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main.py
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main.py
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# from collections import defaultdict
# from math import *
import numpy as np
import random
import sys
import re
import os
import matplotlib.image as mpimg
from visual import ann_visual_weights, rbm_visual_weights
from test_ann import test_ann
from test_rbm import test_rbm
def input_data(data_folder, n_classes=26, test_samples=[6,7,8], valid_samples=[5,6]):
""" """
in_file_names = [ fn for fn in os.listdir(data_folder)
if os.path.isfile( os.path.join(data_folder, fn) ) ]
X_test = [] # TEST
y_test = [] # * n_classes
X_train = [] # TRAIN
y_train = [] # * n_classes
y_valid = [] # VALID
X_valid = [] # * n_classes
y = [0] * 26
for fn in in_file_names:
m = re.match(ur'[a-z]+-(\d+)(?:-(\d+))?(?:-0)?.bmp', fn)
# print fn, m.group(0)
if m:
iy = int(m.group(1))
iz = int(m.group(2)) if m.group(2) else 0
image = mpimg.imread( os.path.join(data_folder, fn) )
b = np.array(image[:,:,0] > 0, dtype=int).flatten()
y[iy] = 1
if iz in test_samples:
y_test.append(y[:])
X_test.append(b)
# elif iz in valid_samples:
# y_valid.append(y[:])
# X_valid.append(b)
else:
y_train.append(y[:])
X_train.append(b)
y[iy] = 0
# inputs by rows
X_test = np.array(X_test)
y_test = np.array(y_test)
X_train = np.array(X_train)
y_train = np.array(y_train)
# X_valid = np.array(X_valid)
# y_valid = np.array(y_valid)
print X_test.shape, y_test.shape, \
X_train.shape, y_train.shape #, \
# X_valid.shape, y_valid.shape
return X_train, y_train, X_test, y_test # , X_valid, y_valid
if __name__ == '__main__':
if sys.argv[1] == '-nt':
X_train, y_train, X_test, y_test = input_data("./data/", test_samples=[7,8])
test_ann(X_train, y_train, X_test, y_test, [X_train.shape[1], 30, y_train.shape[1]])
elif sys.argv[1] == '-nv':
X_train, y_train, X_test, y_test = input_data("./data/", test_samples=[7,8])
ann_visual_weights(X_train, y_train, X_test, y_test)
elif sys.argv[1] == '-rt':
X_train, y_train, X_test, y_test = input_data("./data/", test_samples=[])
test_rbm( X_train, y_train, X_test, y_test)
elif sys.argv[1] == '-rv':
X_train, y_train, X_test, y_test = input_data("./data/", test_samples=[])
rbm_visual_weights(X_train, y_train, X_test, y_test)
else: print 'incorrect console argument'