def __call__(self, net, input, target):
layer = net.layers[0]
if self.adapt:
while True:
self.epochf(None, net, input, target)
for inp, tar in zip(input, target):
out = net.step(inp)
err = tar - out
win = np.argmax(layer.out)
if np.max(err) == 0.0:
layer.np["w"][win] += self.lr * (inp - layer.np["w"][win])
else:
layer.np["w"][win] -= self.lr * (inp - layer.np["w"][win])
else:
while True:
output = []
winners = []
for inp, tar in zip(input, target):
out = net.step(inp)
output.append(out)
winners.append(np.argmax(layer.out))
e = self.error(net, input, target, output)
self.epochf(e, net, input, target)
error = target - output
sign = np.sign((np.max(error, axis=1) == 0) - 0.5)
layer.np["w"][winners] += self.lr * (input - layer.np["w"][winners])
return None
def __init__(self, x):
x = np.asfarray(x)
if x.ndim != 2:
raise ValueError('x mast have 2 dimensions')
min = np.min(x, axis=0)
dist = np.max(x, axis=0) - min
min = min.reshape((1, min.size,)) #replacement for " min.shape = 1, min.size"
dist = dist.reshape((1, dist.size,)) #replacement for " dist.shape = 1, dist.size"
self.min = min
self.dist = dist
