def check(mu, Lambda, alpha, beta, ndim):
X = GaussianGamma(
mu,
(
Lambda if isinstance(Lambda._moments, WishartMoments) else
Lambda.as_wishart(ndim=ndim)
),
alpha,
beta,
ndim=ndim
)
self.assert_moments(
X,
postprocess=lambda u: [
u[0],
u[1] + linalg.transpose(u[1], ndim=ndim),
u[2],
u[3]
],
rtol=1e-5,
atol=1e-6,
eps=1e-8
)
X.observe(
(
np.random.randn(*(X.plates + X.dims[0])),
np.random.rand(*X.plates)
)
)
self.assert_message_to_parent(X, mu)
self.assert_message_to_parent(
X,
Lambda,
postprocess=lambda m: [
m[0] + linalg.transpose(m[0], ndim=ndim),
m[1],
]
)
self.assert_message_to_parent(X, beta)
def test_message_to_parents(self):
""" Check gradient passed to inputs parent node """
N = 3
D = 2
Mu = Gaussian(np.random.randn(D), random.covariance(D))
Lambda = Wishart(D, random.covariance(D))
A = Gaussian(np.random.randn(D,D), random.covariance(D))
V = Gamma(D, np.random.rand(D))
X = GaussianMarkovChain(Mu, Lambda, A, V, n=N+1)
Y = Gaussian(X, random.covariance(D))
self.assert_moments(
X,
postprocess=lambda u: [
u[0],
u[1] + linalg.transpose(u[1], ndim=1),
u[2]
]
)
Y.observe(np.random.randn(N+1, D))
self.assert_message_to_parent(X, Mu, eps=1e-8)
self.assert_message_to_parent(
X,
Lambda,
eps=1e-8,
postprocess=lambda u: [
u[0] + linalg.transpose(u[0], ndim=1),
u[1],
]
)
self.assert_message_to_parent(X, A)
self.assert_message_to_parent(X, V, eps=1e-10, atol=1e-5)
pass
def check(Mu, Lambda, A, V, U):
X = GaussianMarkovChain(Mu, Lambda, A, V, inputs=U)
Y = Gaussian(X, random.covariance(D))
# Check moments
self.assert_moments(
X,
postprocess=lambda u: [
u[0],
u[1] + linalg.transpose(u[1], ndim=1),
u[2]
]
)
Y.observe(np.random.randn(N+1, D))
X.update()
# Check gradient messages to parents
self.assert_message_to_parent(X, Mu)
self.assert_message_to_parent(
X,
Lambda,
postprocess=lambda phi: [
phi[0] + linalg.transpose(phi[0], ndim=1),
phi[1]
]
)
self.assert_message_to_parent(
X,
A,
postprocess=lambda phi: [
phi[0],
phi[1] + linalg.transpose(phi[1], ndim=1),
]
)
self.assert_message_to_parent(X, V)
self.assert_message_to_parent(X, U)
def _compute_moments(self, *u_parents):
"""
Compute the moments of the sum
"""
u0 = functools.reduce(np.add, (u_parent[0] for u_parent in u_parents))
u1 = functools.reduce(np.add, (u_parent[1] for u_parent in u_parents))
for i in range(self.N):
for j in range(i + 1, self.N):
xi_xj = linalg.outer(u_parents[i][0],
u_parents[j][0],
ndim=self.ndim)
xj_xi = linalg.transpose(xi_xj, ndim=self.ndim)
u1 = u1 + xi_xj + xj_xi
return [u0, u1]
def _compute_moments(self, *u_parents):
"""
Compute the moments of the sum
"""
u0 = functools.reduce(np.add,
(u_parent[0] for u_parent in u_parents))
u1 = functools.reduce(np.add,
(u_parent[1] for u_parent in u_parents))
for i in range(self.N):
for j in range(i+1, self.N):
xi_xj = linalg.outer(u_parents[i][0], u_parents[j][0], ndim=self.ndim)
xj_xi = linalg.transpose(xi_xj, ndim=self.ndim)
u1 = u1 + xi_xj + xj_xi
return [u0, u1]
