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Tools.py
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Tools.py
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import theano
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams
from PIL import Image
import numpy as np
import cPickle
import os
theano.config.floatX = 'float32'
def show_image_from(out_now):
im = create_image_helper(out_now)
im.show()
# print "Output image"
def save_images_from(single_patterns, img_path):
img_ctr = 0
if not os.path.exists(img_path):
os.mkdir(img_path)
else:
print "Error: Img-path already exists."
for pattern in single_patterns:
img = create_image_helper(pattern)
img.save(img_path+'/image#'+str(img_ctr), 'BMP')
img_ctr += 1
def save_images_from_pairs(pattern_pairs, img_path):
img_ctr = 0
if not os.path.exists(img_path):
os.mkdir(img_path)
else:
print "Error: Img-path already exists."
for pattern in pattern_pairs:
img_in = create_image_helper(pattern[0])
img_out = create_image_helper(pattern[1])
img_in.save(img_path+'/image#'+str(img_ctr)+'_input', 'BMP')
img_out.save(img_path+'/image#'+str(img_ctr)+'_output', 'BMP')
img_ctr += 1
def create_image_helper(in_values):
pattern = np.asarray(in_values, dtype=np.float32)
width = 7
height = 7
pixel_scaling_factor = 2 ** 3 # Exponent of two for symmetry.
im = Image.new('1', (width*pixel_scaling_factor, height*pixel_scaling_factor))
for element in range(pattern.shape[1]):
for i in range(pixel_scaling_factor):
for j in range(pixel_scaling_factor):
im.putpixel(((element % width)*pixel_scaling_factor + j,
np.floor(element/height).astype(np.int8) * pixel_scaling_factor + i),
pattern[0][element] * 255)
return im
def show_image_ca3(in_values):
pattern = np.asarray(in_values, dtype=np.float32)
width = 24
height = 20
pixel_scaling_factor = 2 ** 3
im = Image.new('1', (width*pixel_scaling_factor, height*pixel_scaling_factor))
for element in range(pattern.shape[1]):
for i in range(pixel_scaling_factor):
for j in range(pixel_scaling_factor):
im.putpixel(((element % width)*pixel_scaling_factor + j,
np.floor(element/width).astype(np.int8) * pixel_scaling_factor + i),
pattern[0][element] * 255)
im.show()
shared_random_generator = RandomStreams()
x_r = T.iscalar()
y_r = T.iscalar()
p_scalar = T.fscalar('p_scalar')
binomial_f = theano.function([x_r, y_r, p_scalar], outputs=shared_random_generator.
binomial(size=(x_r, y_r), n=1, p=p_scalar, dtype='float32'))
rows = T.iscalar()
columns = T.iscalar()
uniform_f = theano.function([rows, columns], outputs=shared_random_generator.
uniform(size=(rows, columns), low=-0.1, high=0.1, dtype='float32'))
random_f = theano.function([rows, columns], outputs=shared_random_generator.random_integers(
size=(rows, columns), low=0, high=10000, dtype='float32')/10000.)
def set_contains_pattern(patterns_set, pattern):
for pat in patterns_set:
if get_pattern_correlation(pat, pattern) == 1:
return True
return False
pat1 = T.fmatrix()
pat2 = T.fmatrix()
get_pattern_correlation = theano.function([pat1, pat2], outputs=T.sum(pat1 * pat2)/(pat1.shape[0] * pat1.shape[1]))
def get_pattern_correlation_slow(pattern_1, pattern_2):
corr = 0
for row_ind in range(len(pattern_1)):
for col_ind in range(len(pattern_1[0])):
corr += pattern_1[row_ind][col_ind] * pattern_2[row_ind][col_ind]
return corr
def save_experiment_4_1_results(hpc, chaotically_recalled_patterns, custom_name):
experiment_dir = get_experiment_dir()
hpc_f = file(experiment_dir+'/hpc_'+custom_name+'.save', 'wb')
cPickle.dump(hpc, hpc_f, protocol=cPickle.HIGHEST_PROTOCOL)
hpc_f.close()
save_images_from(chaotically_recalled_patterns, experiment_dir+'/images')
f2 = file(experiment_dir+'/_chaotically_recalled_patterns.save', 'wb')
cPickle.dump(chaotically_recalled_patterns, f2, protocol=cPickle.HIGHEST_PROTOCOL)
f2.close()
def save_experiment_4_2_results(information_vector, custom_name):
experiment_dir = get_experiment_dir()
pseudopatterns_I = information_vector[0]
pseudopatterns_II = information_vector[1]
save_images_from_pairs(pseudopatterns_I, experiment_dir+'/pseudopatterns_I')
save_images_from_pairs(pseudopatterns_II, experiment_dir+'/pseudopatterns_II')
neocortically_recalled_IOs = information_vector[2]
save_images_from_pairs(neocortically_recalled_IOs, experiment_dir+'/neocortical_recall')
f_goodness = file(experiment_dir+'/goodness_of_fit.txt', 'w')
f_goodness.write(str(information_vector[4]) + ' <-- goodness of fit') # goodness of fit
f_goodness.close()
f = file(experiment_dir+'/information_vector'+custom_name+'.save', 'wb')
cPickle.dump(information_vector, f, protocol=cPickle.HIGHEST_PROTOCOL)
f.close()
def get_experiment_counter():
experiment_ctr_f = file('saved_data/ctr.save', 'rb')
experiment_ctr = cPickle.load(experiment_ctr_f)
experiment_ctr_f.close()
return experiment_ctr
def increment_experiment_counter():
experiment_ctr_f = file('saved_data/ctr.save', 'rb')
experiment_ctr = cPickle.load(experiment_ctr_f)
experiment_ctr_f.close()
experiment_ctr += 1
experiment_ctr_f = file('saved_data/ctr.save', 'wb')
cPickle.dump(experiment_ctr, experiment_ctr_f, protocol=cPickle.HIGHEST_PROTOCOL)
experiment_ctr_f.close()
def get_experiment_dir():
experiment_counter = get_experiment_counter()
experiment_dir = 'saved_data/experiment#'+str(experiment_counter)
if not os.path.exists(experiment_dir):
os.mkdir(experiment_dir)
else:
print "Info.: OS path already exists."
return experiment_dir