def _expand_independent_outputs(fvar, full_cov, full_output_cov):
"""
Reshapes fvar to the correct shape, specified by `full_cov` and `full_output_cov`.
:param fvar: has shape N x P (full_cov = False) or P x N x N (full_cov = True).
:return:
1. full_cov: True and full_output_cov: True
fvar N x P x N x P
2. full_cov: True and full_output_cov: False
fvar P x N x N
3. full_cov: False and full_output_cov: True
fvar N x P x P
4. full_cov: False and full_output_cov: False
fvar N x P
"""
if full_cov and full_output_cov:
fvar = tf.matrix_diag(tf.transpose(fvar)) # N x N x P x P
fvar = tf.transpose(fvar, [0, 2, 1, 3]) # N x P x N x P
if not full_cov and full_output_cov:
fvar = tf.matrix_diag(fvar) # N x P x P
if full_cov and not full_output_cov:
pass # P x N x N
if not full_cov and not full_output_cov:
pass # N x P
return fvar
def _expectation(p, rbf_kern, feat1, lin_kern, feat2, nghp=None):
"""
Compute the expectation:
expectation[n] = <Ka_{Z1, x_n} Kb_{x_n, Z2}>_p(x_n)
- K_lin_{.,.} :: RBF kernel
- K_rbf_{.,.} :: Linear kernel
Different Z1 and Z2 are handled if p is diagonal and K_lin and K_rbf have disjoint
active_dims, in which case the joint expectations simplify into a product of expectations
:return: NxM1xM2
"""
if rbf_kern.on_separate_dims(lin_kern) and isinstance(p, DiagonalGaussian): # no joint expectations required
eKxz1 = expectation(p, (rbf_kern, feat1))
eKxz2 = expectation(p, (lin_kern, feat2))
return eKxz1[:, :, None] * eKxz2[:, None, :]
if feat1 != feat2:
raise NotImplementedError("Features have to be the same for both kernels.")
if rbf_kern.active_dims != lin_kern.active_dims:
raise NotImplementedError("active_dims have to be the same for both kernels.")
with params_as_tensors_for(rbf_kern), params_as_tensors_for(lin_kern), \
params_as_tensors_for(feat1), params_as_tensors_for(feat2):
# use only active dimensions
Xcov = rbf_kern._slice_cov(tf.matrix_diag(p.cov) if isinstance(p, DiagonalGaussian) else p.cov)
Z, Xmu = rbf_kern._slice(feat1.Z, p.mu)
N = tf.shape(Xmu)[0]
D = tf.shape(Xmu)[1]
lin_kern_variances = lin_kern.variance if lin_kern.ARD \
else tf.zeros((D,), dtype=settings.tf_float) + lin_kern.variance
rbf_kern_lengthscales = rbf_kern.lengthscales if rbf_kern.ARD \
else tf.zeros((D,), dtype=settings.tf_float) + rbf_kern.lengthscales ## Begin RBF eKxz code:
chol_L_plus_Xcov = tf.cholesky(tf.matrix_diag(rbf_kern_lengthscales ** 2) + Xcov) # NxDxD
Z_transpose = tf.transpose(Z)
all_diffs = Z_transpose - tf.expand_dims(Xmu, 2) # NxDxM
exponent_mahalanobis = tf.matrix_triangular_solve(chol_L_plus_Xcov, all_diffs, lower=True) # NxDxM
exponent_mahalanobis = tf.reduce_sum(tf.square(exponent_mahalanobis), 1) # NxM
exponent_mahalanobis = tf.exp(-0.5 * exponent_mahalanobis) # NxM
sqrt_det_L = tf.reduce_prod(rbf_kern_lengthscales)
sqrt_det_L_plus_Xcov = tf.exp(tf.reduce_sum(tf.log(tf.matrix_diag_part(chol_L_plus_Xcov)), axis=1))
determinants = sqrt_det_L / sqrt_det_L_plus_Xcov # N
eKxz_rbf = rbf_kern.variance * (determinants[:, None] * exponent_mahalanobis) ## NxM <- End RBF eKxz code
tiled_Z = tf.tile(tf.expand_dims(Z_transpose, 0), (N, 1, 1)) # NxDxM
z_L_inv_Xcov = tf.matmul(tiled_Z, Xcov / rbf_kern_lengthscales[:, None] ** 2., transpose_a=True) # NxMxD
cross_eKzxKxz = tf.cholesky_solve(
chol_L_plus_Xcov, (lin_kern_variances * rbf_kern_lengthscales ** 2.)[..., None] * tiled_Z) # NxDxM
cross_eKzxKxz = tf.matmul((z_L_inv_Xcov + Xmu[:, None, :]) * eKxz_rbf[..., None], cross_eKzxKxz) # NxMxM
return cross_eKzxKxz
def _expectation(p, kern1, feat1, kern2, feat2, nghp=None):
"""
Compute the expectation:
expectation[n] = <Ka_{Z1, x_n} Kb_{x_n, Z2}>_p(x_n)
- Ka_{.,.}, Kb_{.,.} :: RBF kernels
Ka and Kb as well as Z1 and Z2 can differ from each other, but this is supported
only if the Gaussian p is Diagonal (p.cov NxD) and Ka, Kb have disjoint active_dims
in which case the joint expectations simplify into a product of expectations
:return: NxMxM
"""
if kern1.on_separate_dims(kern2) and isinstance(p, DiagonalGaussian): # no joint expectations required
eKxz1 = expectation(p, (kern1, feat1))
eKxz2 = expectation(p, (kern2, feat2))
return eKxz1[:, :, None] * eKxz2[:, None, :]
if feat1 != feat2 or kern1 != kern2:
raise NotImplementedError("The expectation over two kernels has only an "
"analytical implementation if both kernels are equal.")
kern = kern1
feat = feat1
with params_as_tensors_for(kern), params_as_tensors_for(feat):
# use only active dimensions
Xcov = kern._slice_cov(tf.matrix_diag(p.cov) if isinstance(p, DiagonalGaussian) else p.cov)
Z, Xmu = kern._slice(feat.Z, p.mu)
N = tf.shape(Xmu)[0]
D = tf.shape(Xmu)[1]
squared_lengthscales = kern.lengthscales ** 2. if kern.ARD \
else tf.zeros((D,), dtype=settings.tf_float) + kern.lengthscales ** 2.
sqrt_det_L = tf.reduce_prod(0.5 * squared_lengthscales) ** 0.5
C = tf.cholesky(0.5 * tf.matrix_diag(squared_lengthscales) + Xcov) # NxDxD
dets = sqrt_det_L / tf.exp(tf.reduce_sum(tf.log(tf.matrix_diag_part(C)), axis=1)) # N
C_inv_mu = tf.matrix_triangular_solve(C, tf.expand_dims(Xmu, 2), lower=True) # NxDx1
C_inv_z = tf.matrix_triangular_solve(C,
tf.tile(tf.expand_dims(tf.transpose(Z) / 2., 0), [N, 1, 1]),
lower=True) # NxDxM
mu_CC_inv_mu = tf.expand_dims(tf.reduce_sum(tf.square(C_inv_mu), 1), 2) # Nx1x1
z_CC_inv_z = tf.reduce_sum(tf.square(C_inv_z), 1) # NxM
zm_CC_inv_zn = tf.matmul(C_inv_z, C_inv_z, transpose_a=True) # NxMxM
two_z_CC_inv_mu = 2 * tf.matmul(C_inv_z, C_inv_mu, transpose_a=True)[:, :, 0] # NxM
exponent_mahalanobis = mu_CC_inv_mu + tf.expand_dims(z_CC_inv_z, 1) + \
tf.expand_dims(z_CC_inv_z, 2) + 2 * zm_CC_inv_zn - \
tf.expand_dims(two_z_CC_inv_mu, 2) - tf.expand_dims(two_z_CC_inv_mu, 1) # NxMxM
exponent_mahalanobis = tf.exp(-0.5 * exponent_mahalanobis) # NxMxM
# Compute sqrt(self.K(Z)) explicitly to prevent automatic gradient from
# being NaN sometimes, see pull request #615
kernel_sqrt = tf.exp(-0.25 * kern.square_dist(Z, None))
return kern.variance ** 2 * kernel_sqrt * \
tf.reshape(dets, [N, 1, 1]) * exponent_mahalanobis
def _quadrature_expectation(p, obj1, feature1, obj2, feature2, num_gauss_hermite_points):
"""
General handling of quadrature expectations for Gaussians and DiagonalGaussians
Fallback method for missing analytic expectations
"""
num_gauss_hermite_points = 100 if num_gauss_hermite_points is None else num_gauss_hermite_points
warnings.warn("Quadrature is used to calculate the expectation. This means that "
"an analytical implementations is not available for the given combination.")
if obj2 is None:
eval_func = lambda x: get_eval_func(obj1, feature1)(x)
elif obj1 is None:
raise NotImplementedError("First object cannot be None.")
else:
eval_func = lambda x: (get_eval_func(obj1, feature1, np.s_[:, :, None])(x) *
get_eval_func(obj2, feature2, np.s_[:, None, :])(x))
if isinstance(p, DiagonalGaussian):
if isinstance(obj1, kernels.Kernel) and isinstance(obj2, kernels.Kernel) \
and obj1.on_separate_dims(obj2): # no joint expectations required
eKxz1 = quadrature_expectation(p, (obj1, feature1),
num_gauss_hermite_points=num_gauss_hermite_points)
eKxz2 = quadrature_expectation(p, (obj2, feature2),
num_gauss_hermite_points=num_gauss_hermite_points)
return eKxz1[:, :, None] * eKxz2[:, None, :]
else:
cov = tf.matrix_diag(p.cov)
else:
cov = p.cov
return mvnquad(eval_func, p.mu, cov, num_gauss_hermite_points)
def _build_predict(self, Xnew, full_cov=False):
"""
The posterior variance of F is given by
q(f) = N(f | K alpha + mean, [K^-1 + diag(lambda**2)]^-1)
Here we project this to F*, the values of the GP at Xnew which is given
by
q(F*) = N ( F* | K_{*F} alpha + mean, K_{**} - K_{*f}[K_{ff} +
diag(lambda**-2)]^-1 K_{f*} )
"""
# compute kernel things
Kx = self.kern.K(self.X, Xnew)
K = self.kern.K(self.X)
# predictive mean
f_mean = tf.matmul(Kx, self.q_alpha, transpose_a=True) + self.mean_function(Xnew)
# predictive var
A = K + tf.matrix_diag(tf.transpose(1. / tf.square(self.q_lambda)))
L = tf.cholesky(A)
Kx_tiled = tf.tile(tf.expand_dims(Kx, 0), [self.num_latent, 1, 1])
LiKx = tf.matrix_triangular_solve(L, Kx_tiled)
if full_cov:
f_var = self.kern.K(Xnew) - tf.matmul(LiKx, LiKx, transpose_a=True)
else:
f_var = self.kern.Kdiag(Xnew) - tf.reduce_sum(tf.square(LiKx), 1)
return f_mean, tf.transpose(f_var)
def _expectation(p, kern, feat, none1, none2, nghp=None):
"""
Compute the expectation:
<K_{X, Z}>_p(X)
- K_{.,.} :: RBF kernel
:return: NxM
"""
with params_as_tensors_for(kern), params_as_tensors_for(feat):
# use only active dimensions
Xcov = kern._slice_cov(p.cov)
Z, Xmu = kern._slice(feat.Z, p.mu)
D = tf.shape(Xmu)[1]
if kern.ARD:
lengthscales = kern.lengthscales
else:
lengthscales = tf.zeros((D,), dtype=settings.tf_float) + kern.lengthscales
chol_L_plus_Xcov = tf.cholesky(tf.matrix_diag(lengthscales ** 2) + Xcov) # NxDxD
all_diffs = tf.transpose(Z) - tf.expand_dims(Xmu, 2) # NxDxM
exponent_mahalanobis = tf.matrix_triangular_solve(chol_L_plus_Xcov, all_diffs, lower=True) # NxDxM
exponent_mahalanobis = tf.reduce_sum(tf.square(exponent_mahalanobis), 1) # NxM
exponent_mahalanobis = tf.exp(-0.5 * exponent_mahalanobis) # NxM
sqrt_det_L = tf.reduce_prod(lengthscales)
sqrt_det_L_plus_Xcov = tf.exp(tf.reduce_sum(tf.log(tf.matrix_diag_part(chol_L_plus_Xcov)), axis=1))
determinants = sqrt_det_L / sqrt_det_L_plus_Xcov # N
return kern.variance * (determinants[:, None] * exponent_mahalanobis)
def _expectation(p, mean, none, kern, feat, nghp=None):
"""
Compute the expectation:
expectation[n] = <x_n K_{x_n, Z}>_p(x_n)
- K_{.,.} :: RBF kernel
:return: NxDxM
"""
Xmu, Xcov = p.mu, p.cov
with tf.control_dependencies([tf.assert_equal(
tf.shape(Xmu)[1], tf.constant(kern.input_dim, settings.tf_int),
message="Currently cannot handle slicing in exKxz.")]):
Xmu = tf.identity(Xmu)
with params_as_tensors_for(kern), params_as_tensors_for(feat):
D = tf.shape(Xmu)[1]
lengthscales = kern.lengthscales if kern.ARD \
else tf.zeros((D,), dtype=settings.float_type) + kern.lengthscales
chol_L_plus_Xcov = tf.cholesky(tf.matrix_diag(lengthscales ** 2) + Xcov) # NxDxD
all_diffs = tf.transpose(feat.Z) - tf.expand_dims(Xmu, 2) # NxDxM
sqrt_det_L = tf.reduce_prod(lengthscales)
sqrt_det_L_plus_Xcov = tf.exp(tf.reduce_sum(tf.log(tf.matrix_diag_part(chol_L_plus_Xcov)), axis=1))
determinants = sqrt_det_L / sqrt_det_L_plus_Xcov # N
exponent_mahalanobis = tf.cholesky_solve(chol_L_plus_Xcov, all_diffs) # NxDxM
non_exponent_term = tf.matmul(Xcov, exponent_mahalanobis, transpose_a=True)
non_exponent_term = tf.expand_dims(Xmu, 2) + non_exponent_term # NxDxM
exponent_mahalanobis = tf.reduce_sum(all_diffs * exponent_mahalanobis, 1) # NxM
exponent_mahalanobis = tf.exp(-0.5 * exponent_mahalanobis) # NxM
return kern.variance * (determinants[:, None] * exponent_mahalanobis)[:, None, :] * non_exponent_term
def Test(self):
np.random.seed(1)
n = shape_[-1]
batch_shape = shape_[:-2]
a = np.random.uniform(
low=-1.0, high=1.0, size=n * n).reshape([n, n]).astype(dtype_)
a += a.T
a = np.tile(a, batch_shape + (1, 1))
if dtype_ == np.float32:
atol = 1e-4
else:
atol = 1e-12
for compute_v in False, True:
np_e, np_v = np.linalg.eig(a)
with self.test_session():
if compute_v:
tf_e, tf_v = tf.self_adjoint_eig(tf.constant(a))
# Check that V*diag(E)*V^T is close to A.
a_ev = tf.batch_matmul(
tf.batch_matmul(tf_v, tf.matrix_diag(tf_e)), tf_v, adj_y=True)
self.assertAllClose(a_ev.eval(), a, atol=atol)
# Compare to numpy.linalg.eig.
CompareEigenDecompositions(self, np_e, np_v, tf_e.eval(), tf_v.eval(),
atol)
else:
tf_e = tf.self_adjoint_eigvals(tf.constant(a))
self.assertAllClose(
np.sort(np_e, -1), np.sort(tf_e.eval(), -1), atol=atol)
def _expectation(p, kern1, feat1, kern2, feat2, nghp=None):
"""
Compute the expectation:
expectation[n] = <Ka_{Z1, x_n} Kb_{x_n, Z2}>_p(x_n)
- Ka_{.,.}, Kb_{.,.} :: Linear kernels
Ka and Kb as well as Z1 and Z2 can differ from each other, but this is supported
only if the Gaussian p is Diagonal (p.cov NxD) and Ka, Kb have disjoint active_dims
in which case the joint expectations simplify into a product of expectations
:return: NxMxM
"""
if kern1.on_separate_dims(kern2) and isinstance(p, DiagonalGaussian): # no joint expectations required
eKxz1 = expectation(p, (kern1, feat1))
eKxz2 = expectation(p, (kern2, feat2))
return eKxz1[:, :, None] * eKxz2[:, None, :]
if kern1 != kern2 or feat1 != feat2:
raise NotImplementedError("The expectation over two kernels has only an "
"analytical implementation if both kernels are equal.")
kern = kern1
feat = feat1
with params_as_tensors_for(kern), params_as_tensors_for(feat):
# use only active dimensions
Xcov = kern._slice_cov(tf.matrix_diag(p.cov) if isinstance(p, DiagonalGaussian) else p.cov)
Z, Xmu = kern._slice(feat.Z, p.mu)
N = tf.shape(Xmu)[0]
var_Z = kern.variance * Z
tiled_Z = tf.tile(tf.expand_dims(var_Z, 0), (N, 1, 1)) # NxMxD
XX = Xcov + tf.expand_dims(Xmu, 1) * tf.expand_dims(Xmu, 2) # NxDxD
return tf.matmul(tf.matmul(tiled_Z, XX), tiled_Z, transpose_b=True)
def testSampleWithBroadcastScale(self):
# mu corresponds to a 2-batch of 3-variate normals
mu = np.zeros([2, 3])
# diag corresponds to no batches of 3-variate normals
diag = np.ones([3])
with self.test_session():
dist = tfd.VectorExponentialDiag(mu, diag, validate_args=True)
mean = dist.mean()
self.assertAllEqual([2, 3], mean.get_shape())
self.assertAllClose(mu + diag, mean.eval())
n = int(1e4)
samps = dist.sample(n, seed=0).eval()
samps_centered = samps - samps.mean(axis=0)
cov_mat = tf.matrix_diag(diag).eval()**2
sample_cov = np.matmul(samps_centered.transpose([1, 2, 0]),
samps_centered.transpose([1, 0, 2])) / n
self.assertAllClose(mu + diag, samps.mean(axis=0),
atol=0.10, rtol=0.05)
self.assertAllClose([cov_mat, cov_mat], sample_cov,
atol=0.10, rtol=0.05)
def K(self, X, X2=None, presliced=False):
if X2 is None:
d = tf.fill(tf.stack([tf.shape(X)[0]]), tf.squeeze(self.variance))
return tf.matrix_diag(d)
else:
shape = tf.stack([tf.shape(X)[0], tf.shape(X2)[0]])
return tf.zeros(shape, settings.float_type)
def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder):
shape = list(shape)
diag_shape = shape[:-1]
diag = tf.random_normal(diag_shape, dtype=dtype.real_dtype)
if dtype.is_complex:
diag = tf.complex(
diag, tf.random_normal(diag_shape, dtype=dtype.real_dtype))
diag_ph = tf.placeholder(dtype=dtype)
if use_placeholder:
# Evaluate the diag here because (i) you cannot feed a tensor, and (ii)
# diag is random and we want the same value used for both mat and
# feed_dict.
diag = diag.eval()
operator = linalg.LinearOperatorDiag(diag_ph)
feed_dict = {diag_ph: diag}
else:
operator = linalg.LinearOperatorDiag(diag)
feed_dict = None
mat = tf.matrix_diag(diag)
return operator, mat, feed_dict
def testVector(self):
with self.test_session(use_gpu=self._use_gpu):
v = np.array([1.0, 2.0, 3.0])
mat = np.diag(v)
v_diag = tf.matrix_diag(v)
self.assertEqual((3, 3), v_diag.get_shape())
self.assertAllEqual(v_diag.eval(), mat)
def K(self, X, X2=None, full_output_cov=True):
K = self.kern.K(X, X2) # N x N2
if full_output_cov:
Ks = tf.tile(K[..., None], [1, 1, self.P]) # N x N2 x P
return tf.transpose(tf.matrix_diag(Ks), [0, 2, 1, 3]) # N x P x N2 x P
else:
return tf.tile(K[None, ...], [self.P, 1, 1]) # P x N x N2
def test_broadcast_apply_and_solve(self):
# These cannot be done in the automated (base test class) tests since they
# test shapes that tf.matmul cannot handle.
# In particular, tf.matmul does not broadcast.
with self.test_session() as sess:
x = tf.random_normal(shape=(2, 2, 3, 4))
# This LinearOperatorDiag will be brodacast to (2, 2, 3, 3) during solve
# and apply with 'x' as the argument.
diag = tf.random_uniform(shape=(2, 1, 3))
operator = linalg.LinearOperatorDiag(diag)
self.assertAllEqual((2, 1, 3, 3), operator.shape)
# Create a batch matrix with the broadcast shape of operator.
diag_broadcast = tf.concat(1, (diag, diag))
mat = tf.matrix_diag(diag_broadcast)
self.assertAllEqual((2, 2, 3, 3), mat.get_shape()) # being pedantic.
operator_apply = operator.apply(x)
mat_apply = tf.matmul(mat, x)
self.assertAllEqual(operator_apply.get_shape(), mat_apply.get_shape())
self.assertAllClose(*sess.run([operator_apply, mat_apply]))
operator_solve = operator.solve(x)
mat_solve = tf.matrix_solve(mat, x)
self.assertAllEqual(operator_solve.get_shape(), mat_solve.get_shape())
self.assertAllClose(*sess.run([operator_solve, mat_solve]))
def K(self, X, X2=None, presliced=False):
if X2 is None:
d = tf.fill(tf.shape(X)[:-1], tf.squeeze(self.variance))
return tf.matrix_diag(d)
else:
shape = tf.concat([tf.shape(X)[:-2],
tf.reshape(tf.shape(X)[-2], [1]),
tf.reshape(tf.shape(X2)[-2], [1])], 0)
return tf.zeros(shape, settings.float_type)
def testSample(self):
mu = [-1., 1]
diag = [1., -2]
dist = tfd.VectorLaplaceDiag(mu, diag, validate_args=True)
samps = self.evaluate(dist.sample(int(1e4), seed=0))
cov_mat = 2. * self.evaluate(tf.matrix_diag(diag))**2
self.assertAllClose(mu, samps.mean(axis=0), atol=0., rtol=0.05)
self.assertAllClose(cov_mat, np.cov(samps.T), atol=0.05, rtol=0.05)
def K(self, X, X2=None):
X, X2 = self._slice(X, X2)
X = tf.cast(X[:, 0], tf.int32)
if X2 is None:
X2 = X
else:
X2 = tf.cast(X2[:, 0], tf.int32)
B = tf.matmul(self.W, self.W, transpose_b=True) + tf.matrix_diag(self.kappa)
return tf.gather(tf.transpose(tf.gather(B, X2)), X)
def testGrad(self):
shapes = ((3,), (7, 4))
with self.test_session(use_gpu=self._use_gpu):
for shape in shapes:
x = tf.constant(np.random.rand(*shape), np.float32)
y = tf.matrix_diag(x)
error = tf.test.compute_gradient_error(x, x.get_shape().as_list(),
y, y.get_shape().as_list())
self.assertLess(error, 1e-4)
def _build_operator_and_mat(self, batch_shape, k, dtype=np.float64):
# Build an identity matrix with right shape and dtype.
# Build an operator that should act the same way.
batch_shape = list(batch_shape)
diag_shape = batch_shape + [k]
matrix_shape = batch_shape + [k, k]
diag = tf.ones(diag_shape, dtype=dtype)
identity_matrix = tf.matrix_diag(diag)
operator = operator_pd_identity.OperatorPDIdentity(matrix_shape, dtype)
return operator, identity_matrix.eval()
def testSample(self):
mu = [-1.0, 1.0]
diag = [1.0, 2.0]
with self.test_session():
dist = distributions.MultivariateNormalDiag(mu, diag)
samps = dist.sample_n(1000, seed=0).eval()
cov_mat = tf.matrix_diag(diag).eval()**2
self.assertAllClose(mu, samps.mean(axis=0), atol=0.1)
self.assertAllClose(cov_mat, np.cov(samps.T), atol=0.1)
def testMultivariateNormalDiagWithSoftplusStDev(self):
mu = [-1.0, 1.0]
diag = [-1.0, -2.0]
with self.test_session():
dist = distributions.MultivariateNormalDiagWithSoftplusStDev(mu, diag)
samps = dist.sample(1000, seed=0).eval()
cov_mat = tf.matrix_diag(tf.nn.softplus(diag)).eval()**2
self.assertAllClose(mu, samps.mean(axis=0), atol=0.1)
self.assertAllClose(cov_mat, np.cov(samps.T), atol=0.1)
def testMultivariateNormalDiagWithSoftplusScale(self):
mu = [-1.0, 1.0]
diag = [-1.0, -2.0]
dist = tfd.MultivariateNormalDiagWithSoftplusScale(
mu, diag, validate_args=True)
samps = self.evaluate(dist.sample(1000, seed=0))
cov_mat = self.evaluate(tf.matrix_diag(tf.nn.softplus(diag))**2)
self.assertAllClose(mu, samps.mean(axis=0), atol=0.1)
self.assertAllClose(cov_mat, np.cov(samps.T), atol=0.1)
def _covariance(self):
# Let
# W = (w1,...,wk), with wj ~ iid Exponential(0, 1).
# Then this distribution is
# X = loc + LW,
# and then since Cov(wi, wj) = 1 if i=j, and 0 otherwise,
# Cov(X) = L Cov(W W^T) L^T = L L^T.
if distribution_util.is_diagonal_scale(self.scale):
return tf.matrix_diag(tf.square(self.scale.diag_part()))
else:
return self.scale.matmul(self.scale.to_dense(), adjoint_arg=True)
def testSample(self):
mu = [-1., 1]
diag = [1., -2]
with self.test_session():
dist = tfd.MultivariateNormalDiag(mu, diag, validate_args=True)
samps = dist.sample(int(1e3), seed=0).eval()
cov_mat = tf.matrix_diag(diag).eval()**2
self.assertAllClose(mu, samps.mean(axis=0),
atol=0., rtol=0.05)
self.assertAllClose(cov_mat, np.cov(samps.T),
atol=0.05, rtol=0.05)
def testSample(self):
mu = [-2., 1]
diag = [1., -2]
with self.test_session():
dist = tfd.VectorExponentialDiag(mu, diag, validate_args=True)
samps = dist.sample(int(1e4), seed=0).eval()
cov_mat = tf.matrix_diag(diag).eval()**2
self.assertAllClose([-2 + 1, 1. - 2], samps.mean(axis=0),
atol=0., rtol=0.05)
self.assertAllClose(cov_mat, np.cov(samps.T),
atol=0.05, rtol=0.05)
def _covariance(self):
# Let
# W = (w1,...,wk), with wj ~ iid Laplace(0, 1).
# Then this distribution is
# X = loc + LW,
# and since E[X] = loc,
# Cov(X) = E[LW W^T L^T] = L E[W W^T] L^T.
# Since E[wi wj] = 0 if i != j, and 2 if i == j, we have
# Cov(X) = 2 LL^T
if distribution_util.is_diagonal_scale(self.scale):
return 2. * tf.matrix_diag(tf.square(self.scale.diag_part()))
else:
return 2. * self.scale.matmul(self.scale.to_dense(), adjoint_arg=True)
def testBatchVector(self):
with self.test_session(use_gpu=self._use_gpu):
v_batch = np.array([[1.0, 2.0, 3.0],
[4.0, 5.0, 6.0]])
mat_batch = np.array(
[[[1.0, 0.0, 0.0],
[0.0, 2.0, 0.0],
[0.0, 0.0, 3.0]],
[[4.0, 0.0, 0.0],
[0.0, 5.0, 0.0],
[0.0, 0.0, 6.0]]])
v_batch_diag = tf.matrix_diag(v_batch)
self.assertEqual((2, 3, 3), v_batch_diag.get_shape())
self.assertAllEqual(v_batch_diag.eval(), mat_batch)
def _updated_mat(self, mat, v, diag):
# Get dense matrix defined by its square root, which is an update of `mat`:
# A = (mat + v D v^T) (mat + v D v^T)^T
# D is the diagonal matrix with `diag` on the diagonal.
# If diag is None, then it defaults to the identity matrix, so DV^T = V^T
if diag is None:
diag_vt = tf.matrix_transpose(v)
else:
diag_mat = tf.matrix_diag(diag)
diag_vt = tf.matmul(diag_mat, v, adjoint_b=True)
v_diag_vt = tf.matmul(v, diag_vt)
sqrt = mat + v_diag_vt
a = tf.matmul(sqrt, sqrt, adjoint_b=True)
return a.eval()
def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder):
diag = linear_operator_test_util.random_sign_uniform(
shape[:-1], minval=1., maxval=2., dtype=dtype)
if use_placeholder:
diag_ph = tf.placeholder(dtype=dtype)
# Evaluate the diag here because (i) you cannot feed a tensor, and (ii)
# diag is random and we want the same value used for both mat and
# feed_dict.
diag = diag.eval()
operator = linalg.LinearOperatorDiag(diag_ph)
feed_dict = {diag_ph: diag}
else:
operator = linalg.LinearOperatorDiag(diag)
feed_dict = None
mat = tf.matrix_diag(diag)
return operator, mat, feed_dict
def batch_matrix_log(x, epsilon):
"""
Matrix log with epsilon to ensure stability.
Input must be a Symmetric matrix.
Parameters
----------
x : tf.Tensor with [..., dim1, dim2]
epsilon
Returns
-------
log of eigen-values.
"""
s, u, v = tf.svd(x)
# print(s.eval())
inner = s + epsilon
inner = tf.log(inner)
inner = tf.matrix_diag(inner)
return tf.matmul(u, tf.matmul(inner, tf.transpose(u, [0,2,1])))
def _quadrature_expectation(p, obj1, feature1, obj2, feature2,
num_gauss_hermite_points):
"""
General handling of quadrature expectations for Gaussians and DiagonalGaussians
Fallback method for missing analytic expectations
"""
num_gauss_hermite_points = 100 if num_gauss_hermite_points is None else num_gauss_hermite_points
logger.warn(
"Quadrature is used to calculate the expectation. This means that "
"an analytical implementations is not available for the given combination."
)
if obj2 is None:
eval_func = lambda x: get_eval_func(obj1, feature1)(x)
elif obj1 is None:
raise NotImplementedError("First object cannot be None.")
else:
eval_func = lambda x: (get_eval_func(obj1, feature1, np.s_[:, :, None])
(x) * get_eval_func(obj2, feature2, np.
s_[:, None, :])(x))
if isinstance(p, DiagonalGaussian):
if isinstance(obj1, kernels.Kernel) and isinstance(obj2, kernels.Kernel) \
and obj1.on_separate_dims(obj2): # no joint expectations required
eKxz1 = quadrature_expectation(
p, (obj1, feature1),
num_gauss_hermite_points=num_gauss_hermite_points)
eKxz2 = quadrature_expectation(
p, (obj2, feature2),
num_gauss_hermite_points=num_gauss_hermite_points)
return eKxz1[:, :, None] * eKxz2[:, None, :]
else:
cov = tf.matrix_diag(p.cov)
else:
cov = p.cov
return mvnquad(eval_func, p.mu, cov, num_gauss_hermite_points)
def constraints(self):
# don't how to do this, to keep in consistent, need to return this twice
# decisions_at_negative = tf.less(self._decision_vars, 0.0 - self.epsilon)
decisions_at_bound = 1 - tf.abs(
tf.cast(tf.equal(self._decision_vars, 0.0), dtype=tf.float32) +
tf.cast(tf.equal(self._decision_vars, 1.0), dtype=tf.float32))
# decisions_at_bound *= 100
# positive_margin = 0.5 - tf.abs(self._decision_vars - 0.5) - self._epsilon
# negative_margin = -0.5 + tf.abs(self._decision_vars - 0.5) - self._epsilon
# positive_margin = tf.reshape(positive_margin, [-1])
# negative_margin = tf.reshape(negative_margin, [-1])
# assume epsilon won't be too large s.t. two interval overlap
# decisions_at_bound = tf.abs(
# tf.cast(tf.less(tf.abs(self._decision_vars - 0.0), self._epsilon), dtype=tf.float32) +
# tf.cast(tf.less(tf.abs(self._decision_vars - 1.0), self._epsilon), dtype=tf.float32))
# decisions_at_negative = tf.less(self._decision_vars, 0.0, 'decisions_at_negative_side')
decisions_at_bound = tf.reshape(decisions_at_bound, [-1])
decision_vars = tf.rint(self._decision_vars)
lose_diagonal = decision_vars - tf.matrix_diag(
tf.matrix_diag_part(decision_vars))
predecessor = tf.reduce_sum(lose_diagonal, 0) - 1
successor = tf.reduce_sum(lose_diagonal, 1) - 1
n = self._size
mtz = []
# mtz, Miller-Tucker-Zemlin formulation
# enforce single subtour
for i in range(n):
for j in range(n):
if i != j:
mtz.append(dummy_vars[i] - dummy_vars[j] +
n * lose_diagonal[i][j] - n + 1)
mtz = tf.stack(mtz) # n x (n-1)
return tf.concat([
decisions_at_bound, decisions_at_bound, predecessor, successor, mtz
], 0)
def classification_loss(self):
F_h_h = tf.matmul(self.h_temp, tf.transpose(self.h_temp))
F_hn_hn = tf.diag_part(F_h_h)
F_h_h = tf.subtract(F_h_h, tf.matrix_diag(F_hn_hn))
classes = tf.reduce_max(self.gt) - tf.reduce_min(self.gt) + 1
label_onehot = tf.one_hot(self.gt - 1, classes) # gt begin from 1
label_num = tf.reduce_sum(
label_onehot, 0,
keep_dims=True) # should sub 1.Avoid numerical errors
F_h_h_sum = tf.matmul(F_h_h, label_onehot)
label_num_broadcast = tf.tile(label_num,
[self.trainLen, 1]) - label_onehot
F_h_h_mean = tf.divide(F_h_h_sum, label_num_broadcast)
gt_ = tf.cast(tf.argmax(F_h_h_mean, axis=1),
tf.int32) + 1 # gt begin from 1
F_h_h_mean_max = tf.reduce_max(F_h_h_mean, axis=1, keep_dims=False)
theta = tf.cast(tf.not_equal(self.gt, gt_), tf.float32)
F_h_hn_mean_ = tf.multiply(F_h_h_mean, label_onehot)
F_h_hn_mean = tf.reduce_sum(F_h_hn_mean_, axis=1, name='F_h_hn_mean')
return tf.reduce_sum(
tf.nn.relu(tf.add(theta, tf.subtract(F_h_h_mean_max,
F_h_hn_mean))))
def svd(A, full_matrices=False, compute_uv=True, name=None):
# since dA = dUSVt + UdSVt + USdVt
# we can simply recompute each matrix using A = USVt
# while blocking gradients to the original op.
_, M, N = A.get_shape().as_list()
P = min(M, N)
S0, U0, V0 = map(tf.stop_gradient, tf.svd(A, full_matrices=True, name=name))
Ui, Vti = map(tf.matrix_inverse, [U0, tf.transpose(V0, (0, 2, 1))])
# A = USVt
# S = UiAVti
S = tf.matmul(Ui, tf.matmul(A, Vti))
S = tf.matrix_diag_part(S)
if not compute_uv:
return S
Si = tf.pad(tf.matrix_diag(1/S0), [[0,0], [0,N-P], [0,M-P]])
# U = AVtiSi
U = tf.matmul(A, tf.matmul(Vti, Si))
U = U if full_matrices else U[:, :M, :P]
# Vt = SiUiA
V = tf.transpose(tf.matmul(Si, tf.matmul(Ui, A)), (0, 2, 1))
V = V if full_matrices else V[:, :N, :P]
return S, U, V
def log_prob_fn(params):
rho, alpha, sigma = tf.split(params, [num_features, 1, 1], -1)
one = tf.ones(num_features)
def indep(d):
return tfd.Independent(d, 1)
p_rho = indep(tfd.InverseGamma(5. * one, 5. * one))
p_alpha = indep(tfd.HalfNormal([1.]))
p_sigma = indep(tfd.HalfNormal([1.]))
rho_shape = tf.shape(rho)
alpha_shape = tf.shape(alpha)
x1 = tf.expand_dims(x, -2)
x2 = tf.expand_dims(x, -3)
exp = -0.5 * tf.squared_difference(x1, x2)
exp /= tf.reshape(
tf.square(rho),
tf.concat([rho_shape[:1], [1, 1], rho_shape[1:]], 0))
exp = tf.reduce_sum(exp, -1, keep_dims=True)
exp += 2. * tf.reshape(
tf.log(alpha),
tf.concat([alpha_shape[:1], [1, 1], alpha_shape[1:]], 0))
exp = tf.exp(exp[Ellipsis, 0])
exp += tf.matrix_diag(
tf.tile(tf.square(sigma), [1, int(x.shape[0])]) + 1e-6)
exp = tf.check_numerics(exp, "exp 2 has NaNs")
with tf.control_dependencies([tf.print(exp[0], summarize=99999)]):
exp = tf.identity(exp)
p_y = tfd.MultivariateNormalFullCovariance(covariance_matrix=exp)
log_prob = (p_rho.log_prob(rho) + p_alpha.log_prob(alpha) +
p_sigma.log_prob(sigma) + p_y.log_prob(y))
return log_prob
def gradient_svd(op, ds, dU, dV):
s, U, V = op.outputs
u_sz = tf.squeeze(tf.slice(tf.shape(dU),[1],[1]))
v_sz = tf.squeeze(tf.slice(tf.shape(dV),[1],[1]))
s_sz = tf.squeeze(tf.slice(tf.shape(ds),[1],[1]))
S = tf.matrix_diag(s)
s_2 = tf.square(s)
eye = tf.expand_dims(tf.eye(s_sz),0)
k = (1 - eye)/(tf.expand_dims(s_2,2)-tf.expand_dims(s_2,1) + eye)
KT = tf.matrix_transpose(k)
KT = removenan(KT)
def msym(X):
return (X+tf.matrix_transpose(X))
def left_grad(U,S,V,dU,dV):
U, V = (V, U); dU, dV = (dV, dU)
D = tf.matmul(dU,tf.matrix_diag(1/(s+1e-8)))
US = tf.matmul(U,S)
grad = tf.matmul(D, V, transpose_b=True)\
+tf.matmul(tf.matmul(U,tf.matrix_diag(tf.matrix_diag_part(-tf.matmul(U,D,transpose_a=True)))), V, transpose_b=True)\
+tf.matmul(2*tf.matmul(US, msym(KT*(tf.matmul(V,-tf.matmul(V,tf.matmul(D,US,transpose_a=True)),transpose_a=True)))),V,transpose_b=True)
grad = tf.matrix_transpose(grad)
return grad
def right_grad(U,S,V,dU,dV):
US = tf.matmul(U,S)
grad = tf.matmul(2*tf.matmul(US, msym(KT*(tf.matmul(V,dV,transpose_a=True))) ),V,transpose_b=True)
return grad
grad = tf.cond(tf.greater(v_sz, u_sz), lambda : left_grad(U,S,V,dU,dV),
lambda : right_grad(U,S,V,dU,dV))
return [grad]
def __init__(self,
beta,
gamma,
mean,
variance,
conv_weights,
strides=(1, 1),
padding='same',
epsilon=1e-3,
dilation_rate=(1, 1),
**kwargs):
super(FuseConvBN, self).__init__(**kwargs)
# conv layer config
self.strides = strides
self.padding = padding
self.dilation_rate = dilation_rate
# origin weights
self.beta = tf.constant(beta, dtype='float32')
self.gamma = tf.constant(gamma, dtype='float32')
self.mean = tf.constant(mean, dtype='float32')
self.variance = tf.constant(variance, dtype='float32')
self.conv_weights = tf.constant(conv_weights, dtype='float32')
# compute W_{bn} & b_{bn}
k, k, filters_in, filters_out = K.int_shape(
self.conv_weights) # build shape in keras Conv
self.filters_out = filters_out
weights_conv = tf.reshape(
tf.transpose(self.conv_weights, (3, 0, 1, 2)), (filters_out, -1))
weights_bn = tf.matrix_diag(gamma / tf.sqrt(variance + epsilon))
bias_bn = beta - gamma * mean / tf.sqrt(variance + epsilon)
# compute fused W & b
fused_weights = tf.matmul(weights_bn, weights_conv)
self.fused_weights = tf.transpose(
tf.reshape(fused_weights, (filters_out, k, k, filters_in)),
(1, 2, 3, 0))
self.fused_bias = bias_bn
def _expectation(p, kern, feat, none1, none2, nghp=None):
"""
Compute the expectation:
<K_{X, Z}>_p(X)
- K_{.,.} :: RBF kernel
remember in this case, p(X) is factorized not independently.
:return: NxM
"""
print('TGaussian Psi1')
with params_as_tensors_for(kern, feat):
Xcov = kern._slice_cov(p.cov) # QxNxN - because var distribution is factorized only per latent dimension
Z, Xmu = kern._slice(feat.Z, p.mu) # MxQ and NxQ
D = tf.shape(Xmu)[1] # Q
if kern.ARD:
lengthscales = kern.lengthscales
else:
lengthscales = tf.zeros((D,), dtype=settings.tf_float) + kern.lengthscales
#The entry of this covariance matrix is the variance
chol_L_plus_Xcov = tf.cholesky(tf.matrix_diag(lengthscales ** 2) + Xcov) #QxNxN
print(chol_L_plus_Xcov)
all_diffs = tf.transpose(Z) - tf.expand_dims(Xmu, 2)
all_diffs = tf.transpose(all_diffs, [1, 0, 2]) # QxNxM
exponent_mahalanobis = tf.matrix_triangular_solve(chol_L_plus_Xcov, all_diffs, lower=True) # QxNxN QxNxM
exponent_mahalanobis = tf.reduce_sum(tf.square(exponent_mahalanobis), 1) # Q x M
exponent_mahalanobis = tf.exp(-0.5 * exponent_mahalanobis) # Q x M
sqrt_det_L = tf.reduce_prod(lengthscales)
sqrt_det_L_plus_Xcov = tf.exp(tf.reduce_sum(tf.log(tf.matrix_diag_part(chol_L_plus_Xcov)), axis=1))
determinants = sqrt_det_L / sqrt_det_L_plus_Xcov # N
return kern.variance * (determinants[:, None] * exponent_mahalanobis)
def decode(self, vec_rep):
"""De-vectorizes the bosonic matrices. Reverse of the encode method.
Arguments:
vec_rep (tensor of shape (batch_size, K)): the vectorized gauge representatives
Returns:
rep (tensor of shape (batch_size, 3, N, N)): the bosonic matrix gauge representatives
"""
batch_size = int(vec_rep.shape[0])
if self.bijector is None:
vec_rep = tf.reshape(vec_rep, [batch_size, -1, self.algebra.dim])
return self.algebra.vector_to_matrix(vec_rep)
else:
N = self.algebra.N
dim = N - 1
diff = self.bijector.forward(vec_rep[:, :dim])
# by our gauge fixing, each row of e must be nondecreasing
e = tf.concat(
[tf.zeros([batch_size, 1]),
tf.cumsum(diff, axis=-1)], axis=-1)
# the sum of each row of e should also be zero
e = e - tf.reduce_sum(e, axis=-1, keepdims=True) / N
mat_e = tf.cast(tf.expand_dims(tf.matrix_diag(e), axis=-3),
tf.complex64)
# by our gauge fixing, first (N - 1) elements in each row of vec_rest must vanish
# and the following (N - 1) elements must be positive
# note this depends on our ordering of the SU(N) basis in algebra.py
vec_rest = tf.concat([
tf.zeros([batch_size, dim]),
self.bijector.forward(vec_rep[:, dim:2 * dim]),
vec_rep[:, 2 * dim:]
],
axis=-1)
vec_rest = tf.reshape(vec_rest, [batch_size, 2, self.algebra.dim])
mat_rest = self.algebra.vector_to_matrix(vec_rest)
rep = tf.concat([mat_e, mat_rest], axis=-3)
return rep
def transition_model(self, states, covariances, odometry):
"""
Implements a stochastic transition model for localization.
:param states: tf op (batch, K, 3), particle states before the update.
:param covariances: tf op (batch, K, 3, 3)
:param odometry: tf op (batch, 3), odometry reading, relative motion in the robot coordinate frame
:return: particle_states updated with the odometry and optionally transition noise
"""
translation_std = self.params.transition_std[
0] / self.params.map_pixel_in_meters # In pixels
rotation_std = self.params.transition_std[1] # In radians
with tf.name_scope('transition'):
part_x, part_y, part_th = tf.unstack(states, axis=-1, num=3)
odometry = tf.expand_dims(odometry, axis=1)
odom_x, odom_y, odom_th = tf.unstack(odometry, axis=-1, num=3)
cos_th = tf.cos(part_th)
sin_th = tf.sin(part_th)
delta_x = cos_th * odom_x - sin_th * odom_y
delta_y = sin_th * odom_x + cos_th * odom_y
delta_th = odom_th
new_th = tf.mod(part_th + delta_th + np.pi, 2 * np.pi) - np.pi
states = tf.stack([part_x + delta_x, part_y + delta_y, new_th],
axis=-1)
pose_cov = tf.square(
tf.constant([translation_std, translation_std, rotation_std],
tf.float32))
noise = tf.abs(
tf.random_normal(states.get_shape(), mean=0.0,
stddev=1.0)) * pose_cov
noise = tf.matrix_diag(noise)
covariances = covariances + noise
return states, covariances
def get_params(self, x, c, b, m, id):
B = tf.shape(x)[0]
d = self.hps.dimension
mask = np.arange(d, dtype=np.float32)
mask = tf.mod(mask + id, 2)
mask = tf.tile(tf.expand_dims(mask, axis=0), [B, 1])
inp = tf.concat([x * mask, mask, c, b, m], axis=1)
params = self.nets[id](inp)
scale, shift = tf.split(params, 2, axis=1)
# reorder
query = m * (1 - b)
order = tf.contrib.framework.argsort(query,
direction='DESCENDING',
stable=True)
t = tf.batch_gather(tf.matrix_diag(query), order)
t = tf.transpose(t, perm=[0, 2, 1])
scale = tf.einsum('nd,ndi->ni', scale, t)
shift = tf.einsum('nd,ndi->ni', shift, t)
# mask
scale = scale * (1. - mask)
shift = shift * (1. - mask)
return scale, shift
def _getMatrixTree(r, A, mask1, mask2, mask_multiply, mask_add):
if mask_multiply is None:
A_masked = A
else:
A_masked = A * mask_multiply
L_reduce = tf.reduce_sum(A_masked, 1)
L_diag = tf.matrix_diag(L_reduce)
L_minus = L_diag - A_masked
LL_diag = L_minus[:, 1:, :]
LL = tf.concat([tf.expand_dims(r, [1]), LL_diag], 1)
if mask_multiply is None:
LL_inv = tf.matrix_inverse(LL)
else:
LL_masked = mask_multiply * LL
LL_masked = LL_masked + mask_add
LL_inv = tf.matrix_inverse(LL_masked) # batch_l, doc_l, doc_l
d0 = tf.multiply(r, LL_inv[:, :, 0]) # root
LL_inv_diag = tf.expand_dims(tf.matrix_diag_part(LL_inv), 2)
tmp1 = tf.matrix_transpose(tf.multiply(tf.matrix_transpose(A_masked), LL_inv_diag))
tmp2 = tf.multiply(A_masked, tf.matrix_transpose(LL_inv))
d_no_root = mask1 * tmp1 - mask2 * tmp2
d = tf.concat([tf.expand_dims(d0,[1]), d_no_root], 1) # add column at beginning for root
return d, d_no_root, LL
def dignoal(x, kernel_size, scatter_rate): #利用对角采样计算均值，
b = tf.shape(x)[0]
w = tf.shape(x)[1]
h = tf.shape(x)[2]
dig = tf.matrix_diag(
(([0] * scatter_rate + [1]) * kernel_size)[:kernel_size])
dig = tf.tile(dig, [
tf.cast(tf.math.ceil(w / kernel_size), dtype=tf.int32),
tf.cast(tf.math.ceil(w / kernel_size), dtype=tf.int32)
])[:w, :h]
dig = tf.tile(tf.expand_dims(tf.expand_dims(dig, axis=0), axis=-1),
[b, 1, 1, tf.shape(x)[-1]])
x_ = x * tf.cast(dig, dtype=tf.float32)
num =tf.cast(b,dtype=tf.float64)*tf.math.floor(w/kernel_size)*tf.math.floor(h/kernel_size)*tf.cast(tf.math.floor(kernel_size/(scatter_rate+1)),dtype=tf.float64)+\
tf.cast(b,dtype=tf.float64)*tf.math.floor(tf.floormod(w,kernel_size)/(scatter_rate+1))*tf.math.floor(h/kernel_size)+\
tf.cast(b,dtype=tf.float64)*tf.math.floor(tf.floormod(h,kernel_size)/(scatter_rate+1))*tf.math.floor(w/kernel_size)+\
tf.cast(b,dtype=tf.float64)*tf.math.floor(tf.reduce_min([tf.floormod(w,kernel_size),tf.floormod(h,kernel_size)])/(scatter_rate+1))
ave = tf.reduce_sum(x_, axis=[0, 1, 2]) / tf.expand_dims(
tf.cast(num, dtype=tf.float32), axis=-1)
return ave
def dx_dtheta_log_px(self, dmu_log_px_, w_px_i_px_norm, exponent_, xi_):
# Returns a n * d * # of components matrix
dx_dw_log_px_, dx_dmu_log_px_, dx_dsigma2_log_px_ = [], [], []
zeta = tf.reduce_sum(dmu_log_px_, [0])
for i in range(self.weights.shape[0]):
zeta_m_exponent = tf.expand_dims(zeta - exponent_[i], 1)
w_px_i_px_norm_i = tf.expand_dims(w_px_i_px_norm[i], -1)
dx_dw_log_px_.append(zeta_m_exponent * w_px_i_px_norm_i)
diag_precision = tf.diag(1. / self.distributions[i].variance())
exponent_i_tensor = tf.expand_dims(exponent_[i], -1)
dx_dmu_log_px_.append(
(tf.matmul(exponent_i_tensor, zeta_m_exponent) +
diag_precision) * w_px_i_px_norm_i)
xi_i_tensor = tf.expand_dims(xi_[i], -1)
exponent_i_diag = tf.matrix_diag(exponent_[i])
diag_precision = tf.expand_dims(diag_precision, 0)
dx_dsigma2_log_px_.append(
(tf.matmul(xi_i_tensor, zeta_m_exponent) +
exponent_i_diag * diag_precision) * w_px_i_px_norm_i)
dx_dw_log_px, dx_dmu_log_px, dx_dsigma2_log_px = tf.stack(
dx_dw_log_px_), tf.stack(dx_dmu_log_px_), tf.stack(
dx_dsigma2_log_px_)
return [dx_dw_log_px, dx_dmu_log_px, dx_dsigma2_log_px]
def _no_cho(Kf=Kf, y=y):
Kf = (Kf + tf.transpose(Kf, perm=[0, 2, 1])) / 2.
e, v = tf.self_adjoint_eig(Kf)
e = tf.where(e > 1e-14, e, 1e-14 * tf.ones_like(e))
Kf = tf.matmul(tf.matmul(v, tf.matrix_diag(e), transpose_a=True),
v)
logdet = tf.reduce_sum(tf.where(e > 1e-14, tf.log(e),
tf.zeros_like(e)),
axis=-1,
name='logdet')
#batch_size, n, 1
alpha = tf.squeeze(tf.matrix_solve(Kf,
tf.expand_dims(y, -1),
name='solve_alpha'),
axis=2)
fstar = tf.matmul(Knm, tf.expand_dims(alpha, -1), transpose_a=True)
cov = Kmm
cov -= tf.matmul(Knm, tf.matrix_solve(Kf, Knm), transpose_a=True)
log_mar_like = (-tf.reduce_sum(y * alpha, axis=1) - logdet -
n * np.log(2. * np.pi)) / 2.
return fstar, cov, log_mar_like
def testSampleWithBroadcastScale(self):
# mu corresponds to a 2-batch of 3-variate normals
mu = np.zeros([2, 3])
# diag corresponds to no batches of 3-variate normals
diag = np.ones([3])
dist = tfd.VectorExponentialDiag(mu, diag, validate_args=True)
mean = dist.mean()
self.assertAllEqual([2, 3], mean.shape)
self.assertAllClose(mu + diag, self.evaluate(mean))
n = int(1e4)
samps = self.evaluate(dist.sample(n, seed=0))
samps_centered = samps - samps.mean(axis=0)
cov_mat = self.evaluate(tf.matrix_diag(diag))**2
sample_cov = np.matmul(
samps_centered.transpose([1, 2, 0]), samps_centered.transpose([1, 0, 2
])) / n
self.assertAllClose(mu + diag, samps.mean(axis=0), atol=0.10, rtol=0.05)
self.assertAllClose([cov_mat, cov_mat], sample_cov, atol=0.10, rtol=0.05)
def ds(x):
# kx ky qx qy om
# x[0] x[1] x[2] x[3] x[4]
topkq = -complex(0, 1) * V0 * ((x[0] + x[2]) - complex(0, 1) *
(x[1] + x[3]))
botkq = complex(0, 1) * V0 * ((x[0] + x[2]) + complex(0, 1) *
(x[1] + x[3]))
innkq = x[4] + complex(0, 1) * Gamm - A * ((x[0] + x[2])**2 +
(x[1] + x[3])**2) - V2
topk = -complex(0, 1) * V0 * (x[0] - complex(0, 1) * x[1])
botk = complex(0, 1) * V0 * (x[0] + complex(0, 1) * x[1])
innk = x[4] + complex(0, 1) * Gamm - A * (x[0]**2 + x[1]**2) - V2
# cent = tf.arange(-(N - 1) / 2, (N - 1) / 2 + 1, 1)
d = hOmg * tf.matrix_diag(cent)
Ginkq = tf.matrix_diag(topkq, k=1) + tf.matrix_diag(
botkq, k=1) + tf.matrix_diag(innkq, k=0) - d
Gink = tf.matrix_diag(topk, k=1) + tf.matrix_diag(
botk, k=1) + tf.matrix_diag(innk, k=0) - d
Grkq = tf.linalg.inv(Ginkq)
Gakq = tf.transpose(tf.conj(Grkq))
Grk = tf.linalg.inv(Gink)
Gak = tf.transpose(tf.conj(Grk))
fer = tf.heaviside(-(d + tf.eye(N) * (x[4] - mu)), 0)
in1 = tf.matmul(Grkq, tf.matmul(Grk, tf.matmul(fer, Gak)))
in2 = tf.matmul(Grkq, tf.matmul(fer, tf.matmul(Gakq, Gak)))
tr = tf.trace(in1 + in2)
# HERE i will divide by DOS, multiply by 2 for spin, and divide by (2pi)^3
dchi = -(4) * Gamm * tr / math.pi**2
return dchi
def loss(inputs, labels):
norm_inputs = tf.nn.l2_normalize(inputs, axis=1)
dot_matrix = tf.expand_dims(norm_inputs, 1) * tf.expand_dims(
norm_inputs, 0)
sim_matrix = tf.reduce_sum(dot_matrix, axis=2)
mask = tf.equal(tf.expand_dims(labels, axis=1),
tf.expand_dims(labels, axis=0))
mask = tf.cast(mask, dtype=tf.float32)
pos_mask = mask - tf.matrix_diag(tf.ones_like(labels, dtype=tf.float32))
neg_mask = 1.0 - mask
easy_a_p = tf.reduce_max(sim_matrix * pos_mask - 1e10 * (1.0 - pos_mask),
axis=1,
keepdims=True)
easy_a_n = tf.reduce_min(sim_matrix * neg_mask + 1e10 * (1.0 - neg_mask),
axis=1,
keepdims=True)
easy_a_n_mask = tf.logical_and(tf.equal(sim_matrix, easy_a_n),
tf.cast(neg_mask, dtype=tf.bool))
sh_a_n_mask = tf.logical_and(tf.less(sim_matrix, easy_a_p),
tf.cast(neg_mask, dtype=tf.bool))
sh_a_n_mask = tf.logical_or(sh_a_n_mask, easy_a_n_mask)
sh_a_n_mask = tf.cast(sh_a_n_mask, dtype=tf.float32)
sh_a_n = tf.reduce_max(sim_matrix * sh_a_n_mask - 1e10 * (1 - sh_a_n_mask),
axis=1,
keepdims=True)
easy_a_p_exp = tf.exp(easy_a_p)
sh_a_n_exp = tf.exp(sh_a_n)
ep_loss = -tf.log(easy_a_p_exp / (easy_a_p_exp + sh_a_n_exp))
ep_loss_mean = tf.reduce_mean(ep_loss)
return ep_loss_mean
def kb_module(self, H, ent_emb, ent_W):
h_h, w = int(H.get_shape()[1]), int(H.get_shape()[2]) #30,64
print H.get_shape(), ent_emb.get_shape()
h_e, w_e = int(ent_emb.get_shape()[2]), int(ent_emb.get_shape()[3]) #5
out1 = tf.reduce_mean(H, axis=1) #(?,64)
reshape_h1 = tf.expand_dims(out1, 1) #(?,1,64)
reshape_h1 = tf.expand_dims(reshape_h1, 1) #(?,1,1,64)
reshape_h1 = tf.tile(reshape_h1, [1, h_h, h_e, 1]) #(?,30,5,64)
reshape_h1 = tf.reshape(reshape_h1, [-1, w]) #(? * 30 * 5,64)
reshape_h2 = tf.reshape(ent_emb, [-1, w_e]) #(? * 30 * 5,64)
print reshape_h1.get_shape(), reshape_h2.get_shape()
M = tf.tanh(
tf.add(tf.matmul(reshape_h1, ent_W['Wqm']),
tf.matmul(reshape_h2, ent_W['Wam']))) #(?,att)
M = tf.matmul(M, ent_W['Wms']) #(?,1)
S = tf.reshape(M, [-1, h_e]) #(?,5)
S = tf.nn.softmax(S)
S_diag = tf.matrix_diag(S) #(?,5,5)
reshape_ent = tf.reshape(ent_emb, [-1, h_e, w_e]) #(?*30,5,64)
attention_a = tf.matmul(S_diag, reshape_ent) #(?*30,5,64)
attention_a = tf.reshape(attention_a,
[-1, h_h, h_e, w_e]) #(?,30,5,64)
#attention_a = tf.reshape(attention_a, [-1, h_h*h_e, w]) #(?,30,5,64)
out2 = tf.reduce_mean(attention_a, axis=2)
#output_a = self.avg_pooling(attention_a)
#output_a = self.max_pooling(attention_a)
#out = tf.reduce_mean(tf.concat([H, out2],2), axis=1)
#return tf.tanh(out)
return tf.concat([H, out2], 2), out2
return tf.concat([H, out2], 2)
return out1, out2
def _expectation(p, kern1, feat1, kern2, feat2, nghp=None):
"""
Compute the expectation:
expectation[n] = <Ka_{Z1, x_n} Kb_{x_n, Z2}>_p(x_n)
- Ka_{.,.}, Kb_{.,.} :: Linear kernels
Ka and Kb as well as Z1 and Z2 can differ from each other, but this is supported
only if the Gaussian p is Diagonal (p.cov NxD) and Ka, Kb have disjoint active_dims
in which case the joint expectations simplify into a product of expectations
:return: NxMxM
"""
if kern1.on_separate_dims(kern2) and isinstance(
p, DiagonalGaussian): # no joint expectations required
eKxz1 = expectation(p, (kern1, feat1))
eKxz2 = expectation(p, (kern2, feat2))
return eKxz1[:, :, None] * eKxz2[:, None, :]
if kern1 != kern2 or feat1 != feat2:
raise NotImplementedError(
"The expectation over two kernels has only an "
"analytical implementation if both kernels are equal.")
kern = kern1
feat = feat1
with params_as_tensors_for(kern), params_as_tensors_for(feat):
# use only active dimensions
Xcov = kern._slice_cov(
tf.matrix_diag(p.cov) if isinstance(p, DiagonalGaussian) else p.cov
)
Z, Xmu = kern._slice(feat.Z, p.mu)
N = tf.shape(Xmu)[0]
var_Z = kern.variance * Z
tiled_Z = tf.tile(tf.expand_dims(var_Z, 0), (N, 1, 1)) # NxMxD
XX = Xcov + tf.expand_dims(Xmu, 1) * tf.expand_dims(Xmu, 2) # NxDxD
return tf.matmul(tf.matmul(tiled_Z, XX), tiled_Z, transpose_b=True)
def get_kl_terms(self,q_mu,q_sqrt):
if self.white:
alpha = tf.transpose(q_mu)[:,:,None] # DxMx1
else:
alpha = tf.matrix_triangular_solve(self.Lu_tiled,tf.transpose(q_mu)[:,:,None], lower=True) # DxMxM * DxMx1 --> DxMx1
if self.q_diag:
Lq = Lq_diag = q_sqrt # MxD
Lq_full = tf.matrix_diag(tf.transpose(q_sqrt)) # DxMxM
else:
Lq = Lq_full = tf.matrix_band_part(q_sqrt, -1, 0) # force lower triangle # DxMxM
Lq_diag = tf.transpose(tf.matrix_diag_part(Lq)) # MxD
# Mahalanobis term: μqᵀ Σp⁻¹ μq
mahalanobis = tf.reduce_sum(tf.square(alpha),axis=[1,2])[:,None] # Dx1
# Log-determinant of the covariance of q(x):
logdet_qcov = tf.reduce_sum(tf.log(tf.square(Lq_diag)),axis=0)[:,None] # Dx1
# Trace term: tr(Σp⁻¹ Σq)
if self.white:
if self.q_diag:
trace = tf.reduce_sum(tf.square(Lq),axis=0)[:,None] # MxD --> Dx1
else:
trace = tf.reduce_sum(tf.square(Lq),axis=[1,2])[:,None] # DxMxM --> Dx1
else:
LpiLq = tf.matrix_triangular_solve(self.Lu_tiled, Lq_full, lower=True) # DxMxM
trace = tf.reduce_sum(tf.square(LpiLq),axis=[1,2])[:,None] # Dx1
# Log-determinant of the covariance of p(x):
if not self.white:
log_sqdiag_Lp = tf.stack([tf.log(tf.square(tf.matrix_diag_part(self.Lu_tiled[d]))) for d in range(self.num_outputs)],axis=0) #DxM
logdet_pcov = tf.reduce_sum(log_sqdiag_Lp,axis=1)[:,None] #Dx1
else:
logdet_pcov = 0
return logdet_pcov, logdet_qcov, mahalanobis, trace
def _expectation(p, kern, feat, none1, none2, nghp=None):
"""
Compute the expectation:
<K_{X, Z}>_p(X)
- K_{.,.} :: RBF kernel
:return: NxM
"""
with params_as_tensors_for(kern), params_as_tensors_for(feat):
# use only active dimensions
Xcov = kern._slice_cov(p.cov)
Z, Xmu = kern._slice(feat.Z, p.mu)
D = tf.shape(Xmu)[1]
if kern.ARD:
lengthscales = kern.lengthscales
else:
lengthscales = tf.zeros(
(D, ), dtype=settings.tf_float) + kern.lengthscales
chol_L_plus_Xcov = tf.cholesky(tf.matrix_diag(lengthscales**2) +
Xcov) # NxDxD
all_diffs = tf.transpose(Z) - tf.expand_dims(Xmu, 2) # NxDxM
exponent_mahalanobis = tf.matrix_triangular_solve(chol_L_plus_Xcov,
all_diffs,
lower=True) # NxDxM
exponent_mahalanobis = tf.reduce_sum(tf.square(exponent_mahalanobis),
1) # NxM
exponent_mahalanobis = tf.exp(-0.5 * exponent_mahalanobis) # NxM
sqrt_det_L = tf.reduce_prod(lengthscales)
sqrt_det_L_plus_Xcov = tf.exp(
tf.reduce_sum(tf.log(tf.matrix_diag_part(chol_L_plus_Xcov)),
axis=1))
determinants = sqrt_det_L / sqrt_det_L_plus_Xcov # N
return kern.variance * (determinants[:, None] * exponent_mahalanobis)
def call(self, x, mask=None):
if not self.built:
raise Exception("Secondary stat layer not built")
logging.debug('Secondary_stat parameter', type(x)) # Confirm the type of x is indeed tensor4D
cov_mat, x_mean = self.calculate_pre_cov(x)
# print('call during second {}'.format(self.eps))
# cov_mat += self.eps * self.b
if self.robust:
""" Implement the robust estimate, by apply an elementwise function to it. """
if K.backend() != 'tensorflow':
raise RuntimeError("Not support for theano now")
import tensorflow as tf
# with tf.device('/cpu:0'):
s, u = tf.self_adjoint_eig(cov_mat)
comp = tf.zeros_like(s)
s = tf.where(tf.less(s, comp), comp, s)
# s = tf.Print(s, [s], message='s:', summarize=self.out_dim)
inner = robust_estimate_eigenvalues(s, alpha=self.cov_alpha)
inner = tf.identity(inner, 'RobustEigen')
# inner = tf.Print(inner, [inner], message='inner:', summarize=self.out_dim)
cov_mat = tf.matmul(u, tf.matmul(tf.matrix_diag(inner), tf.transpose(u, [0, 2, 1])))
if self.cov_mode == 'mean' or self.cov_mode == 'pmean':
# Encode mean into Cov mat.
addition_array = K.mean(x_mean, axis=1, keepdims=True)
addition_array /= addition_array # Make it 1
if self.cov_mode == 'pmean':
x_mean = self.mean_p * x_mean
new_cov = K.concatenate(
[cov_mat + K.batch_dot(x_mean, K.permute_dimensions(x_mean, (0, 2, 1))), x_mean])
else:
new_cov = K.concatenate([cov_mat, x_mean])
tmp = K.concatenate([K.permute_dimensions(x_mean, (0, 2, 1)), addition_array])
new_cov = K.concatenate([new_cov, tmp], axis=1)
cov_mat = K.identity(new_cov, 'final_cov_mat')
return cov_mat
def _expectation(p, mean, none, kern, feat, nghp=None):
"""
Compute the expectation:
expectation[n] = <x_{n+1} K_{x_n, Z}>_p(x_{n:n+1})
- K_{.,.} :: RBF kernel
- p :: MarkovGaussian distribution (p.cov 2x(N+1)xDxD)
:return: NxDxM
"""
Xmu, Xcov = p.mu, p.cov
with tf.control_dependencies([tf.assert_equal(
tf.shape(Xmu)[1], tf.constant(kern.input_dim, settings.tf_int),
message="Currently cannot handle slicing in exKxz.")]):
Xmu = tf.identity(Xmu)
with params_as_tensors_for(kern, feat):
D = tf.shape(Xmu)[1]
lengthscales = kern.lengthscales if kern.ARD \
else tf.zeros((D,), dtype=settings.float_type) + kern.lengthscales
chol_L_plus_Xcov = tf.cholesky(tf.matrix_diag(lengthscales ** 2) + Xcov[0, :-1]) # NxDxD
all_diffs = tf.transpose(feat.Z) - tf.expand_dims(Xmu[:-1], 2) # NxDxM
sqrt_det_L = tf.reduce_prod(lengthscales)
sqrt_det_L_plus_Xcov = tf.exp(tf.reduce_sum(tf.log(tf.matrix_diag_part(chol_L_plus_Xcov)), axis=1))
determinants = sqrt_det_L / sqrt_det_L_plus_Xcov # N
exponent_mahalanobis = tf.cholesky_solve(chol_L_plus_Xcov, all_diffs) # NxDxM
non_exponent_term = tf.matmul(Xcov[1, :-1], exponent_mahalanobis, transpose_a=True)
non_exponent_term = tf.expand_dims(Xmu[1:], 2) + non_exponent_term # NxDxM
exponent_mahalanobis = tf.reduce_sum(all_diffs * exponent_mahalanobis, 1) # NxM
exponent_mahalanobis = tf.exp(-0.5 * exponent_mahalanobis) # NxM
return kern.variance * (determinants[:, None] * exponent_mahalanobis)[:, None, :] * non_exponent_term
def get_feature(input_q, input_a, att_W,index):
h_q, w = int(input_q.get_shape()[1]), int(input_q.get_shape()[2])
h_a = int(input_a.get_shape()[1])
output_q = max_pooling(input_q)
reshape_q = tf.expand_dims(output_q, 1)
reshape_q = tf.tile(reshape_q, [1, h_a, 1])
reshape_q = tf.reshape(reshape_q, [-1, w])
reshape_a = tf.reshape(input_a, [-1, w])
M = tf.tanh(tf.add(tf.matmul(reshape_q, tf.squeeze(att_W['Wqm'][:,:,index])), tf.matmul(reshape_a, tf.squeeze(att_W['Wam'][:,:,index]))))
M = tf.matmul(M, tf.expand_dims(att_W['Wms'][:,index],-1))
S = tf.reshape(M, [-1, h_a])
S = tf.nn.softmax(S)
S_diag = tf.matrix_diag(S)
attention_a = tf.matmul(S_diag, input_a)
attention_a = tf.reshape(attention_a, [-1, h_a, w])
output_a = max_pooling(attention_a)
return tf.tanh(output_q), tf.tanh(output_a)
def _attention_module(self, query, key, value, unit, in_kp):
with tf.variable_scope('attention', reuse=True):
query = tf.layers.dense(
query,
unit,
name='qk_map',
activation=tf.nn.relu,
use_bias=False,
kernel_initializer=self.u_init,
reuse=tf.AUTO_REUSE,
kernel_regularizer=tf.contrib.layers.l2_regularizer(0.003))
query = tf.nn.dropout(query, in_kp)
key = tf.layers.dense(
key,
self.emb_size,
name='qk_map',
activation=tf.nn.relu,
use_bias=False,
kernel_initializer=self.u_init,
reuse=tf.AUTO_REUSE,
kernel_regularizer=tf.contrib.layers.l2_regularizer(0.003))
key = tf.nn.dropout(key, in_kp)
score = tf.matmul(query, tf.transpose(key, [0, 2, 1])) / math.sqrt(
self.emb_size) # [B,T,T]
#masks the diagonal of the affinity matrix
a_mask = tf.ones([tf.shape(score)[1], tf.shape(score)[2]])
a_mask = a_mask - tf.matrix_diag(tf.ones([tf.shape(score)[1]]))
a_mask = tf.expand_dims(a_mask, [0])
a_mask = tf.tile(a_mask, [tf.shape(score)[0], 1, 1])
score *= a_mask
score = tf.nn.softmax(score, axis=2)
output = tf.matmul(score, value)
return output
def get_g(self, h_stack, x_t): """ calculate y_t ~ g(*|h_t-1, x_t) h_stack.shape = (n_particles, batch_size, Dh) x_t.shape = (n_particles, batch_size, Dx) """ x_t_ft = self.get_x_ft(x_t) # x_t_ft.shape = (n_particles, batch_size, Dx_1) with tf.variable_scope(self.variable_scope + '/get_g'): h_x_concat = tf.concat((h_stack, x_t_ft), axis = 2, name = 'h_x_concat') mu = fully_connected(h_x_concat, self.Dy, weights_initializer=xavier_initializer(uniform=False), activation_fn = None, reuse = tf.AUTO_REUSE, scope = "mu") # mu.shape = (n_paticles, batch_size, Dx) sigma = fully_connected(h_x_concat, self.Dy, weights_initializer=xavier_initializer(uniform=False), biases_initializer=tf.constant_initializer(0.6), activation_fn = tf.nn.softplus, reuse = tf.AUTO_REUSE, scope = "sigma") + self.sigma_cons # sigma.shape = (n_paticles, batch_size, Dx) g = tfd.MultivariateNormalFullCovariance(loc = mu, covariance_matrix = tf.matrix_diag(sigma), name = "g") return g
def build_likelihood(self, Z, kern, kern_t, give_KL=True):
"""
:param Z: inducing points
:param kern: kernel for the q(X)
:param kern_t: kernel for the p(X)
:param give_KL:
:return:
"""
# "The Dynamical Variational GP-LVM for Sequence Data" part in sec 3.3 of Andreas Damianou's Phd thesis.
#########################################
Kxx = kern_t.K(self.t) + tf.eye(self.num_data, dtype=float_type) * 1e-6 # N x N, prior covariance for p(X)
Lx = tf.cholesky(Kxx)
Lambda = tf.matrix_diag(tf.transpose(self.X_variational_var)) # Q x N x N, prior covariance for q(X)
tmp = tf.eye(self.num_data, dtype=float_type) + tf.einsum('ijk,kl->ijl', tf.einsum('ij,kil->kjl', Lx, Lambda), Lx) # I + Lx^T x Lambda x Lx in batch mode
Ltmp = tf.cholesky(tmp) # Q x N x N
tmp2 = tf.matrix_triangular_solve(Ltmp, tf.tile(tf.expand_dims(tf.transpose(Lx), 0), tf.stack([self.num_latent, 1, 1])))
S_full = tf.einsum('ijk,ijl->ikl', tmp2, tmp2) # Q x N x N
S = tf.transpose(tf.matrix_diag_part(S_full)) # N x Q, marginal distribution of multivariate normal, from column-wise to row-wise.
mu = tf.matmul(Kxx, self.X_variational_mean) # N x Q
##########################################
psi0 = tf.reduce_sum(kern.eKdiag(mu, S), 0) # N
psi1 = kern.eKxz(Z, mu, S) # N x M
psi2 = tf.reduce_sum(kern.eKzxKxz(Z, mu, S), 0) # N x M x M
# compute the KL[q(X) || p(X)]
NQ = tf.cast(tf.size(mu), float_type)
if give_KL:
KL = -0.5 * NQ
KL += tf.reduce_sum(tf.log(tf.matrix_diag_part(Ltmp))) # trace tricks
KL += 0.5 * tf.reduce_sum(tf.trace(tf.cholesky_solve(tf.tile(tf.expand_dims(Lx, 0),
tf.stack([self.num_latent, 1, 1])) , S_full + tf.einsum('ji,ki->ijk', mu, mu))))
return KL, psi0, psi1, psi2
else:
return psi0, psi1, psi2
def build_predict_fs(self, Xnew, full_cov=False):
"""
The posterior variance of F is given by
q(f) = N(f | K alpha + mean, [K^-1 + diag(lambda**2)]^-1)
Here we project this to F*, the values of the GP at Xnew which is given
by
q(F*) = N ( F* | K_{*F} alpha + mean, K_{**} - K_{*f}[K_{ff} +
diag(lambda**-2)]^-1 K_{f*} )
"""
f_means, f_vars = [], []
for d in range(self.D):
# compute kernel things
Kx = self.kerns[d].K(self.X, Xnew)
K = self.kerns[d].K(self.X)
# predictive mean
f_mean = tf.matmul(Kx, self.q_alpha[d, :, :],
transpose_a=True) + self.mean_functions[d](Xnew)
# predictive var
A = K + tf.matrix_diag(
tf.transpose(1. / tf.square(self.q_lambda[d, :, :])))
L = tf.cholesky(A)
Kx_tiled = tf.tile(tf.expand_dims(Kx, 0),
[self.num_latent.value, 1, 1])
LiKx = tf.matrix_triangular_solve(L, Kx_tiled)
if full_cov:
f_var = self.kerns[d].K(Xnew) - tf.matmul(
LiKx, LiKx, transpose_a=True)
else:
f_var = self.kerns[d].Kdiag(Xnew) - tf.reduce_sum(
tf.square(LiKx), 1)
f_means.append(f_mean)
f_vars.append(tf.transpose(f_var))
return tf.stack(f_means), tf.stack(f_vars)
