def error(self, net, input):
layer = net.layers[0]
winner_output = np.zeros_like(input)
output = net.sim(input)
winners = np.argmax(output, axis=1)
e = layer.np["w"][winners] - input
return net.errorf(e)
def deriv(self, x, y):
"""
Derivative of transfer function SatLin
"""
d = np.zeros_like(x)
d[(x > self.out_min) & (x < self.out_max) ] = 1
return d
def deriv(self, x, y):
"""
Derivative of transfer function SatLins
"""
d = np.zeros_like(x)
d[(x > -1) & (x < 1) ] = 1
return d
def __call__(self, dist):
r = np.zeros_like(dist)
min = np.argmin(dist)
r[min] = 1.0
return r
def deriv(self, x, y):
"""
Derivative of transfer function HardLims
"""
return np.zeros_like(x)
