def ve_step(self, mu, sigma, alpha=1., beta=0.0):
"""
VE-step
"""
# Copy beta parameter
self.beta = beta
# Compute complete-data likelihood maps, replacing very small
# values for numerical stability
for i in range(self.ntissues):
self.ref_[:,i] = self.prior_[:,i]**alpha
self.ref_[:,i] *= self.dist(self.data_, mu[i], sigma[i])
self.ref_[:] = np.maximum(self.ref_, TINY)
# Normalize reference probability map
if beta == 0.0:
self.ppm[self.mask] = (self.ref_.T/self.ref_.sum(1)).T
# Update and normalize reference probabibility map using
# neighborhood information (mean-field theory)
else:
print(' ... MRF correction')
# Deal with mixing matrix and label switching
mixmat = self.mixmat
if not mixmat == None:
mixmat = self.sort_mixmat(mu)
self.ppm = _ve_step(self.ppm, self.ref_,
np.array(self.mask, dtype='int'),
beta, self.copy, self.hard, mixmat)
def ve_step(self, mu, sigma, alpha=1., beta=0.0):
"""
VE-step
"""
for i in range(self.ntissues):
self.ref_[:,i] = self.prior_[:,i]**alpha
self.ref_[:,i] *= self.dist(self.data_, mu[i], sigma[i])
# Replace very small values for numerical stability
self.ref_[:] = np.maximum(self.ref_, TINY)
# Normalize reference probability map
if beta == 0.0:
self.ppm[self.mask] = (self.ref_.T/self.ref_.sum(1)).T
# Update and normalize reference probabibility map using
# neighborhood information (mean-field theory)
else:
print(' ... MRF correction')
self.ppm = _ve_step(self.ppm, self.ref_,
np.array(self.mask, dtype='int'),
beta, self.copy, self.hard)
