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]
