1 /*M/////////////////////////////////////////////////////////////////////////////////////// 2 // 3 // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. 4 // 5 // By downloading, copying, installing or using the software you agree to this license. 6 // If you do not agree to this license, do not download, install, 7 // copy or use the software. 8 // 9 // 10 // Intel License Agreement 11 // 12 // Copyright (C) 2000, Intel Corporation, all rights reserved. 13 // Third party copyrights are property of their respective owners. 14 // 15 // Redistribution and use in source and binary forms, with or without modification, 16 // are permitted provided that the following conditions are met: 17 // 18 // * Redistribution's of source code must retain the above copyright notice, 19 // this list of conditions and the following disclaimer. 20 // 21 // * Redistribution's in binary form must reproduce the above copyright notice, 22 // this list of conditions and the following disclaimer in the documentation 23 // and/or other materials provided with the distribution. 24 // 25 // * The name of Intel Corporation may not be used to endorse or promote products 26 // derived from this software without specific prior written permission. 27 // 28 // This software is provided by the copyright holders and contributors "as is" and 29 // any express or implied warranties, including, but not limited to, the implied 30 // warranties of merchantability and fitness for a particular purpose are disclaimed. 31 // In no event shall the Intel Corporation or contributors be liable for any direct, 32 // indirect, incidental, special, exemplary, or consequential damages 33 // (including, but not limited to, procurement of substitute goods or services; 34 // loss of use, data, or profits; or business interruption) however caused 35 // and on any theory of liability, whether in contract, strict liability, 36 // or tort (including negligence or otherwise) arising in any way out of 37 // the use of this software, even if advised of the possibility of such damage. 38 // 39 //M*/ 40 41 #include "precomp.hpp" 42 43 namespace cv { namespace ml { 44 45 ParamGrid::ParamGrid() { minVal = maxVal = 0.; logStep = 1; } 46 ParamGrid::ParamGrid(double _minVal, double _maxVal, double _logStep) 47 { 48 minVal = std::min(_minVal, _maxVal); 49 maxVal = std::max(_minVal, _maxVal); 50 logStep = std::max(_logStep, 1.); 51 } 52 53 bool StatModel::empty() const { return !isTrained(); } 54 55 int StatModel::getVarCount() const { return 0; } 56 57 bool StatModel::train( const Ptr<TrainData>&, int ) 58 { 59 CV_Error(CV_StsNotImplemented, ""); 60 return false; 61 } 62 63 bool StatModel::train( InputArray samples, int layout, InputArray responses ) 64 { 65 return train(TrainData::create(samples, layout, responses)); 66 } 67 68 float StatModel::calcError( const Ptr<TrainData>& data, bool testerr, OutputArray _resp ) const 69 { 70 Mat samples = data->getSamples(); 71 int layout = data->getLayout(); 72 Mat sidx = testerr ? data->getTestSampleIdx() : data->getTrainSampleIdx(); 73 const int* sidx_ptr = sidx.ptr<int>(); 74 int i, n = (int)sidx.total(); 75 bool isclassifier = isClassifier(); 76 Mat responses = data->getResponses(); 77 78 if( n == 0 ) 79 n = data->getNSamples(); 80 81 if( n == 0 ) 82 return -FLT_MAX; 83 84 Mat resp; 85 if( _resp.needed() ) 86 resp.create(n, 1, CV_32F); 87 88 double err = 0; 89 for( i = 0; i < n; i++ ) 90 { 91 int si = sidx_ptr ? sidx_ptr[i] : i; 92 Mat sample = layout == ROW_SAMPLE ? samples.row(si) : samples.col(si); 93 float val = predict(sample); 94 float val0 = responses.at<float>(si); 95 96 if( isclassifier ) 97 err += fabs(val - val0) > FLT_EPSILON; 98 else 99 err += (val - val0)*(val - val0); 100 if( !resp.empty() ) 101 resp.at<float>(i) = val; 102 /*if( i < 100 ) 103 { 104 printf("%d. ref %.1f vs pred %.1f\n", i, val0, val); 105 }*/ 106 } 107 108 if( _resp.needed() ) 109 resp.copyTo(_resp); 110 111 return (float)(err / n * (isclassifier ? 100 : 1)); 112 } 113 114 /* Calculates upper triangular matrix S, where A is a symmetrical matrix A=S'*S */ 115 static void Cholesky( const Mat& A, Mat& S ) 116 { 117 CV_Assert(A.type() == CV_32F); 118 119 int dim = A.rows; 120 S.create(dim, dim, CV_32F); 121 122 int i, j, k; 123 124 for( i = 0; i < dim; i++ ) 125 { 126 for( j = 0; j < i; j++ ) 127 S.at<float>(i,j) = 0.f; 128 129 float sum = 0.f; 130 for( k = 0; k < i; k++ ) 131 { 132 float val = S.at<float>(k,i); 133 sum += val*val; 134 } 135 136 S.at<float>(i,i) = std::sqrt(std::max(A.at<float>(i,i) - sum, 0.f)); 137 float ival = 1.f/S.at<float>(i, i); 138 139 for( j = i + 1; j < dim; j++ ) 140 { 141 sum = 0; 142 for( k = 0; k < i; k++ ) 143 sum += S.at<float>(k, i) * S.at<float>(k, j); 144 145 S.at<float>(i, j) = (A.at<float>(i, j) - sum)*ival; 146 } 147 } 148 } 149 150 /* Generates <sample> from multivariate normal distribution, where <mean> - is an 151 average row vector, <cov> - symmetric covariation matrix */ 152 void randMVNormal( InputArray _mean, InputArray _cov, int nsamples, OutputArray _samples ) 153 { 154 Mat mean = _mean.getMat(), cov = _cov.getMat(); 155 int dim = (int)mean.total(); 156 157 _samples.create(nsamples, dim, CV_32F); 158 Mat samples = _samples.getMat(); 159 randu(samples, 0., 1.); 160 161 Mat utmat; 162 Cholesky(cov, utmat); 163 int flags = mean.cols == 1 ? 0 : GEMM_3_T; 164 165 for( int i = 0; i < nsamples; i++ ) 166 { 167 Mat sample = samples.row(i); 168 gemm(sample, utmat, 1, mean, 1, sample, flags); 169 } 170 } 171 172 }} 173 174 /* End of file */ 175
