.create(samplesCount, 1, CV_32FC1);
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for (int i = 0 ; i < samplesCount; i++)
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{
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responses.at<float>(i) = decisionFunction(samples.row(i), weights, shift) > 0 ? 1.f : -1.f;
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}
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}
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CV_SVMSGDTrainTest::CV_SVMSGDTrainTest(const Mat &weights, float shift, TrainDataType _type, double _precision)
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{
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type = _type;
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precision = _precision;
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int featureCount = weights.cols;
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switch(type)
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{
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case UNIFORM_SAME_SCALE:
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generateSameBorders(featureCount);
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break;
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case UNIFORM_DIFFERENT_SCALES:
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generateDifferentBorders(featureCount);
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break;
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default:
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CV_Error(CV_StsBadArg, "Unknown train data type");
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}
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RNG rng(0);
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Mat trainSamples;
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Mat trainResponses;
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int trainSamplesCount = 10000;
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makeData(trainSamplesCount, weights, shift, rng, trainSamples, trainResponses);
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data = TrainData::create(trainSamples, cv::ml::ROW_SAMPLE, trainResponses);
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int testSamplesCount = 100000;
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makeData(testSamplesCount, weights, shift, rng, testSamples, testResponses);
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}
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void CV_SVMSGDTrainTest::run( int /*start_from*/ )
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{
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cv::Ptr<SVMSGD> svmsgd = SVMSGD::create();
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svmsgd->train(data);
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Mat responses;
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svmsgd->predict(testSamples, responses);
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int errCount = 0;
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int testSamplesCount = testSamples.rows;
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CV_Assert((responses.type() == CV_32FC1) && (testResponses.type() == CV_32FC1));
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for (int i = 0; i < testSamplesCount; i++)
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{
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if (responses.at<float>(i) * testResponses.at<float>(i) < 0)
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errCount++;
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}
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float err = (float)errCount / testSamplesCount;
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if ( err > precision )
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{
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
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}
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}
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void makeWeightsAndShift(int featureCount, Mat &weights, float &shift)
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{
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weights.create(1, featureCount, CV_32FC1);
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cv::RNG rng(0);
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double lowerLimit = -1;
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double upperLimit = 1;
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rng.fill(weights, RNG::UNIFORM, lowerLimit, upperLimit);
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shift = static_cast<float>(rng.uniform(-featureCount, featureCount));
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}
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TEST(ML_SVMSGD, trainSameScale2)
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{
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int featureCount = 2;
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Mat weights;
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float shift = 0;
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makeWeightsAndShift(featureCount, weights, shift);
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CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_SAME_SCALE);
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test.safe_run();
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}
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TEST(ML_SVMSGD, trainSameScale5)
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{
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int featureCount = 5;
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Mat weights;
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float shift = 0;
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makeWeightsAndShift(featureCount, weights, shift);
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CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_SAME_SCALE);
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test.safe_run();
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}
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TEST(ML_SVMSGD, trainSameScale100)
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{
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int featureCount = 100;
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Mat weights;
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float shift = 0;
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makeWeightsAndShift(featureCount, weights, shift);
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CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_SAME_SCALE, 0.02);
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test.safe_run();
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}
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TEST(ML_SVMSGD, trainDifferentScales2)
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{
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int featureCount = 2;
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Mat weights;
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float shift = 0;
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makeWeightsAndShift(featureCount, weights, shift);
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CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_DIFFERENT_SCALES, 0.01);
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test.safe_run();
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}
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TEST(ML_SVMSGD, trainDifferentScales5)
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{
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int featureCount = 5;
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Mat weights;
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float shift = 0;
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makeWeightsAndShift(featureCount, weights, shift);
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CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_DIFFERENT_SCALES, 0.01);
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test.safe_run();
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}
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TEST(ML_SVMSGD, trainDifferentScales100)
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{
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int featureCount = 100;
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Mat weights;
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float shift = 0;
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makeWeightsAndShift(featureCount, weights, shift);
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CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_DIFFERENT_SCALES, 0.01);
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test.safe_run();
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}
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TEST(ML_SVMSGD, twoPoints)
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{
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Mat samples(2, 2, CV_32FC1);
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samples.at<float>(0,0) = 0;
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samples.at<float>(0,1) = 0;
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samples.at<float>(1,0) = 1000;
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samples.at<float>(1,1) = 1;
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Mat responses(2, 1, CV_32FC1);
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responses.at<float>(0) = -1;
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responses.at<float>(1) = 1;
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cv::Ptr<TrainData> trainData = TrainData::create(samples, cv::ml::ROW_SAMPLE, responses);
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Mat realWeights(1, 2, CV_32FC1);
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realWeights.at<float>(0) = 1000;
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realWeights.at<float>(1) = 1;
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float realShift = -500000.5;
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float normRealWeights = static_cast<float>(norm(realWeights));
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realWeights /= normRealWeights;
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realShift /= normRealWeights;
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cv::Ptr<SVMSGD> svmsgd = SVMSGD::create();
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svmsgd->setOptimalParameters();
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svmsgd->train( trainData );
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Mat foundWeights = svmsgd->getWeights();
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float foundShift = svmsgd->getShift();
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float normFoundWeights = static_cast<float>(norm(foundWeights));
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foundWeights /= normFoundWeights;
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foundShift /= normFoundWeights;
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CV_Assert((norm(foundWeights - realWeights) < 0.001) && (abs((foundShift - realShift) / realShift) < 0.05));
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}
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