def __call__(self,x):
x-=self.t1
clip_lower(x,0)
z = (x>=self.t2)*(1.0*exp(self.e*(-self.t2))+(x-self.t2)) + \
(x<self.t2)*(exp(self.e*(x-self.t2))-exp(self.e*(-self.t2)))
x*=0
x+=self.a*z
def __call__(self, x):
x -= self.t1
clip_lower(x, 0)
z = (x>=self.t2)*(1.0*exp(self.e*(-self.t2))+(x-self.t2)) + \
(x<self.t2)*(exp(self.e*(x-self.t2))-exp(self.e*(-self.t2)))
x *= 0
x += self.a * z
def __call__(self,x):
if self.first_call:
self.first_call = False
if self.randomized_init:
self.t = ones(x.shape, x.dtype.char) * self.t_init + \
(topo.pattern.random.UniformRandom() \
(xdensity=x.shape[0],ydensity=x.shape[1])-0.5) * \
self.noise_magnitude*2
else:
self.t = ones(x.shape, x.dtype.char) * self.t_init
x -= self.t
clip_lower(x,0)
x *= self.gain
def __call__(self,x):
if self.first_call:
self.first_call = False
if self.randomized_init:
self.t = ones(x.shape, x.dtype.char) * self.t_init + \
(topo.pattern.random.UniformRandom() \
(xdensity=x.shape[0],ydensity=x.shape[1]) \
-0.5)*self.noise_magnitude*2
else:
self.t = ones(x.shape, x.dtype.char) * self.t_init
self.y_avg = ones(x.shape, x.dtype.char) * self.target_activity
x_orig = copy.copy(x); x -= self.t
clip_lower(x,0); x *= self.linear_slope
if self.plastic: # & (float(topo.sim.time()) % 1.0 >= 0.54):
self.y_avg = (1.0-self.smoothing)*x + self.smoothing*self.y_avg
self.t += self.learning_rate * (self.y_avg - self.target_activity)
def __call__(self, x):
if self.first_call:
self.first_call = False
if self.randomized_init:
self.t = ones(x.shape, x.dtype.char) * self.t_init + (topo.pattern.random.UniformRandom(seed=123)(xdensity=x.shape[0], ydensity=x.shape[1]) - 0.5) * self.noise_magnitude * 2
else:
self.t = ones(x.shape, x.dtype.char) * self.t_init
self.y_avg = ones(x.shape, x.dtype.char) * self.mu
x_orig = copy(x)
x -= self.t
clip_lower(x, 0)
x *= self.alpha
if self.plastic & (float(topo.sim.time()) % 1.0 >= 0.54):
self.y_avg = (1.0 - self.smoothing) * x + self.smoothing * self.y_avg
self.t += self.eta * (self.y_avg - self.mu)
def __call__(self,x):
x -= self.t
clip_lower(x,0)
a = power(x,self.e)
x*=0
x+=a
def __call__(self,x):
x -= self.t
clip_lower(x,0)
x *= x
def _apply_threshold(self,x):
"""Applies the piecewise-linear thresholding operation to the activity."""
x -= self.t
clip_lower(x,0)
if self.linear_slope != 1.0:
x *= self.linear_slope
def __call__(self, x):
x -= self.t
clip_lower(x, 0)
a = power(x, self.e)
x *= 0
x += a
def __call__(self, x):
x -= self.t
clip_lower(x, 0)
x *= x
