fives = fives + 1 elif fives == 1: five_1 = train_character_list[i] if ms.walk_around_ccw(five_1._data_bw).shape[0] == ms.walk_around_ccw(five_0._data_bw).shape[0]: fives = fives + 1 i = i + 1 five_0._perimeter_path = ms.walk_around_ccw(five_0._data_bw) five_0._xseries = ms.convert_path_to_xseries(five_0._perimeter_path) five_1._perimeter_path = ms.walk_around_ccw(five_1._data_bw) five_1._xseries = ms.convert_path_to_xseries(five_1._perimeter_path) five_0_x = five_0._xseries five_0_x = data_transform.stretch_data_x(five_0_x) five_0_x = data_transform.normalize_data(five_0_x) five_1_x = five_1._xseries five_1_x = data_transform.stretch_data_x(five_1_x) five_1_x = data_transform.normalize_data(five_1_x)
########################### ## BEGIN DTW ALGORITHM ########################### correct_list = np.zeros((10,1)) total_list = np.zeros((10,1)) for i in range(train_character_list.shape[0]): train_character = train_character_list[i] train_character_list[i]._perimeter_path = ms.walk_around_ccw(train_character._data_bw) train_character._xseries = ms.convert_path_to_xseries(train_character._perimeter_path) train_character._yseries = ms.convert_path_to_yseries(train_character._perimeter_path) for i in range(test_character_list.shape[0]): test_character = test_character_list[i] test_character._perimeter_path = ms.walk_around_ccw(test_character._data_bw) test_character._xseries = ms.convert_path_to_xseries(test_character._perimeter_path) test_character._yseries = ms.convert_path_to_yseries(test_character._perimeter_path) chf.dtw_classify_character(test_character, train_character_list) if(test_character._classification == test_character._predicted_classification):
