def applyFilter(self,inputSig):
if np.size(inputSig) == 1:
u = inputSig # Handle scalar case
myXnn = self.Xnn # get current state
self.Xnn = np.add(np.matmul(self.A,myXnn), self.B*u) # State updated
ytemp = np.add(np.matmul(self.C,myXnn), self.D*u)
filtSig = np.asscalar(ytemp)
else:
filtSig = [0]*len(inputSig) # list of zeros
for i in range(len(inputSig)):
u = inputSig[i]
myXnn = self.Xnn # get current state
self.Xnn = np.add(np.matmul(self.A,myXnn), self.B*u) # State updated
ytemp = np.add(np.matmul(self.C,myXnn), self.D*u)
# self.Xnn = np.add(matmult(self.A, myXnn), self.B*u)
# ytemp = np.add(matmult(self.C, myXnn), self.D*u)
filtSig[i] = np.asscalar(ytemp)
return filtSig
def forward(self, ngrami):
x = self.C[ngrami[0, :], :][0]
x = x.reshape((x.shape[0] * x.shape[1], 1))
o = np.dot(self.H, x) + self.D
a = self.sigmoid(o)
e = np.dot(self.U, a) + self.B
z = np.dot(self.W, x)
y = np.add(e, z)
return y, a, x
def forward(self, ngrami):
aux = self.C[ngrami[0, :], :][0]
aux = aux.reshape((aux.shape[0] * aux.shape[1], 1))
x = shared(np.asmatrix(aux, 'float32'))
o = self.linear(self.H, x, self.D)
a = self.sigmoid(o)
e = self.linear(self.U, a, self.B)
z = self.dot(self.W, x)
y = np.add(e, z)
return y, a, x
