def _mW(self,K,W0,xd,NK,m0,XDim,beta0,S):
Winv = [None for _ in range(K)]
for k in range(K):
Winv[k] = NK[k]*S[k] + inv0(W0)
Q0 = np.reshape(xd[k,:] - m0, (XDim,1))
q = np.dot(Q0,Q0.T)
Winv[k] += (beta0*NK[k] / (beta0 + NK[k]) ) * q
assert np.shape(q)==(XDim,XDim)
W = []
for k in range(K):
try:
W.append(inv0(Winv[k]))
except np.linalg.LinAlgError:
#print 'Winv[%i]'%k, Winv[k]
raise np.linalg.LinAlgError()
return W
def _mW(self, K, W0, xd, NK, m0, XDim, beta0, S):
Winv = [None for _ in range(K)]
for k in range(K):
Winv[k] = NK[k] * S[k] + inv0(W0)
Q0 = reshape(xd[k, :] - m0, (XDim, 1))
q = dot(Q0, Q0.T)
Winv[k] += (beta0 * NK[k] / (beta0 + NK[k])) * q
assert shape(q) == (XDim, XDim)
W = []
for k in range(K):
try:
W.append(inv0(Winv[k]))
except linalg.linalg.LinAlgError:
#print 'Winv[%i]'%k, Winv[k]
raise linalg.linalg.LinAlgError()
return W
def expC(self):
"""calculate expected covariance matrix for each component"""
return np.array([inv0(Wk*vk) for (Wk,vk) in zip(self._W,self._vk)])
def expC(self):
#calculate expected covariance matrix (for each component)
return array([inv0(Wk*vk) for (Wk,vk) in zip(self._W,self._vk)])
def expC(self):
#calculate expected covariance matrix (for each component)
return array([inv0(Wk * vk) for (Wk, vk) in zip(self._W, self._vk)])
