Searched refs:classLabels (Results 1 – 7 of 7) sorted by relevance
131 classLabels.clear(); in clear()174 data->getClassLabels().copyTo(classLabels); in startTraining()175 int nclasses = (int)classLabels.size(); in startTraining()468 int m = (int)classLabels.size(); in calcValue()538 node->value = classLabels[max_k]; in calcValue()643 int m = (int)classLabels.size(); in findSplitOrdClass()812 int m = (int)classLabels.size(); in findSplitCatClass()1371 int i, ncats = (int)catOfs.size(), nclasses = (int)classLabels.size(); in predictTrees()1392 predictType = !_isClassifier || (classLabels.size() == 2 && (flags & RAW_OUTPUT) != 0) ? in predictTrees()1487 sum = (flags & RAW_OUTPUT) ? (float)best_idx : classLabels[best_idx]; in predictTrees()[all …]
158 return classLabels.empty() ? VAR_ORDERED : VAR_CATEGORICAL; in getResponseType()194 Mat getClassLabels() const { return classLabels; } in getClassLabels()221 classLabels.release(); in clear()401 Mat(labels).copyTo(classLabels); in setData()969 Mat normCatResponses, classLabels, classCounters; member in cv::ml::TrainDataImpl
358 vector<int> classLabels; member in cv::ml::DTreesImpl
132 int nclasses = (int)classLabels.size(); in train()
367 ival = classLabels[ival]; in predictTrees()
1455 const int* classLabels = data->get_class_labels(data->data_root, classLabelsBuf); in update_weights() local1480 orig_response->data.i[i] = classLabels[i]*2 - 1; in update_weights()1485 weights->data.db[i] = w0*p[classLabels[i]]; in update_weights()1499 orig_response->data.i[i] = classLabels[i]*2 - 1; in update_weights()1501 weights->data.db[i] = w0*p[classLabels[i]]; in update_weights()