patterns.append(array)
hop = Hopfield()
hop.memorize_patterns(patterns, view = view)
print("Training complete. Do you want to see the weight matrices? [Y/N]")
yesNo = str(raw_input())
if yesNo == 'Y' or yesNo == 'y':
hop.show_patterns()
print("Stating restoration...")
corruptedImage = convert_image('corrupted.png')
originalCorr = corruptedImage
newImage = hop.update(corruptedImage)
i = 0
while corruptedImage.all() != newImage.all():
print("update #{}".format(i))
corruptedImage = newImage
newImage = hop.update(corruptedImage)
i += 1
if i > 10000:
print("Warning: forced break!")
break
plt.subplot(211)
plt.imshow(originalCorr,
cmap=plt.cm.BuPu_r,
interpolation='none')
def train(train_data):
model = Hopfield(0, 25)
model.update(train_data)
return model
