def test_riemannian_gradient(self):
"""Test Riemannian gradient of a Gaussian node."""
D = 3
#
# Without observations
#
# Construct model
mu = np.random.randn(D)
Lambda = random.covariance(D)
X = Gaussian(mu, Lambda)
# Random initialization
mu0 = np.random.randn(D)
Lambda0 = random.covariance(D)
X.initialize_from_parameters(mu0, Lambda0)
# Initial parameters
phi0 = X.phi
# Gradient
g = X.get_riemannian_gradient()
# Parameters after VB-EM update
X.update()
phi1 = X.phi
# Check
self.assertAllClose(g[0], phi1[0] - phi0[0])
self.assertAllClose(g[1], phi1[1] - phi0[1])
# TODO/FIXME: Actually, gradient should be zero because cost function
# is zero without observations! Use the mask!
#
# With observations
#
# Construct model
mu = np.random.randn(D)
Lambda = random.covariance(D)
X = Gaussian(mu, Lambda)
V = random.covariance(D)
Y = Gaussian(X, V)
Y.observe(np.random.randn(D))
# Random initialization
mu0 = np.random.randn(D)
Lambda0 = random.covariance(D)
X.initialize_from_parameters(mu0, Lambda0)
# Initial parameters
phi0 = X.phi
# Gradient
g = X.get_riemannian_gradient()
# Parameters after VB-EM update
X.update()
phi1 = X.phi
# Check
self.assertAllClose(g[0], phi1[0] - phi0[0])
self.assertAllClose(g[1], phi1[1] - phi0[1])
pass
def test_riemannian_gradient(self):
"""Test Riemannian gradient of a Gaussian node."""
D = 3
#
# Without observations
#
# Construct model
mu = np.random.randn(D)
Lambda = random.covariance(D)
X = Gaussian(mu, Lambda)
# Random initialization
mu0 = np.random.randn(D)
Lambda0 = random.covariance(D)
X.initialize_from_parameters(mu0, Lambda0)
# Initial parameters
phi0 = X.phi
# Gradient
g = X.get_riemannian_gradient()
# Parameters after VB-EM update
X.update()
phi1 = X.phi
# Check
self.assertAllClose(g[0],
phi1[0] - phi0[0])
self.assertAllClose(g[1],
phi1[1] - phi0[1])
# TODO/FIXME: Actually, gradient should be zero because cost function
# is zero without observations! Use the mask!
#
# With observations
#
# Construct model
mu = np.random.randn(D)
Lambda = random.covariance(D)
X = Gaussian(mu, Lambda)
V = random.covariance(D)
Y = Gaussian(X, V)
Y.observe(np.random.randn(D))
# Random initialization
mu0 = np.random.randn(D)
Lambda0 = random.covariance(D)
X.initialize_from_parameters(mu0, Lambda0)
# Initial parameters
phi0 = X.phi
# Gradient
g = X.get_riemannian_gradient()
# Parameters after VB-EM update
X.update()
phi1 = X.phi
# Check
self.assertAllClose(g[0],
phi1[0] - phi0[0])
self.assertAllClose(g[1],
phi1[1] - phi0[1])
pass
