def compute_u_and_g(phi, mask=True):
#print(-phi[1])
L = utils.m_chol(-phi[1])
k = np.shape(phi[0])[-1]
# Moments
u0 = utils.m_chol_solve(L, 0.5 * phi[0])
u1 = utils.m_outer(u0, u0) + 0.5 * utils.m_chol_inv(L)
u = [u0, u1]
# G
g = (-0.5 * np.einsum('...i,...i', u[0], phi[0]) +
0.5 * utils.m_chol_logdet(L) + 0.5 * np.log(2) * k)
#+ 0.5 * np.log(2) * self.dims[0][0])
return (u, g)
def compute_u_and_g(phi, mask=True):
#print(-phi[1])
L = utils.m_chol(-phi[1])
k = np.shape(phi[0])[-1]
# Moments
u0 = utils.m_chol_solve(L, 0.5*phi[0])
u1 = utils.m_outer(u0, u0) + 0.5 * utils.m_chol_inv(L)
u = [u0, u1]
# G
g = (-0.5 * np.einsum('...i,...i', u[0], phi[0])
+ 0.5 * utils.m_chol_logdet(L)
+ 0.5 * np.log(2) * k)
#+ 0.5 * np.log(2) * self.dims[0][0])
return (u, g)
def random(self):
# TODO/FIXME: You shouldn't draw random values for
# observed/fixed elements!
# Note that phi[1] is -0.5*inv(Cov)
U = utils.m_chol(-2 * self.phi[1])
mu = self.u[0]
z = np.random.normal(0, 1, self.get_shape(0))
# Compute mu + U'*z
#return mu + np.einsum('...ij,...i->...j', U, z)
#scipy.linalg.solve_triangular(a, b, trans=0, lower=False, unit_diagonal=False, overwrite_b=False, debug=False)
#print('gaussian.random', np.shape(mu), np.shape(z))
z = utils.m_solve_triangular(U, z, trans='T', lower=False)
return mu + z
def random(self):
# TODO/FIXME: You shouldn't draw random values for
# observed/fixed elements!
# Note that phi[1] is -0.5*inv(Cov)
U = utils.m_chol(-2*self.phi[1])
mu = self.u[0]
z = np.random.normal(0, 1, self.get_shape(0))
# Compute mu + U'*z
#return mu + np.einsum('...ij,...i->...j', U, z)
#scipy.linalg.solve_triangular(a, b, trans=0, lower=False, unit_diagonal=False, overwrite_b=False, debug=False)
#print('gaussian.random', np.shape(mu), np.shape(z))
z = utils.m_solve_triangular(U, z, trans='T', lower=False)
return mu + z
