tagsLoader = TagsLoader(directory_tags_path, classification_DIP_result) tagsLoader.Load() # # gnx = result[0] map_fetures = result[1] number_of_learning_for_mean = 1.0 save_clf_file_name = result_path + r'clf/' load_clf_file_name = None random_state = 1 deep = False if (deep): mm.deepLearning( gnx, map_fetures, number_of_learning_for_mean=number_of_learning_for_mean, classifications=classification_DIP_result, tags_loader=tagsLoader, result_path=result_path, random_state=random_state) else: mm.machineLearning( gnx, map_fetures, number_of_learning_for_mean=number_of_learning_for_mean, classifications=classification_DIP_result, ml_algos=ml_algos, tags_loader=tagsLoader, result_path=result_path)
map_fetures = result[1] number_of_learning_for_mean = 1.0 deep = True if (deep): mm.deepLearning(gnx, map_fetures, number_of_learning_for_mean=3.0, classifications=classification_wiki_result, tags_loader=tagsLoader, result_path=result_path, edges=True, save_clf_file_name=result_path + r'clf/', load_clf_file_name=None, random_state=1) else: mm.machineLearning( gnx, map_fetures, number_of_learning_for_mean=number_of_learning_for_mean, classifications=classification_wiki_result, ml_algos=ml_algos, tags_loader=tagsLoader, result_path=result_path, edges=True, save_clf_file_name=None, load_clf_file_name=result_path + r'clf/', random_state=1) #step 3: vizualization