def forward(self, input):
"""
Compute forward step over the input using its weights
Parameters
----------
input : ndarray
a numpy array (1,nIn + 1) containing the input of the layer
Returns
-------
ndarray :
a numpy array (nOut,1) containing the output of the layer
"""
self.input[1:, :] = input.T
self.output = Activation.sigmoid(np.dot(self.weights, self.input))
return self.output
def _fire(self, inp):
return Activation.sigmoid(np.dot(np.array(inp), self.weights))
def _fire(self, inp):
return Activation.sigmoid(np.dot(np.array(inp), self.weight))
def _fire(self, inp):
#print np.array(inp).shape
#print np.array(self.weights).shape
return Activation.sigmoid(np.dot(np.array(inp), self.weights))
pass
def _fire(self, inp):
#TODO compute a vector containing all sigmoids of neurons
ret = np.zeros(self.n_out)
for i in range(0, self.n_out):
ret[i] = Activation.sigmoid(np.dot(np.append(1,inp), self.weights[:,i]))
return ret
def fire(self, input):
return Activation.sigmoid(np.dot(np.array(input), self.weight))
def _fire(self, inp, weightsOfNeuron):
return Activation.sigmoid(np.dot(np.array(inp), np.array(weightsOfNeuron)))
def fire(self, input):
# Look at how we change the activation function here!!!!
# Not Activation.sign as in the perceptron, but sigmoid
return Activation.sigmoid(np.dot(np.array(input), self.weight))
def fire(self, input):
# input (n,1)
return Activation.sigmoid(np.dot(self.weight,input))
