Lines Matching full:trainsamples
96 trainSamples.release();
332 preprocessSampleData(samples, trainSamples, isKMeansInit ? CV_32FC1 : CV_64FC1, false);
396 int nsamples = trainSamples.rows;
402 if(trainSamples.type() != CV_32FC1)
403 trainSamples.convertTo(trainSamplesFlt, CV_32FC1);
405 trainSamplesFlt = trainSamples;
421 if(trainSamples.type() != CV_64FC1)
425 trainSamples = trainSamplesBuffer;
439 const Mat sample = trainSamples.row(sampleIndex);
477 int dim = trainSamples.cols;
549 trainSamples.release();
630 trainProbs.create(trainSamples.rows, nclusters, CV_64FC1);
631 trainLabels.create(trainSamples.rows, 1, CV_32SC1);
632 trainLogLikelihoods.create(trainSamples.rows, 1, CV_64FC1);
636 CV_DbgAssert(trainSamples.type() == CV_64FC1);
639 for(int sampleIndex = 0; sampleIndex < trainSamples.rows; sampleIndex++)
642 Vec2d res = computeProbabilities(trainSamples.row(sampleIndex), &sampleProbs, CV_64F);
651 int dim = trainSamples.cols;
661 const double minPosWeight = trainSamples.rows * DBL_EPSILON;
676 for(int sampleIndex = 0; sampleIndex < trainSamples.rows; sampleIndex++)
677 clusterMean += trainProbs.at<double>(sampleIndex, clusterIndex) * trainSamples.row(sampleIndex);
706 for(int sampleIndex = 0; sampleIndex < trainSamples.rows; sampleIndex++)
708 centeredSample = trainSamples.row(sampleIndex) - means.row(clusterIndex);
757 weights /= trainSamples.rows;
826 Mat trainSamples;
