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40
41 #include "precomp.hpp"
42
43 namespace cv { namespace ml {
44
ParamGrid()45 ParamGrid::ParamGrid() { minVal = maxVal = 0.; logStep = 1; }
ParamGrid(double _minVal,double _maxVal,double _logStep)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
empty() const53 bool StatModel::empty() const { return !isTrained(); }
54
getVarCount() const55 int StatModel::getVarCount() const { return 0; }
56
train(const Ptr<TrainData> &,int)57 bool StatModel::train( const Ptr<TrainData>&, int )
58 {
59 CV_Error(CV_StsNotImplemented, "");
60 return false;
61 }
62
train(InputArray samples,int layout,InputArray responses)63 bool StatModel::train( InputArray samples, int layout, InputArray responses )
64 {
65 return train(TrainData::create(samples, layout, responses));
66 }
67
calcError(const Ptr<TrainData> & data,bool testerr,OutputArray _resp) const68 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 */
Cholesky(const Mat & A,Mat & 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 */
randMVNormal(InputArray _mean,InputArray _cov,int nsamples,OutputArray _samples)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