def __call__(self, value):
# check type
if isinstance(value, ArrayType):
pass
elif isinstance(value, (tuple,list)):
value = array(value)
else:
raise TypeError("pArray data must be of type array/tuple/list.")
# check rank
if rank(value) != self._rank:
raise TypeError("pArray data must be of rank %d but it is %d." % (_rank, rank(value)))
return value
def learn_step(self, input, output):
if rank(input) == 1:
input = reshape(input, (len(input), 1))
X = join((input, [[1]]))
self._fn.learn_step(X, output)
def learn_step(self, input, output):
if rank(input) == 1:
input = reshape(input, (self.num_inputs, 1))
if rank(output) == 1:
output = reshape(output, (self.num_outputs, 1))
result = self(input)
err = output - result
self.MSE = norm(err.flat**2) / self.num_outputs
self.debug("MSE =", self.MSE)
alpha = self.alpha / sum(input**2)
self.w += alpha * transpose(dot(input, transpose(err)))
self.debug("update ratio =", norm(self(input) - result) / norm(err))
def learn_step(self,input,output):
if rank(input) == 1:
input = reshape(input,(len(input),1))
X = join((input,[[1]]))
self._fn.learn_step(X,output)
def learn_step(self,input,output):
if rank(input) == 1:
input = reshape(input,(self.num_inputs,1))
if rank(output) == 1:
output = reshape(output,(self.num_outputs,1))
result = self(input)
err = output - result
self.MSE = norm(err.flat**2)/self.num_outputs
self.debug("MSE =",self.MSE)
alpha = self.alpha/sum(input**2)
self.w += alpha*transpose(dot(input,transpose(err)))
self.debug( "update ratio =", norm(self(input)-result)/norm(err))
def __call__(self, input, error=False):
if rank(input) == 1:
X = join((input, [1]))
else:
X = join((input, [[1]]))
return self._fn(X, error=error)
def __call__(self,input,error=False):
if rank(input) == 1:
X = join((input,[1]))
else:
X = join((input,[[1]]))
return self._fn(X,error=error)
