/external/opencv3/modules/ml/src/ |
nbayes.cpp | 52 nallvars = 0; 72 nallvars = __nallvars; 98 if( nallvars != __nallvars || 312 if( samples.type() != CV_32F || samples.cols != nallvars ) 314 "The input samples must be 32f matrix with the number of columns = nallvars" ); 345 fs << "var_count" << (var_idx.empty() ? nallvars : (int)var_idx.total()); 346 fs << "var_all" << nallvars; local 385 fn["var_all"] >> nallvars; local 387 if( nallvars <= 0 ) 440 nallvars = 0 448 int nallvars; member in class:cv::ml::NormalBayesClassifierImpl [all...] |
rtrees.cpp | 143 int nallvars = w->data->getNAllVars(); local 145 vector<float> samplebuf(nallvars); 149 Mat sample0, sample(nallvars, 1, CV_32F, &samplebuf[0]); 168 varImportance.resize(nallvars, 0.f); 204 sample = Mat( nallvars, 1, CV_32F, psamples + sstep0*w->sidx[j], sstep1*sizeof(psamples[0]) ); 254 sample0 = Mat( nallvars, 1, CV_32F, psamples + sstep0*w->sidx[j], sstep1*sizeof(psamples[0]) ); 255 for( k = 0; k < nallvars; k++ ) 278 for( vi_ = 0; vi_ < nallvars; vi_++ )
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tree.cpp | 149 int nallvars = data->getNAllVars(); local 155 setRangeVector(varIdx, nallvars); 207 int nallvars = (int)varType.size(); local 208 compVarIdx.assign(nallvars, -1); 213 CV_Assert( 0 <= vi && vi < nallvars && vi > prevIdx ); [all...] |