55     int nclusters;  member in cv::ml::EMImpl
63         nclusters = val;  in CV_IMPL_PROPERTY_S()
64         CV_Assert(nclusters > 1);  in CV_IMPL_PROPERTY_S()
69         return nclusters;  in getClustersNumber()
87         nclusters = DEFAULT_NCLUSTERS;  in EMImpl()
172             _outputs.create(samples.rows, nclusters, ptype);  in predict()
208             _probs.create(1, nclusters, ptype);  in predict2()
236                                int nclusters, int covMatType, const Mat* probs, const Mat* means,  in checkTrainData()  argument
247         CV_Assert(nclusters > 0);  in checkTrainData()
248         CV_Assert(nclusters <= nsamples);  in checkTrainData()
258              probs->rows == nsamples && probs->cols == nclusters &&  in checkTrainData()
263 …   (weights->cols == 1 || weights->rows == 1) && static_cast<int>(weights->total()) == nclusters &&  in checkTrainData()
268              means->rows == nclusters && means->cols == dim &&  in checkTrainData()
273              static_cast<int>(covs->size()) == nclusters));  in checkTrainData()
328         checkTrainData(startStep, samples, nclusters, covMatType, probs0, means0, covs0, weights0);  in setTrainData()
356             covs.resize(nclusters);  in setTrainData()
365         covsEigenValues.resize(nclusters);  in decomposeCovs()
367             covsRotateMats.resize(nclusters);  in decomposeCovs()
368         invCovsEigenValues.resize(nclusters);  in decomposeCovs()
369         for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)  in decomposeCovs()
415         kmeans(trainSamplesFlt, nclusters, labels,  in clusterTrainSamples()
430         weights = Mat(1, nclusters, CV_64FC1, Scalar(0));  in clusterTrainSamples()
431         covs.resize(nclusters);  in clusterTrainSamples()
432         for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)  in clusterTrainSamples()
461         logWeightDivDet.create(1, nclusters, CV_64FC1);  in computeLogWeightDivDet()
464         for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)  in computeLogWeightDivDet()
528         covs.resize(nclusters);  in doTrain()
529         for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)  in doTrain()
572         Mat L(1, nclusters, CV_64FC1), centeredSample(1, dim, CV_64F);  in computeProbabilities()
574         for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)  in computeProbabilities()
630         trainProbs.create(trainSamples.rows, nclusters, CV_64FC1);  in eStep()
658         means.create(nclusters, dim, CV_64FC1);  in mStep()
664         for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)  in mStep()
682         covs.resize(nclusters);  in mStep()
683         covsEigenValues.resize(nclusters);  in mStep()
685             covsRotateMats.resize(nclusters);  in mStep()
686         invCovsEigenValues.resize(nclusters);  in mStep()
687         for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)  in mStep()
742         for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)  in mStep()
762         fs << "nclusters" << nclusters;  in write_params()
788         nclusters = (int)fn["nclusters"];  in read_params()