def __init__(self, interpreter=None, parent=None):
# initialization of the superclass
super(DesignerMainWindow, self).__init__(parent)
# setup the GUI --> function generated by pyuic4
self.setupUi(self)
self.hop = HopfieldNetwork(100)
self.hop.setClocksHopfield()
self.defaultButtonState()
self.mem1 = []
self.mem2 = []
self.mem3 = []
self.mem4 = []
self.input = []
self.connectElements()
self.runCount = 0
self.runPushButton.setEnabled(False)
self.statusbar.showMessage('Hopfield Network Demo')
self.inputChanged = False
length = len(v1)
for i in range(length):
if v1[i] != v2[i]:
diff += 1
diff /= length
return diff
countOfPatterns = 2
size = 100
#vectors = csv_parser.read_input('SN_projekt3/animals-14x9.csv')
vectors = PngReader.getSomeBitmaps(countOfPatterns, 'pokemon' + str(size))
width = size
height = size
hn = HopfieldNetwork(height * width, countOfPatterns, False, 0.1, 1)
result = hn.train(vectors)
print("Result: ", result)
f, axarr = plt.pyplot.subplots(countOfPatterns, 2)
for i in range(countOfPatterns):
axarr[i, 0].imshow(Img.vector_to_img(
vectors[i],
height,
width,
False,
))
axarr[i, 1].imshow(Img.vector_to_img(hn.Z[i], height, width, False))
diff = calculateDiff(vectors[i], hn.Z[i])
plt.pyplot.sca(axarr[i, 1])
def train_hopfield(rows_no, cols_no, patterns, args):
hopfield_net = HopfieldNetwork(rows_no * cols_no)
hopfield_net.learn_patterns(patterns, args.train_lr)
return hopfield_net
