def _update_inverses(self):
"""
Calculates and stores kernel, and its inverses.
"""
for j in range(self.num_latent_proc):
self.Kzz[j, :, :] = self.kernels_latent[j].K(self.Z[j, :, :])
self.chol[j, :, :] = jitchol(self.Kzz[j, :, :])
self.invZ[j, :, :] = inv_chol(self.chol[j, :, :])
self.log_detZ[j] = pddet(self.chol[j, :, :])
self.hypers_changed = False
self.inducing_changed = False
def _update_inverses(self):
"""
Calculates and stores kernel, and its inverses.
"""
for j in range(self.num_latent_proc):
self.Kzz[j, :, :] = self.kernels_latent[j].K(self.Z[j, :, :])
self.chol[j, :, :] = jitchol(self.Kzz[j, :, :])
self.invZ[j, :, :] = inv_chol(self.chol[j, :, :])
self.log_detZ[j] = pddet(self.chol[j, :, :])
self.hypers_changed = False
self.inducing_changed = False
def log_normal(self):
log_normal = -0.5 * (
self.num_latent * self.num_dim * np.log(2 * np.pi) + np.log(2))
for i in xrange(self.num_latent):
log_normal -= 0.5 * util.pddet(self.covars_cholesky[i])
return log_normal.astype(np.float32)
def log_pdf(self, j, k, l):
return -((self.s[0, j, :, :].shape[0])/2) * (math.log(2 * math.pi) + math.log(2.0)) - \
0.5 * pddet(self.L[0,j,:])
def log_normal(self):
log_normal = -0.5 * (self.num_latent * self.num_dim * np.log(2 * np.pi) + np.log(2))
for i in xrange(self.num_latent):
log_normal -= 0.5 * util.pddet(self.covars_cholesky[i])
return log_normal.astype(np.float32)
def log_pdf(self, j, k, l):
return -((self.s[0, j, :, :].shape[0])/2) * (math.log(2 * math.pi) + math.log(2.0)) - \
0.5 * pddet(self.L[0,j,:])