def calculateELBO(self):
""" Compute Evidence Lower Bound """
Wtmp = self.markov_blanket["W"].getExpectations()
Ztmp = self.markov_blanket["Z"].getExpectations()
W, WW = Wtmp["E"], Wtmp["E2"]
Z, ZZ = Ztmp["E"], Ztmp["E2"]
zeta = self.params["zeta"]
tau = self.markov_blanket["Tau"].getValue()
mask = self.getMask()
# Precompute terms
ZW = Z.dot(W.T)
ZZWW = s.square(ZW) - s.dot(s.square(Z), s.square(W).T) + ZZ.dot(WW.T)
# term1 = 0.5*tau*(ZW - zeta)**2
term1 = 0.5 * tau * (ZZWW - 2 * ZW * zeta + s.square(zeta))
term2 = (ZW - zeta) * (sigmoid(zeta) *
(1. - self.obs / self.ratefn(zeta)))
term3 = self.ratefn(zeta) - self.obs * s.log(self.ratefn(zeta))
elbo = -(term1 + term2 + term3)
elbo[mask] = 0.
# I AM NOT SURE WHY NAs are generated...
np.isnan(elbo).sum()
elbo[np.isnan(elbo)] = 0.
return elbo.sum()
def updateExpectations(self):
# Update the pseudodata
self.E = self.params["zeta"] - 4.*(sigmoid(self.params["zeta"]) - self.obs)
# regress out feature-wise mean from the pseudodata
self.means = self.E.mean(axis=0).data
self.E -= self.means
def updateExpectations(self):
# Update the pseudodata
tau = self.markov_blanket["Tau"].getValue() # to-do: not expand
# self.E = self.params["zeta"] - sigmoid(self.params["zeta"])*(1-self.obs/self.ratefn(self.params["zeta"]))/tau[None,:]
self.E = self.params["zeta"] - sigmoid(self.params["zeta"]) * (
1 - self.obs / self.ratefn(self.params["zeta"])) / tau
self.E[self.mask] = 0.
def updateExpectations(self):
# Update the pseudodata
tau = self.markov_blanket["Tau"].getValue()
self.E = self.params["zeta"] - sigmoid(self.params["zeta"])*(1-self.obs/self.ratefn(self.params["zeta"])) / tau
self.E[self.mask] = 0.
# regress out feature-wise mean from the pseudodata
self.means = self.E.mean(axis=0).data
self.E -= self.means
def updateExpectations(self):
# Update the pseudodata
self.E = self.params["zeta"] - 4. * (sigmoid(self.params["zeta"]) -
self.obs)
self.means = self.E.mean(axis=0).data
self.E -= self.means
