sys.path.append('/home/preston/Desktop/Programming/p_lib/python_lib/misc/') sys.path.append('/home/preston/Desktop/Programming/p_lib/python_lib/plot/') sys.path.append('/home/preston/Desktop/Programming/p_lib/python_lib/regression/') import p_plot import Maze_Solver as ms import Character as ch import Character_Functions as chf import softmax_regression as sr import numpy as np character_file_directory = '/home/preston/Desktop/Programming/datasci/projects/digit_recognizer/data/' character_train_file_name = 'short_train.csv' character_test_file_name = 'train.csv' train_character_list = chf.load_characters_kaggle_format(character_file_directory +\ character_train_file_name, 'train', (0,100)) test_character_list = chf.load_characters_kaggle_format(character_file_directory +\ character_test_file_name, 'train', (0,100)) for i in range(train_character_list.shape[0]): char = train_character_list[i] char.calculate_char_features() for i in range(test_character_list.shape[0]): if i%1000 == 0: print("calculating features for " + str(i) + "th test event...") test_character_list[i].calculate_char_features() print('begin softmax regression...')
sys.path.append('/home/preston/Desktop/Programming/p_lib/python_lib/misc/') sys.path.append('/home/preston/Desktop/Programming/p_lib/python_lib/plot/') sys.path.append( '/home/preston/Desktop/Programming/p_lib/python_lib/regression/') import p_plot import Maze_Solver as ms import Character as ch import Character_Functions as chf import softmax_regression as sr import numpy as np character_file_directory = '/home/preston/Desktop/Programming/datasci/projects/digit_recognizer/data/' character_train_file_name = 'short_train.csv' character_test_file_name = 'train.csv' train_character_list = chf.load_characters_kaggle_format(character_file_directory +\ character_train_file_name, 'train', (0,100)) test_character_list = chf.load_characters_kaggle_format(character_file_directory +\ character_test_file_name, 'train', (0,100)) for i in range(train_character_list.shape[0]): char = train_character_list[i] char.calculate_char_features() for i in range(test_character_list.shape[0]): if i % 1000 == 0: print("calculating features for " + str(i) + "th test event...") test_character_list[i].calculate_char_features() print('begin softmax regression...') Lambda = 0.05
sys.exit('cannot understand arguments to main_dtw.py!') print('beginning DTW calculation in range:') print('train: ' + str(train_start_range) + ', ' + str(train_end_range)) print('train: ' + str(test_start_range) + ', ' + str(test_end_range)) ########################### ## LOAD DATA ########################### character_file_directory = '/home/preston/Desktop/Programming/datasci/projects/digit_recognizer/data/' character_train_file_name = 'short_train.csv' character_test_file_name = 'train.csv' train_character_list = chf.load_characters_kaggle_format(character_file_directory +\ character_train_file_name, 'train', (train_start_range,train_end_range)) test_character_list = chf.load_characters_kaggle_format(character_file_directory +\ character_test_file_name, 'train', (test_start_range,test_end_range)) ########################### ## 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]