def setup(self):
"""
This method should be called just before optimization.
"""
# Get moments of X
(X, XnXn, XpXn) = self.X_node.get_moments()
# TODO/FIXME: Sum to plates of A/CovA
XpXp = XnXn[..., :-1, :, :]
#
# Expectations with respect to X
#
self.X0 = X[..., 0, :]
self.X0X0 = XnXn[..., 0, :, :]
# self.XnXn = np.sum(XnXn[...,1:,:,:], axis=-3)
self.XnXn = sum_to_plates(XnXn[..., 1:, :, :], (), plates_from=self.X_node.plates + (self.N - 1,), ndim=2)
# Get moments of the fixed parameter nodes
mu = self.X_node.parents[0].get_moments()[0]
self.Lambda = self.X_node.parents[1].get_moments()[0]
self.Lambda_mu_X0 = linalg.outer(np.einsum("...ik,...k->...i", self.Lambda, mu), self.X0)
self.Lambda_mu_X0 = sum_to_plates(self.Lambda_mu_X0, (), plates_from=self.X_node.plates, ndim=2)
#
# Prepare the rotation for A
#
(self.A_XpXn, self.A_XpXp_A, self.CovA_XpXp) = self._computations_for_A_and_X(XpXn, XpXp)
self.A_rotator.setup(plate_axis=-1)
def test_rotate_plates(self):
# Basic test for Gaussian vectors
X = GaussianARD(np.random.randn(3,2),
np.random.rand(3,2),
shape=(2,),
plates=(3,))
(u0, u1) = X.get_moments()
Cov = u1 - linalg.outer(u0, u0, ndim=1)
Q = np.random.randn(3,3)
Qu0 = np.einsum('ik,kj->ij', Q, u0)
QCov = np.einsum('k,kij->kij', np.sum(Q, axis=0)**2, Cov)
Qu1 = QCov + linalg.outer(Qu0, Qu0, ndim=1)
X.rotate_plates(Q, plate_axis=-1)
(u0, u1) = X.get_moments()
self.assertAllClose(u0, Qu0)
self.assertAllClose(u1, Qu1)
# Test full covariance, that is, with observations
X = GaussianARD(np.random.randn(3,2),
np.random.rand(3,2),
shape=(2,),
plates=(3,))
Y = Gaussian(X, [[2.0, 1.5], [1.5, 3.0]],
plates=(3,))
Y.observe(np.random.randn(3,2))
X.update()
(u0, u1) = X.get_moments()
Cov = u1 - linalg.outer(u0, u0, ndim=1)
Q = np.random.randn(3,3)
Qu0 = np.einsum('ik,kj->ij', Q, u0)
QCov = np.einsum('k,kij->kij', np.sum(Q, axis=0)**2, Cov)
Qu1 = QCov + linalg.outer(Qu0, Qu0, ndim=1)
X.rotate_plates(Q, plate_axis=-1)
(u0, u1) = X.get_moments()
self.assertAllClose(u0, Qu0)
self.assertAllClose(u1, Qu1)
pass
def test_initialization(self):
"""
Test initialization methods of GaussianARD
"""
X = GaussianARD(1, 2, shape=(2, ), plates=(3, ))
# Prior initialization
mu = 1 * np.ones((3, 2))
alpha = 2 * np.ones((3, 2))
X.initialize_from_prior()
u = X._message_to_child()
self.assertAllClose(u[0] * np.ones((3, 2)), mu)
self.assertAllClose(
u[1] * np.ones((3, 2, 2)),
linalg.outer(mu, mu, ndim=1) + misc.diag(1 / alpha, ndim=1))
# Parameter initialization
mu = np.random.randn(3, 2)
alpha = np.random.rand(3, 2)
X.initialize_from_parameters(mu, alpha)
u = X._message_to_child()
self.assertAllClose(u[0], mu)
self.assertAllClose(
u[1],
linalg.outer(mu, mu, ndim=1) + misc.diag(1 / alpha, ndim=1))
# Value initialization
x = np.random.randn(3, 2)
X.initialize_from_value(x)
u = X._message_to_child()
self.assertAllClose(u[0], x)
self.assertAllClose(u[1], linalg.outer(x, x, ndim=1))
# Random initialization
X.initialize_from_random()
pass
def test_initialization(self):
"""
Test initialization methods of GaussianARD
"""
X = GaussianARD(1, 2, shape=(2,), plates=(3,))
# Prior initialization
mu = 1 * np.ones((3, 2))
alpha = 2 * np.ones((3, 2))
X.initialize_from_prior()
u = X._message_to_child()
self.assertAllClose(u[0]*np.ones((3,2)),
mu)
self.assertAllClose(u[1]*np.ones((3,2,2)),
linalg.outer(mu, mu, ndim=1) +
misc.diag(1/alpha, ndim=1))
# Parameter initialization
mu = np.random.randn(3, 2)
alpha = np.random.rand(3, 2)
X.initialize_from_parameters(mu, alpha)
u = X._message_to_child()
self.assertAllClose(u[0], mu)
self.assertAllClose(u[1], linalg.outer(mu, mu, ndim=1) +
misc.diag(1/alpha, ndim=1))
# Value initialization
x = np.random.randn(3, 2)
X.initialize_from_value(x)
u = X._message_to_child()
self.assertAllClose(u[0], x)
self.assertAllClose(u[1], linalg.outer(x, x, ndim=1))
# Random initialization
X.initialize_from_random()
pass
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]
def _compute_moments(self, *u_nodes):
x = misc.concatenate(*[u[0] for u in u_nodes], axis=-1)
xx = misc.block_diag(*[u[1] for u in u_nodes])
# Explicitly broadcast xx to plates of x
x_plates = np.shape(x)[:-1]
xx = np.ones(x_plates)[..., None, None] * xx
# Compute the cross-covariance terms using the means of each variable
# (because covariances are zero for factorized nodes in the VB
# approximation)
i_start = 0
for m in range(len(u_nodes)):
i_end = i_start + np.shape(u_nodes[m][0])[-1]
j_start = 0
for n in range(m):
j_end = j_start + np.shape(u_nodes[n][0])[-1]
xm_xn = linalg.outer(u_nodes[m][0], u_nodes[n][0], ndim=1)
xx[..., i_start:i_end, j_start:j_end] = xm_xn
xx[..., j_start:j_end, i_start:i_end] = misc.T(xm_xn)
j_start = j_end
i_start = i_end
return [x, xx]
def _compute_moments(self, *u_nodes):
x = misc.concatenate(*[u[0] for u in u_nodes], axis=-1)
xx = misc.block_diag(*[u[1] for u in u_nodes])
# Explicitly broadcast xx to plates of x
x_plates = np.shape(x)[:-1]
xx = np.ones(x_plates)[...,None,None] * xx
# Compute the cross-covariance terms using the means of each variable
# (because covariances are zero for factorized nodes in the VB
# approximation)
i_start = 0
for m in range(len(u_nodes)):
i_end = i_start + np.shape(u_nodes[m][0])[-1]
j_start = 0
for n in range(m):
j_end = j_start + np.shape(u_nodes[n][0])[-1]
xm_xn = linalg.outer(u_nodes[m][0], u_nodes[n][0], ndim=1)
xx[...,i_start:i_end,j_start:j_end] = xm_xn
xx[...,j_start:j_end,i_start:i_end] = misc.T(xm_xn)
j_start = j_end
i_start = i_end
return [x, xx]
def test_message_to_child(self):
"""
Test the message from SumMultiply to its children.
"""
def compare_moments(u0, u1, *args):
Y = SumMultiply(*args)
u_Y = Y.get_moments()
self.assertAllClose(u_Y[0], u0)
self.assertAllClose(u_Y[1], u1)
# Test constant parent
y = np.random.randn(2,3,4)
compare_moments(y,
linalg.outer(y, y, ndim=2),
'ij->ij',
y)
# Do nothing for 2-D array
Y = GaussianARD(np.random.randn(5,2,3),
np.random.rand(5,2,3),
plates=(5,),
shape=(2,3))
y = Y.get_moments()
compare_moments(y[0],
y[1],
'ij->ij',
Y)
compare_moments(y[0],
y[1],
Y,
[0,1],
[0,1])
# Sum over the rows of a matrix
Y = GaussianARD(np.random.randn(5,2,3),
np.random.rand(5,2,3),
plates=(5,),
shape=(2,3))
y = Y.get_moments()
mu = np.einsum('...ij->...j', y[0])
cov = np.einsum('...ijkl->...jl', y[1])
compare_moments(mu,
cov,
'ij->j',
Y)
compare_moments(mu,
cov,
Y,
[0,1],
[1])
# Inner product of three vectors
X1 = GaussianARD(np.random.randn(2),
np.random.rand(2),
plates=(),
shape=(2,))
x1 = X1.get_moments()
X2 = GaussianARD(np.random.randn(6,1,2),
np.random.rand(6,1,2),
plates=(6,1),
shape=(2,))
x2 = X2.get_moments()
X3 = GaussianARD(np.random.randn(7,6,5,2),
np.random.rand(7,6,5,2),
plates=(7,6,5),
shape=(2,))
x3 = X3.get_moments()
mu = np.einsum('...i,...i,...i->...', x1[0], x2[0], x3[0])
cov = np.einsum('...ij,...ij,...ij->...', x1[1], x2[1], x3[1])
compare_moments(mu,
cov,
'i,i,i',
X1,
X2,
X3)
compare_moments(mu,
cov,
'i,i,i->',
X1,
X2,
X3)
compare_moments(mu,
cov,
X1,
[9],
X2,
[9],
X3,
[9])
compare_moments(mu,
cov,
X1,
[9],
X2,
[9],
X3,
[9],
[])
# Outer product of two vectors
X1 = GaussianARD(np.random.randn(2),
np.random.rand(2),
plates=(5,),
shape=(2,))
x1 = X1.get_moments()
X2 = GaussianARD(np.random.randn(6,1,2),
np.random.rand(6,1,2),
plates=(6,1),
shape=(2,))
x2 = X2.get_moments()
mu = np.einsum('...i,...j->...ij', x1[0], x2[0])
cov = np.einsum('...ik,...jl->...ijkl', x1[1], x2[1])
compare_moments(mu,
cov,
'i,j->ij',
X1,
X2)
compare_moments(mu,
cov,
X1,
[9],
X2,
[7],
[9,7])
# Matrix product
Y1 = GaussianARD(np.random.randn(3,2),
np.random.rand(3,2),
plates=(),
shape=(3,2))
y1 = Y1.get_moments()
Y2 = GaussianARD(np.random.randn(5,2,3),
np.random.rand(5,2,3),
plates=(5,),
shape=(2,3))
y2 = Y2.get_moments()
mu = np.einsum('...ik,...kj->...ij', y1[0], y2[0])
cov = np.einsum('...ikjl,...kmln->...imjn', y1[1], y2[1])
compare_moments(mu,
cov,
'ik,kj->ij',
Y1,
Y2)
compare_moments(mu,
cov,
Y1,
['i','k'],
Y2,
['k','j'],
['i','j'])
# Trace of a matrix product
Y1 = GaussianARD(np.random.randn(3,2),
np.random.rand(3,2),
plates=(),
shape=(3,2))
y1 = Y1.get_moments()
Y2 = GaussianARD(np.random.randn(5,2,3),
np.random.rand(5,2,3),
plates=(5,),
shape=(2,3))
y2 = Y2.get_moments()
mu = np.einsum('...ij,...ji->...', y1[0], y2[0])
cov = np.einsum('...ikjl,...kilj->...', y1[1], y2[1])
compare_moments(mu,
cov,
'ij,ji',
Y1,
Y2)
compare_moments(mu,
cov,
'ij,ji->',
Y1,
Y2)
compare_moments(mu,
cov,
Y1,
['i','j'],
Y2,
['j','i'])
compare_moments(mu,
cov,
Y1,
['i','j'],
Y2,
['j','i'],
[])
# Vector-matrix-vector product
X1 = GaussianARD(np.random.randn(3),
np.random.rand(3),
plates=(),
shape=(3,))
x1 = X1.get_moments()
X2 = GaussianARD(np.random.randn(6,1,2),
np.random.rand(6,1,2),
plates=(6,1),
shape=(2,))
x2 = X2.get_moments()
Y = GaussianARD(np.random.randn(3,2),
np.random.rand(3,2),
plates=(),
shape=(3,2))
y = Y.get_moments()
mu = np.einsum('...i,...ij,...j->...', x1[0], y[0], x2[0])
cov = np.einsum('...ia,...ijab,...jb->...', x1[1], y[1], x2[1])
compare_moments(mu,
cov,
'i,ij,j',
X1,
Y,
X2)
compare_moments(mu,
cov,
X1,
[1],
Y,
[1,2],
X2,
[2])
# Complex sum-product of 0-D, 1-D, 2-D and 3-D arrays
V = GaussianARD(np.random.randn(7,6,5),
np.random.rand(7,6,5),
plates=(7,6,5),
shape=())
v = V.get_moments()
X = GaussianARD(np.random.randn(6,1,2),
np.random.rand(6,1,2),
plates=(6,1),
shape=(2,))
x = X.get_moments()
Y = GaussianARD(np.random.randn(3,4),
np.random.rand(3,4),
plates=(5,),
shape=(3,4))
y = Y.get_moments()
Z = GaussianARD(np.random.randn(4,2,3),
np.random.rand(4,2,3),
plates=(6,5),
shape=(4,2,3))
z = Z.get_moments()
mu = np.einsum('...,...i,...kj,...jik->...k', v[0], x[0], y[0], z[0])
cov = np.einsum('...,...ia,...kjcb,...jikbac->...kc', v[1], x[1], y[1], z[1])
compare_moments(mu,
cov,
',i,kj,jik->k',
V,
X,
Y,
Z)
compare_moments(mu,
cov,
V,
[],
X,
['i'],
Y,
['k','j'],
Z,
['j','i','k'],
['k'])
#
# Gaussian-gamma parents
#
# Outer product of vectors
X1 = GaussianARD(np.random.randn(2),
np.random.rand(2),
shape=(2,))
x1 = X1.get_moments()
X2 = GaussianGamma(
np.random.randn(6,1,2),
random.covariance(2),
np.random.rand(6,1),
np.random.rand(6,1),
plates=(6,1)
)
x2 = X2.get_moments()
Y = SumMultiply('i,j->ij', X1, X2)
u = Y._message_to_child()
y = np.einsum('...i,...j->...ij', x1[0], x2[0])
yy = np.einsum('...ik,...jl->...ijkl', x1[1], x2[1])
self.assertAllClose(u[0], y)
self.assertAllClose(u[1], yy)
self.assertAllClose(u[2], x2[2])
self.assertAllClose(u[3], x2[3])
pass
def test_message_to_child(self):
"""
Test the message to child of GaussianGammaISO node.
"""
# Simple test
mu = np.array([1, 2, 3])
Lambda = np.identity(3)
a = 2
b = 10
X_alpha = GaussianGammaISO(mu, Lambda, a, b)
u = X_alpha._message_to_child()
self.assertEqual(len(u), 4)
tau = np.array(a / b)
self.assertAllClose(u[0], tau[..., None] * mu)
self.assertAllClose(
u[1],
(linalg.inv(Lambda) + tau[..., None, None] * linalg.outer(mu, mu)))
self.assertAllClose(u[2], tau)
self.assertAllClose(u[3], -np.log(b) + special.psi(a))
# Test with unknown parents
mu = Gaussian(np.arange(3), 10 * np.identity(3))
Lambda = Wishart(10, np.identity(3))
a = 2
b = Gamma(3, 15)
X_alpha = GaussianGammaISO(mu, Lambda, a, b)
u = X_alpha._message_to_child()
(mu, mumu) = mu._message_to_child()
Cov_mu = mumu - linalg.outer(mu, mu)
(Lambda, _) = Lambda._message_to_child()
(b, _) = b._message_to_child()
(tau, logtau) = Gamma(
a, b + 0.5 * np.sum(Lambda * Cov_mu))._message_to_child()
self.assertAllClose(u[0], tau[..., None] * mu)
self.assertAllClose(
u[1],
(linalg.inv(Lambda) + tau[..., None, None] * linalg.outer(mu, mu)))
self.assertAllClose(u[2], tau)
self.assertAllClose(u[3], logtau)
# Test with plates
mu = Gaussian(np.reshape(np.arange(3 * 4), (4, 3)),
10 * np.identity(3),
plates=(4, ))
Lambda = Wishart(10, np.identity(3))
a = 2
b = Gamma(3, 15)
X_alpha = GaussianGammaISO(mu, Lambda, a, b, plates=(4, ))
u = X_alpha._message_to_child()
(mu, mumu) = mu._message_to_child()
Cov_mu = mumu - linalg.outer(mu, mu)
(Lambda, _) = Lambda._message_to_child()
(b, _) = b._message_to_child()
(tau, logtau) = Gamma(
a, b +
0.5 * np.sum(Lambda * Cov_mu, axis=(-1, -2)))._message_to_child()
self.assertAllClose(u[0] * np.ones((4, 1)),
np.ones((4, 1)) * tau[..., None] * mu)
self.assertAllClose(
u[1] * np.ones((4, 1, 1)),
np.ones((4, 1, 1)) *
(linalg.inv(Lambda) + tau[..., None, None] * linalg.outer(mu, mu)))
self.assertAllClose(u[2] * np.ones(4), np.ones(4) * tau)
self.assertAllClose(u[3] * np.ones(4), np.ones(4) * logtau)
pass
def test_message_to_child(self):
"""
Test the message to child of GaussianGamma node.
"""
# Simple test
mu = np.array([1,2,3])
Lambda = np.identity(3)
a = 2
b = 10
X_alpha = GaussianGamma(mu, Lambda, a, b)
u = X_alpha._message_to_child()
self.assertEqual(len(u), 4)
tau = np.array(a/b)
self.assertAllClose(u[0],
tau[...,None] * mu)
self.assertAllClose(u[1],
(linalg.inv(Lambda)
+ tau[...,None,None] * linalg.outer(mu, mu)))
self.assertAllClose(u[2],
tau)
self.assertAllClose(u[3],
-np.log(b) + special.psi(a))
# Test with unknown parents
mu = Gaussian(np.arange(3), 10*np.identity(3))
Lambda = Wishart(10, np.identity(3))
a = 2
b = Gamma(3, 15)
X_alpha = GaussianGamma(mu, Lambda, a, b)
u = X_alpha._message_to_child()
(mu, mumu) = mu._message_to_child()
Cov_mu = mumu - linalg.outer(mu, mu)
(Lambda, _) = Lambda._message_to_child()
(b, _) = b._message_to_child()
(tau, logtau) = Gamma(a, b + 0.5*np.sum(Lambda*Cov_mu))._message_to_child()
self.assertAllClose(u[0],
tau[...,None] * mu)
self.assertAllClose(u[1],
(linalg.inv(Lambda)
+ tau[...,None,None] * linalg.outer(mu, mu)))
self.assertAllClose(u[2],
tau)
self.assertAllClose(u[3],
logtau)
# Test with plates
mu = Gaussian(np.reshape(np.arange(3*4), (4,3)),
10*np.identity(3),
plates=(4,))
Lambda = Wishart(10, np.identity(3))
a = 2
b = Gamma(3, 15)
X_alpha = GaussianGamma(mu, Lambda, a, b, plates=(4,))
u = X_alpha._message_to_child()
(mu, mumu) = mu._message_to_child()
Cov_mu = mumu - linalg.outer(mu, mu)
(Lambda, _) = Lambda._message_to_child()
(b, _) = b._message_to_child()
(tau, logtau) = Gamma(a,
b + 0.5*np.sum(Lambda*Cov_mu,
axis=(-1,-2)))._message_to_child()
self.assertAllClose(u[0] * np.ones((4,1)),
np.ones((4,1)) * tau[...,None] * mu)
self.assertAllClose(u[1] * np.ones((4,1,1)),
np.ones((4,1,1)) * (linalg.inv(Lambda)
+ tau[...,None,None] * linalg.outer(mu, mu)))
self.assertAllClose(u[2] * np.ones(4),
np.ones(4) * tau)
self.assertAllClose(u[3] * np.ones(4),
np.ones(4) * logtau)
pass
def covariance(self):
(x, xx) = self.get()[:2]
Cov = xx - linalg.outer(x, x, ndim=1)
return Cov
def test_message_to_child(self):
"""
Test the message from SumMultiply to its children.
"""
def compare_moments(u0, u1, *args):
Y = SumMultiply(*args)
u_Y = Y.get_moments()
self.assertAllClose(u_Y[0], u0)
self.assertAllClose(u_Y[1], u1)
# Test constant parent
y = np.random.randn(2, 3, 4)
compare_moments(y, linalg.outer(y, y, ndim=2), 'ij->ij', y)
# Do nothing for 2-D array
Y = GaussianARD(np.random.randn(5, 2, 3),
np.random.rand(5, 2, 3),
plates=(5, ),
shape=(2, 3))
y = Y.get_moments()
compare_moments(y[0], y[1], 'ij->ij', Y)
compare_moments(y[0], y[1], Y, [0, 1], [0, 1])
# Sum over the rows of a matrix
Y = GaussianARD(np.random.randn(5, 2, 3),
np.random.rand(5, 2, 3),
plates=(5, ),
shape=(2, 3))
y = Y.get_moments()
mu = np.einsum('...ij->...j', y[0])
cov = np.einsum('...ijkl->...jl', y[1])
compare_moments(mu, cov, 'ij->j', Y)
compare_moments(mu, cov, Y, [0, 1], [1])
# Inner product of three vectors
X1 = GaussianARD(np.random.randn(2),
np.random.rand(2),
plates=(),
shape=(2, ))
x1 = X1.get_moments()
X2 = GaussianARD(np.random.randn(6, 1, 2),
np.random.rand(6, 1, 2),
plates=(6, 1),
shape=(2, ))
x2 = X2.get_moments()
X3 = GaussianARD(np.random.randn(7, 6, 5, 2),
np.random.rand(7, 6, 5, 2),
plates=(7, 6, 5),
shape=(2, ))
x3 = X3.get_moments()
mu = np.einsum('...i,...i,...i->...', x1[0], x2[0], x3[0])
cov = np.einsum('...ij,...ij,...ij->...', x1[1], x2[1], x3[1])
compare_moments(mu, cov, 'i,i,i', X1, X2, X3)
compare_moments(mu, cov, 'i,i,i->', X1, X2, X3)
compare_moments(mu, cov, X1, [9], X2, [9], X3, [9])
compare_moments(mu, cov, X1, [9], X2, [9], X3, [9], [])
# Outer product of two vectors
X1 = GaussianARD(np.random.randn(2),
np.random.rand(2),
plates=(5, ),
shape=(2, ))
x1 = X1.get_moments()
X2 = GaussianARD(np.random.randn(6, 1, 2),
np.random.rand(6, 1, 2),
plates=(6, 1),
shape=(2, ))
x2 = X2.get_moments()
mu = np.einsum('...i,...j->...ij', x1[0], x2[0])
cov = np.einsum('...ik,...jl->...ijkl', x1[1], x2[1])
compare_moments(mu, cov, 'i,j->ij', X1, X2)
compare_moments(mu, cov, X1, [9], X2, [7], [9, 7])
# Matrix product
Y1 = GaussianARD(np.random.randn(3, 2),
np.random.rand(3, 2),
plates=(),
shape=(3, 2))
y1 = Y1.get_moments()
Y2 = GaussianARD(np.random.randn(5, 2, 3),
np.random.rand(5, 2, 3),
plates=(5, ),
shape=(2, 3))
y2 = Y2.get_moments()
mu = np.einsum('...ik,...kj->...ij', y1[0], y2[0])
cov = np.einsum('...ikjl,...kmln->...imjn', y1[1], y2[1])
compare_moments(mu, cov, 'ik,kj->ij', Y1, Y2)
compare_moments(mu, cov, Y1, ['i', 'k'], Y2, ['k', 'j'], ['i', 'j'])
# Trace of a matrix product
Y1 = GaussianARD(np.random.randn(3, 2),
np.random.rand(3, 2),
plates=(),
shape=(3, 2))
y1 = Y1.get_moments()
Y2 = GaussianARD(np.random.randn(5, 2, 3),
np.random.rand(5, 2, 3),
plates=(5, ),
shape=(2, 3))
y2 = Y2.get_moments()
mu = np.einsum('...ij,...ji->...', y1[0], y2[0])
cov = np.einsum('...ikjl,...kilj->...', y1[1], y2[1])
compare_moments(mu, cov, 'ij,ji', Y1, Y2)
compare_moments(mu, cov, 'ij,ji->', Y1, Y2)
compare_moments(mu, cov, Y1, ['i', 'j'], Y2, ['j', 'i'])
compare_moments(mu, cov, Y1, ['i', 'j'], Y2, ['j', 'i'], [])
# Vector-matrix-vector product
X1 = GaussianARD(np.random.randn(3),
np.random.rand(3),
plates=(),
shape=(3, ))
x1 = X1.get_moments()
X2 = GaussianARD(np.random.randn(6, 1, 2),
np.random.rand(6, 1, 2),
plates=(6, 1),
shape=(2, ))
x2 = X2.get_moments()
Y = GaussianARD(np.random.randn(3, 2),
np.random.rand(3, 2),
plates=(),
shape=(3, 2))
y = Y.get_moments()
mu = np.einsum('...i,...ij,...j->...', x1[0], y[0], x2[0])
cov = np.einsum('...ia,...ijab,...jb->...', x1[1], y[1], x2[1])
compare_moments(mu, cov, 'i,ij,j', X1, Y, X2)
compare_moments(mu, cov, X1, [1], Y, [1, 2], X2, [2])
# Complex sum-product of 0-D, 1-D, 2-D and 3-D arrays
V = GaussianARD(np.random.randn(7, 6, 5),
np.random.rand(7, 6, 5),
plates=(7, 6, 5),
shape=())
v = V.get_moments()
X = GaussianARD(np.random.randn(6, 1, 2),
np.random.rand(6, 1, 2),
plates=(6, 1),
shape=(2, ))
x = X.get_moments()
Y = GaussianARD(np.random.randn(3, 4),
np.random.rand(3, 4),
plates=(5, ),
shape=(3, 4))
y = Y.get_moments()
Z = GaussianARD(np.random.randn(4, 2, 3),
np.random.rand(4, 2, 3),
plates=(6, 5),
shape=(4, 2, 3))
z = Z.get_moments()
mu = np.einsum('...,...i,...kj,...jik->...k', v[0], x[0], y[0], z[0])
cov = np.einsum('...,...ia,...kjcb,...jikbac->...kc', v[1], x[1], y[1],
z[1])
compare_moments(mu, cov, ',i,kj,jik->k', V, X, Y, Z)
compare_moments(mu, cov, V, [], X, ['i'], Y, ['k', 'j'], Z,
['j', 'i', 'k'], ['k'])
# Test with constant nodes
N = 10
D = 5
a = np.random.randn(N, D)
B = Gaussian(
np.random.randn(D),
random.covariance(D),
)
X = SumMultiply('i,i->', B, a)
np.testing.assert_allclose(
X.get_moments()[0],
np.einsum('ni,i->n', a,
B.get_moments()[0]),
)
np.testing.assert_allclose(
X.get_moments()[1],
np.einsum('ni,nj,ij->n', a, a,
B.get_moments()[1]),
)
#
# Gaussian-gamma parents
#
# Outer product of vectors
X1 = GaussianARD(np.random.randn(2), np.random.rand(2), shape=(2, ))
x1 = X1.get_moments()
X2 = GaussianGamma(np.random.randn(6, 1, 2),
random.covariance(2),
np.random.rand(6, 1),
np.random.rand(6, 1),
plates=(6, 1))
x2 = X2.get_moments()
Y = SumMultiply('i,j->ij', X1, X2)
u = Y._message_to_child()
y = np.einsum('...i,...j->...ij', x1[0], x2[0])
yy = np.einsum('...ik,...jl->...ijkl', x1[1], x2[1])
self.assertAllClose(u[0], y)
self.assertAllClose(u[1], yy)
self.assertAllClose(u[2], x2[2])
self.assertAllClose(u[3], x2[3])
# Test with constant nodes
N = 10
M = 8
D = 5
a = np.random.randn(N, 1, D)
B = GaussianGamma(
np.random.randn(M, D),
random.covariance(D, size=(M, )),
np.random.rand(M),
np.random.rand(M),
ndim=1,
)
X = SumMultiply('i,i->', B, a)
np.testing.assert_allclose(
X.get_moments()[0],
np.einsum('nmi,mi->nm', a,
B.get_moments()[0]),
)
np.testing.assert_allclose(
X.get_moments()[1],
np.einsum('nmi,nmj,mij->nm', a, a,
B.get_moments()[1]),
)
np.testing.assert_allclose(
X.get_moments()[2],
B.get_moments()[2],
)
np.testing.assert_allclose(
X.get_moments()[3],
B.get_moments()[3],
)
pass
def _compute_bound(self, R, logdet=None, inv=None, Q=None, gradient=False, terms=False):
"""
Rotate q(X) and q(alpha).
Assume:
p(X|alpha) = prod_m N(x_m|0,diag(alpha))
p(alpha) = prod_d G(a_d,b_d)
"""
## R = self._full_rotation_matrix(R)
## if inv is not None:
## inv = self._full_rotation_matrix(inv)
#
# Transform the distributions and moments
#
plates_alpha = self.plates_alpha
plates_X = self.plates_X
# Compute rotated second moment
if self.plate_axis is not None:
# The plate axis has been moved to be the last plate axis
if Q is None:
raise ValueError("Plates should be rotated but no Q give")
# Transform covariance
sumQ = np.sum(Q, axis=0)
QCovQ = sumQ[:, None, None] ** 2 * self.CovX
# Rotate plates
if self.precompute:
QX_QX = np.einsum("...kalb,...ik,...il->...iab", self.X_X, Q, Q)
XX = QX_QX + QCovQ
XX = sum_to_plates(XX, plates_alpha[:-1], ndim=2)
Xmu = np.einsum("...kaib,...ik->...iab", self.X_mu, Q)
Xmu = sum_to_plates(Xmu, plates_alpha[:-1], ndim=2)
else:
X = self.X
mu = self.mu
QX = np.einsum("...ik,...kj->...ij", Q, X)
XX = sum_to_plates(QCovQ, plates_alpha[:-1], ndim=2) + sum_to_plates(
linalg.outer(QX, QX), plates_alpha[:-1], ndim=2, plates_from=plates_X
)
Xmu = sum_to_plates(linalg.outer(QX, self.mu), plates_alpha[:-1], ndim=2, plates_from=plates_X)
mu2 = self.mu2
D = np.shape(XX)[-1]
logdet_Q = D * np.log(np.abs(sumQ))
else:
XX = self.XX
mu2 = self.mu2
Xmu = self.Xmu
logdet_Q = 0
# Compute transformed moments
# mu2 = np.einsum('...ii->...i', mu2)
RXmu = np.einsum("...ik,...ki->...i", R, Xmu)
RXX = np.einsum("...ik,...kj->...ij", R, XX)
RXXR = np.einsum("...ik,...ik->...i", RXX, R)
# <(X-mu) * (X-mu)'>_R
XmuXmu = RXXR - 2 * RXmu + mu2
D = np.shape(R)[0]
# Compute q(alpha)
if self.update_alpha:
# Parameters
a0 = self.a0
b0 = self.b0
a = self.a
b = b0 + 0.5 * sum_to_plates(XmuXmu, plates_alpha, plates_from=None, ndim=0)
# Some expectations
alpha = a / b
logb = np.log(b)
logalpha = -logb # + const
b0_alpha = b0 * alpha
a0_logalpha = a0 * logalpha
else:
alpha = self.alpha
logalpha = 0
#
# Compute the cost
#
def sum_plates(V, *plates):
full_plates = misc.broadcasted_shape(*plates)
r = self.node_X.broadcasting_multiplier(full_plates, np.shape(V))
return r * np.sum(V)
XmuXmu_alpha = XmuXmu * alpha
if logdet is None:
logdet_R = np.linalg.slogdet(R)[1]
inv_R = np.linalg.inv(R)
else:
logdet_R = logdet
inv_R = inv
# Compute entropy H(X)
logH_X = random.gaussian_entropy(-2 * sum_plates(logdet_R + logdet_Q, plates_X), 0)
# Compute <log p(X|alpha)>
logp_X = random.gaussian_logpdf(
sum_plates(XmuXmu_alpha, plates_alpha[:-1] + [D]), 0, 0, sum_plates(logalpha, plates_X + [D]), 0
)
if self.update_alpha:
# Compute entropy H(alpha)
# This cancels out with the log(alpha) term in log(p(alpha))
logH_alpha = 0
# Compute <log p(alpha)>
logp_alpha = random.gamma_logpdf(
sum_plates(b0_alpha, plates_alpha), 0, sum_plates(a0_logalpha, plates_alpha), 0, 0
)
else:
logH_alpha = 0
logp_alpha = 0
# Compute the bound
if terms:
bound = {self.node_X: logp_X + logH_X}
if self.update_alpha:
bound.update({self.node_alpha: logp_alpha + logH_alpha})
else:
bound = 0 + logp_X + logp_alpha + logH_X + logH_alpha
if not gradient:
return bound
#
# Compute the gradient with respect R
#
broadcasting_multiplier = self.node_X.broadcasting_multiplier
def sum_plates(V, plates):
ones = np.ones(np.shape(R))
r = broadcasting_multiplier(plates, np.shape(V)[:-2])
return r * misc.sum_multiply(V, ones, axis=(-1, -2), sumaxis=False, keepdims=False)
D_XmuXmu = 2 * RXX - 2 * gaussian.transpose_covariance(Xmu)
DXmuXmu_alpha = np.einsum("...i,...ij->...ij", alpha, D_XmuXmu)
if self.update_alpha:
D_b = 0.5 * D_XmuXmu
XmuXmu_Dalpha = np.einsum(
"...i,...i,...i,...ij->...ij",
sum_to_plates(XmuXmu, plates_alpha, plates_from=None, ndim=0),
alpha,
-1 / b,
D_b,
)
D_b0_alpha = np.einsum("...i,...i,...i,...ij->...ij", b0, alpha, -1 / b, D_b)
D_logb = np.einsum("...i,...ij->...ij", 1 / b, D_b)
D_logalpha = -D_logb
D_a0_logalpha = a0 * D_logalpha
else:
XmuXmu_Dalpha = 0
D_logalpha = 0
D_XmuXmu_alpha = DXmuXmu_alpha + XmuXmu_Dalpha
D_logR = inv_R.T
# Compute dH(X)
dlogH_X = random.gaussian_entropy(-2 * sum_plates(D_logR, plates_X), 0)
# Compute d<log p(X|alpha)>
dlogp_X = random.gaussian_logpdf(
sum_plates(D_XmuXmu_alpha, plates_alpha[:-1]),
0,
0,
(sum_plates(D_logalpha, plates_X) * broadcasting_multiplier((D,), plates_alpha[-1:])),
0,
)
if self.update_alpha:
# Compute dH(alpha)
# This cancels out with the log(alpha) term in log(p(alpha))
dlogH_alpha = 0
# Compute d<log p(alpha)>
dlogp_alpha = random.gamma_logpdf(
sum_plates(D_b0_alpha, plates_alpha[:-1]), 0, sum_plates(D_a0_logalpha, plates_alpha[:-1]), 0, 0
)
else:
dlogH_alpha = 0
dlogp_alpha = 0
if terms:
raise NotImplementedError()
dR_bound = {self.node_X: dlogp_X + dlogH_X}
if self.update_alpha:
dR_bound.update({self.node_alpha: dlogp_alpha + dlogH_alpha})
else:
dR_bound = 0 * dlogp_X + dlogp_X + dlogp_alpha + dlogH_X + dlogH_alpha
if self.subset:
indices = np.ix_(self.subset, self.subset)
dR_bound = dR_bound[indices]
if self.plate_axis is None:
return (bound, dR_bound)
#
# Compute the gradient with respect to Q (if Q given)
#
# Some pre-computations
Q_RCovR = np.einsum("...ik,...kl,...il,...->...i", R, self.CovX, R, sumQ)
if self.precompute:
Xr_rX = np.einsum("...abcd,...jb,...jd->...jac", self.X_X, R, R)
QXr_rX = np.einsum("...akj,...ik->...aij", Xr_rX, Q)
RX_mu = np.einsum("...jk,...akbj->...jab", R, self.X_mu)
else:
RX = np.einsum("...ik,...k->...i", R, X)
QXR = np.einsum("...ik,...kj->...ij", Q, RX)
QXr_rX = np.einsum("...ik,...jk->...kij", QXR, RX)
RX_mu = np.einsum("...ik,...jk->...kij", RX, mu)
QXr_rX = sum_to_plates(QXr_rX, plates_alpha[:-2], ndim=3, plates_from=plates_X[:-1])
RX_mu = sum_to_plates(RX_mu, plates_alpha[:-2], ndim=3, plates_from=plates_X[:-1])
def psi(v):
"""
Compute: d/dQ 1/2*trace(diag(v)*<(X-mu)*(X-mu)>)
= Q*<X>'*R'*diag(v)*R*<X> + ones * Q diag( tr(R'*diag(v)*R*Cov) )
+ mu*diag(v)*R*<X>
"""
# Precompute all terms to plates_alpha because v has shape
# plates_alpha.
# Gradient of 0.5*v*<x>*<x>
v_QXrrX = np.einsum("...kij,...ik->...ij", QXr_rX, v)
# Gradient of 0.5*v*Cov
Q_tr_R_v_R_Cov = np.einsum("...k,...k->...", Q_RCovR, v)[..., None, :]
# Gradient of mu*v*x
mu_v_R_X = np.einsum("...ik,...kji->...ij", v, RX_mu)
return v_QXrrX + Q_tr_R_v_R_Cov - mu_v_R_X
def sum_plates(V, plates):
ones = np.ones(np.shape(Q))
r = self.node_X.broadcasting_multiplier(plates, np.shape(V)[:-2])
return r * misc.sum_multiply(V, ones, axis=(-1, -2), sumaxis=False, keepdims=False)
if self.update_alpha:
D_logb = psi(1 / b)
XX_Dalpha = -psi(alpha / b * sum_to_plates(XmuXmu, plates_alpha))
D_logalpha = -D_logb
else:
XX_Dalpha = 0
D_logalpha = 0
DXX_alpha = 2 * psi(alpha)
D_XX_alpha = DXX_alpha + XX_Dalpha
D_logdetQ = D / sumQ
N = np.shape(Q)[-1]
# Compute dH(X)
dQ_logHX = random.gaussian_entropy(-2 * sum_plates(D_logdetQ, plates_X[:-1]), 0)
# Compute d<log p(X|alpha)>
dQ_logpX = random.gaussian_logpdf(
sum_plates(D_XX_alpha, plates_alpha[:-2]),
0,
0,
(sum_plates(D_logalpha, plates_X[:-1]) * broadcasting_multiplier((N, D), plates_alpha[-2:])),
0,
)
if self.update_alpha:
D_alpha = -psi(alpha / b)
D_b0_alpha = b0 * D_alpha
D_a0_logalpha = a0 * D_logalpha
# Compute dH(alpha)
# This cancels out with the log(alpha) term in log(p(alpha))
dQ_logHalpha = 0
# Compute d<log p(alpha)>
dQ_logpalpha = random.gamma_logpdf(
sum_plates(D_b0_alpha, plates_alpha[:-2]), 0, sum_plates(D_a0_logalpha, plates_alpha[:-2]), 0, 0
)
else:
dQ_logHalpha = 0
dQ_logpalpha = 0
if terms:
raise NotImplementedError()
dQ_bound = {self.node_X: dQ_logpX + dQ_logHX}
if self.update_alpha:
dQ_bound.update({self.node_alpha: dQ_logpalpha + dQ_logHalpha})
else:
dQ_bound = 0 * dQ_logpX + dQ_logpX + dQ_logpalpha + dQ_logHX + dQ_logHalpha
return (bound, dR_bound, dQ_bound)
def setup(self, plate_axis=None):
"""
This method should be called just before optimization.
For efficiency, sum over axes that are not in mu, alpha nor rotation.
If using Q, set rotate_plates to True.
"""
# Store the original plate_axis parameter for later use in other methods
self.plate_axis = plate_axis
# Manipulate the plate_axis parameter to suit the needs of this method
if plate_axis is not None:
if not isinstance(plate_axis, int):
raise ValueError("Plate axis must be integer")
if plate_axis >= 0:
plate_axis -= len(self.node_X.plates)
if plate_axis < -len(self.node_X.plates) or plate_axis >= 0:
raise ValueError("Axis out of bounds")
plate_axis -= self.ndim - 1 # Why -1? Because one axis is preserved!
# Get the mean parameter. It will not be rotated. This assumes that mu
# and alpha are really independent.
(alpha_mu, alpha_mu2, alpha, _) = self.node_parent.get_moments()
(X, XX) = self.node_X.get_moments()
#
mu = alpha_mu / alpha
mu2 = alpha_mu2 / alpha
# For simplicity, force mu to have the same shape as X
mu = mu * np.ones(self.node_X.dims[0])
mu2 = mu2 * np.ones(self.node_X.dims[0])
## (mu, mumu) = gaussian.reshape_gaussian_array(self.node_mu.dims[0],
## self.node_X.dims[0],
## mu,
## mumu)
# Take diagonal of covariances to variances for axes that are not in R
# (and move those axes to be the last)
XX = covariance_to_variance(XX, ndim=self.ndim, covariance_axis=self.axis)
## mumu = covariance_to_variance(mumu,
## ndim=self.ndim,
## covariance_axis=self.axis)
# Move axes of X and mu and compute their outer product
X = misc.moveaxis(X, self.axis, -1)
mu = misc.moveaxis(mu, self.axis, -1)
mu2 = misc.moveaxis(mu2, self.axis, -1)
Xmu = linalg.outer(X, mu, ndim=1)
D = np.shape(X)[-1]
# Move axes of alpha related variables
def safe_move_axis(x):
if np.ndim(x) >= -self.axis:
return misc.moveaxis(x, self.axis, -1)
else:
return x[..., np.newaxis]
if self.update_alpha:
a = safe_move_axis(self.node_alpha.phi[1])
a0 = safe_move_axis(self.node_alpha.parents[0].get_moments()[0])
b0 = safe_move_axis(self.node_alpha.parents[1].get_moments()[0])
plates_alpha = list(self.node_alpha.plates)
else:
alpha = safe_move_axis(self.node_parent.get_moments()[2])
plates_alpha = list(self.node_parent.get_shape(2))
# Move plates of alpha for R
if len(plates_alpha) >= -self.axis:
plate = plates_alpha.pop(self.axis)
plates_alpha.append(plate)
else:
plates_alpha.append(1)
plates_X = list(self.node_X.get_shape(0))
plates_X.pop(self.axis)
def sum_to_alpha(V, ndim=2):
# TODO/FIXME: This could be improved so that it is not required to
# explicitly repeat to alpha plates. Multiplying by ones was just a
# simple bug fix.
return sum_to_plates(
V * np.ones(plates_alpha[:-1] + ndim * [1]), plates_alpha[:-1], ndim=ndim, plates_from=plates_X
)
if plate_axis is not None:
# Move plate axis just before the rotated dimensions (which are
# last)
def safe_move_plate_axis(x, ndim):
if np.ndim(x) - ndim >= -plate_axis:
return misc.moveaxis(x, plate_axis - ndim, -ndim - 1)
else:
inds = (Ellipsis, None) + ndim * (slice(None),)
return x[inds]
X = safe_move_plate_axis(X, 1)
mu = safe_move_plate_axis(mu, 1)
XX = safe_move_plate_axis(XX, 2)
mu2 = safe_move_plate_axis(mu2, 1)
if self.update_alpha:
a = safe_move_plate_axis(a, 1)
a0 = safe_move_plate_axis(a0, 1)
b0 = safe_move_plate_axis(b0, 1)
else:
alpha = safe_move_plate_axis(alpha, 1)
# Move plates of X and alpha
plate = plates_X.pop(plate_axis)
plates_X.append(plate)
if len(plates_alpha) >= -plate_axis + 1:
plate = plates_alpha.pop(plate_axis - 1)
else:
plate = 1
plates_alpha = plates_alpha[:-1] + [plate] + plates_alpha[-1:]
CovX = XX - linalg.outer(X, X)
self.CovX = sum_to_plates(CovX, plates_alpha[:-2], ndim=3, plates_from=plates_X[:-1])
# Broadcast mumu to ensure shape
# mumu = np.ones(np.shape(XX)[-3:]) * mumu
mu2 = mu2 * np.ones(np.shape(X)[-2:])
self.mu2 = sum_to_alpha(mu2, ndim=1)
if self.precompute:
# Precompute some stuff for the gradient of plate rotation
#
# NOTE: These terms may require a lot of memory if alpha has the
# same or almost the same plates as X.
self.X_X = sum_to_plates(
X[..., :, :, None, None] * X[..., None, None, :, :],
plates_alpha[:-2],
ndim=4,
plates_from=plates_X[:-1],
)
self.X_mu = sum_to_plates(
X[..., :, :, None, None] * mu[..., None, None, :, :],
plates_alpha[:-2],
ndim=4,
plates_from=plates_X[:-1],
)
else:
self.X = X
self.mu = mu
else:
# Sum axes that are not in the plates of alpha
self.XX = sum_to_alpha(XX)
self.mu2 = sum_to_alpha(mu2, ndim=1)
self.Xmu = sum_to_alpha(Xmu)
if self.update_alpha:
self.a = a
self.a0 = a0
self.b0 = b0
else:
self.alpha = alpha
self.plates_X = plates_X
self.plates_alpha = plates_alpha
# Take only a subset of the matrix for rotation
if self.subset is not None:
if self.precompute:
raise NotImplementedError("Precomputation not implemented when " "using a subset")
# from X
self.X = self.X[..., self.subset]
self.mu2 = self.mu2[..., self.subset]
if plate_axis is not None:
# from CovX
inds = []
for i in range(np.ndim(self.CovX) - 2):
inds.append(range(np.shape(self.CovX)[i]))
inds.append(self.subset)
inds.append(self.subset)
indices = np.ix_(*inds)
self.CovX = self.CovX[indices]
# from mu
self.mu = self.mu[..., self.subset]
else:
# from XX
inds = []
for i in range(np.ndim(self.XX) - 2):
inds.append(range(np.shape(self.XX)[i]))
inds.append(self.subset)
inds.append(self.subset)
indices = np.ix_(*inds)
self.XX = self.XX[indices]
# from Xmu
self.Xmu = self.Xmu[..., self.subset]
# from alpha
if self.update_alpha:
if np.shape(self.a)[-1] > 1:
self.a = self.a[..., self.subset]
if np.shape(self.a0)[-1] > 1:
self.a0 = self.a0[..., self.subset]
if np.shape(self.b0)[-1] > 1:
self.b0 = self.b0[..., self.subset]
else:
if np.shape(self.alpha)[-1] > 1:
self.alpha = self.alpha[..., self.subset]
self.plates_alpha[-1] = min(self.plates_alpha[-1], len(self.subset))
