Go to the source code of this file.
Classes | |
| class | boosted_tree_classifier.boosted_tree_classifier |
Namespaces | |
| namespace | boosted_tree_classifier |
Functions | |
| def | boosted_tree_classifier.load |
| def | boosted_tree_classifier.postprocess |
| def | boosted_tree_classifier.release_train_datastructures |
| def | boosted_tree_classifier.save |
| def | boosted_tree_classifier.test |
| def | boosted_tree_classifier.test_postprocess |
| def | boosted_tree_classifier.train |
Variables | |
| boosted_tree_classifier::cv_classifier | |
| boosted_tree_classifier::test_feature_dict | |
| boosted_tree_classifier.test_labels | |
| tuple | boosted_tree_classifier.type_mask = cv.cvCreateMat(1, feature_vector_length+1, cv.CV_8UC1) |
| subsample from the features, NOT USED/NOT WORKING? else: print ut.getTime(), 'more than',max_traning_size,'features, sample from them...' select 2040000 features: all_data = [] all_labels = [] for dict in data: for index in range(dict['set_size']): if dict['labels'][index] == processor.LABEL_SURFACE or dict['labels'][index]== processor.LABEL_CLUTTER: fv = (dict['features'][index])[self.processor.features.get_indexvector(self.features)] all_data += [fv] all_labels += [dict['labels'][index]] | |