if(num_classes == 2)
{
labels_l.convertTo(labels, CV_32F);
- //currently supported training methods LogisticRegression::BATCH and LogisticRegression::MINI_BATCH
if(this->params.train_method == LogisticRegression::BATCH)
new_theta = compute_batch_gradient(data_t, labels, init_theta);
else
{
new_local_labels = (labels_l == it->second)/255;
new_local_labels.convertTo(labels, CV_32F);
- // currently supported training methods LogisticRegression::BATCH and LogisticRegression::MINI_BATCH
if(this->params.train_method == LogisticRegression::BATCH)
new_theta = compute_batch_gradient(data_t, labels, init_theta);
else