# Source code for pyro.ops.gaussian

import math

import torch
from torch.distributions.utils import lazy_property

[docs]class Gaussian:
"""
Non-normalized Gaussian distribution.

This represents an arbitrary semidefinite quadratic function, which can be
interpreted as a rank-deficient scaled Gaussian distribution. The precision
matrix may have zero eigenvalues, thus it may be impossible to work
directly with the covariance matrix.

:param torch.Tensor log_normalizer: a normalization constant, which is mainly used to keep
track of normalization terms during contractions.
:param torch.Tensor info_vec: information vector, which is a scaled version of the mean
info_vec = precision @ mean. We use this represention to make gaussian contraction
fast and stable.
:param torch.Tensor precision: precision matrix of this gaussian.
"""
def __init__(self, log_normalizer, info_vec, precision):
# NB: using info_vec instead of mean to deal with rank-deficient problem
assert info_vec.dim() >= 1
assert precision.dim() >= 2
assert precision.shape[-2:] == info_vec.shape[-1:] * 2
self.log_normalizer = log_normalizer
self.info_vec = info_vec
self.precision = precision

[docs]    def dim(self):
return self.info_vec.size(-1)

[docs]    @lazy_property
def batch_shape(self):
self.info_vec.shape[:-1],
self.precision.shape[:-2])

[docs]    def expand(self, batch_shape):
n = self.dim()
log_normalizer = self.log_normalizer.expand(batch_shape)
info_vec = self.info_vec.expand(batch_shape + (n,))
precision = self.precision.expand(batch_shape + (n, n))
return Gaussian(log_normalizer, info_vec, precision)

[docs]    def reshape(self, batch_shape):
n = self.dim()
log_normalizer = self.log_normalizer.reshape(batch_shape)
info_vec = self.info_vec.reshape(batch_shape + (n,))
precision = self.precision.reshape(batch_shape + (n, n))
return Gaussian(log_normalizer, info_vec, precision)

[docs]    def __getitem__(self, index):
"""
Index into the batch_shape of a Gaussian.
"""
assert isinstance(index, tuple)
log_normalizer = self.log_normalizer[index]
info_vec = self.info_vec[index + (slice(None),)]
precision = self.precision[index + (slice(None), slice(None))]
return Gaussian(log_normalizer, info_vec, precision)

[docs]    @staticmethod
def cat(parts, dim=0):
"""
Concatenate a list of Gaussians along a given batch dimension.
"""
if dim < 0:
dim += len(parts[0].batch_shape)
args = [torch.cat([getattr(g, attr) for g in parts], dim=dim)
for attr in ["log_normalizer", "info_vec", "precision"]]
return Gaussian(*args)

"""
"""
lr = (left, right)
log_normalizer = self.log_normalizer
precision = pad(self.precision, lr + lr)
return Gaussian(log_normalizer, info_vec, precision)

[docs]    def event_permute(self, perm):
"""
Permute along event dimension.
"""
assert isinstance(perm, torch.Tensor)
assert perm.shape == (self.dim(),)
info_vec = self.info_vec[..., perm]
precision = self.precision[..., perm][..., perm, :]
return Gaussian(self.log_normalizer, info_vec, precision)

"""
Adds two Gaussians in log-density space.
"""
assert isinstance(other, Gaussian)
assert self.dim() == other.dim()
return Gaussian(self.log_normalizer + other.log_normalizer,
self.info_vec + other.info_vec,
self.precision + other.precision)

[docs]    def log_density(self, value):
"""
Evaluate the log density of this Gaussian at a point value::

-0.5 * value.T @ precision @ value + value.T @ info_vec + log_normalizer

This is mainly used for testing.
"""
if value.size(-1) == 0:
return self.log_normalizer.expand(batch_shape)
result = (-0.5) * self.precision.matmul(value.unsqueeze(-1)).squeeze(-1)
result = result + self.info_vec
result = (value * result).sum(-1)
return result + self.log_normalizer

[docs]    def condition(self, value):
"""
Condition this Gaussian on a trailing subset of its state.
This should satisfy::

g.condition(y).dim() == g.dim() - y.size(-1)

Note that since this is a non-normalized Gaussian, we include the
density of y in the result. Thus :meth:condition is similar to a
functools.partial binding of arguments::

left = x[..., :n]
right = x[..., n:]
g.log_density(x) == g.condition(right).log_density(left)
"""
assert isinstance(value, torch.Tensor)
assert value.size(-1) <= self.info_vec.size(-1)

n = self.dim() - value.size(-1)
info_a = self.info_vec[..., :n]
info_b = self.info_vec[..., n:]
P_aa = self.precision[..., :n, :n]
P_ab = self.precision[..., :n, n:]
P_bb = self.precision[..., n:, n:]
b = value

info_vec = info_a - P_ab.matmul(b.unsqueeze(-1)).squeeze(-1)
precision = P_aa
log_normalizer = (self.log_normalizer +
-0.5 * P_bb.matmul(b.unsqueeze(-1)).squeeze(-1).mul(b).sum(-1) +
b.mul(info_b).sum(-1))
return Gaussian(log_normalizer, info_vec, precision)

[docs]    def marginalize(self, left=0, right=0):
"""
Marginalizing out variables on either side of the event dimension::

g.marginalize(left=n).event_logsumexp() = g.logsumexp()
g.marginalize(right=n).event_logsumexp() = g.logsumexp()

and for data x:

g.condition(x).event_logsumexp()
= g.marginalize(left=g.dim() - x.size(-1)).log_density(x)
"""
if left == 0 and right == 0:
return self
if left > 0 and right > 0:
raise NotImplementedError
n = self.dim()
n_b = left + right
a = slice(left, n - right)  # preserved
b = slice(None, left) if left else slice(n - right, None)

P_aa = self.precision[..., a, a]
P_ba = self.precision[..., b, a]
P_bb = self.precision[..., b, b]
P_b = P_bb.cholesky()
P_a = P_ba.triangular_solve(P_b, upper=False).solution
P_at = P_a.transpose(-1, -2)
precision = P_aa - P_at.matmul(P_a)

info_a = self.info_vec[..., a]
info_b = self.info_vec[..., b]
b_tmp = info_b.unsqueeze(-1).triangular_solve(P_b, upper=False).solution
info_vec = info_a - P_at.matmul(b_tmp).squeeze(-1)

log_normalizer = (self.log_normalizer +
0.5 * n_b * math.log(2 * math.pi) -
P_b.diagonal(dim1=-2, dim2=-1).log().sum(-1) +
0.5 * b_tmp.squeeze(-1).pow(2).sum(-1))
return Gaussian(log_normalizer, info_vec, precision)

[docs]    def event_logsumexp(self):
"""
Integrates out all latent state (i.e. operating on event dimensions).
"""
n = self.dim()
chol_P = self.precision.cholesky()
chol_P_u = self.info_vec.unsqueeze(-1).triangular_solve(chol_P, upper=False).solution.squeeze(-1)
u_P_u = chol_P_u.pow(2).sum(-1)
return (self.log_normalizer + 0.5 * n * math.log(2 * math.pi) + 0.5 * u_P_u -
chol_P.diagonal(dim1=-2, dim2=-1).log().sum(-1))

[docs]class AffineNormal:
"""
Represents a conditional diagonal normal distribution over a random
variable Y whose mean is an affine function of a random variable X.
The likelihood of X is thus::

AffineNormal(matrix, loc, scale).condition(y).log_density(x)

which is equivalent to::

Normal(x @ matrix + loc, scale).to_event(1).log_prob(y)

:param torch.Tensor matrix: A transformation from X to Y.
Should have rightmost shape (x_dim, y_dim).
:param torch.Tensor loc: A constant offset for Y's mean.
Should have rightmost shape (y_dim,).
:param torch.Tensor scale: Standard deviation for Y.
Should have rightmost shape (y_dim,).
"""
def __init__(self, matrix, loc, scale):
assert loc.shape == scale.shape
x_dim, y_dim = matrix.shape[-2:]
self.matrix = matrix
self.loc = loc
self.scale = scale

[docs]    def condition(self, value):
"""
Condition on a Y value.

:param torch.Tensor value: A value of Y.
:return Gaussian: A gaussian likelihood over X.
"""
assert value.size(-1) == self.loc.size(-1)
prec_sqrt = self.matrix / self.scale.unsqueeze(-2)
precision = prec_sqrt.matmul(prec_sqrt.transpose(-1, -2))
delta = (value - self.loc) / self.scale
info_vec = prec_sqrt.matmul(delta.unsqueeze(-1)).squeeze(-1)
log_normalizer = (-0.5 * self.loc.size(-1) * math.log(2 * math.pi)
- 0.5 * delta.pow(2).sum(-1) - self.scale.log().sum(-1))
return Gaussian(log_normalizer, info_vec, precision)

[docs]def mvn_to_gaussian(mvn):
"""
Convert a MultivaiateNormal distribution to a Gaussian.

:param ~torch.distributions.MultivariateNormal mvn: A multivariate normal distribution.
:return: An equivalent Gaussian object.
:rtype: ~pyro.ops.gaussian.Gaussian
"""
assert isinstance(mvn, torch.distributions.MultivariateNormal)
n = mvn.loc.size(-1)
precision = mvn.precision_matrix
info_vec = precision.matmul(mvn.loc.unsqueeze(-1)).squeeze(-1)
log_normalizer = (-0.5 * n * math.log(2 * math.pi) +
-0.5 * (info_vec * mvn.loc).sum(-1) -
mvn.scale_tril.diagonal(dim1=-2, dim2=-1).log().sum(-1))
return Gaussian(log_normalizer, info_vec, precision)

[docs]def matrix_and_mvn_to_gaussian(matrix, mvn):
"""
Convert a noisy affine function to a Gaussian. The noisy affine function is defined as::

y = x @ matrix + mvn.sample()

:param ~torch.Tensor matrix: A matrix with rightmost shape (x_dim, y_dim).
:param ~torch.distributions.MultivariateNormal mvn: A multivariate normal distribution.
:return: A Gaussian with broadcasted batch shape and .dim() == x_dim + y_dim.
:rtype: ~pyro.ops.gaussian.Gaussian
"""
assert (isinstance(mvn, torch.distributions.MultivariateNormal) or
(isinstance(mvn, torch.distributions.Independent) and
isinstance(mvn.base_dist, torch.distributions.Normal)))
assert isinstance(matrix, torch.Tensor)
x_dim, y_dim = matrix.shape[-2:]
assert mvn.event_shape == (y_dim,)
matrix = matrix.expand(batch_shape + (x_dim, y_dim))
mvn = mvn.expand(batch_shape)

# Handle diagonal normal distributions as an efficient special case.
if isinstance(mvn, torch.distributions.Independent):
return AffineNormal(matrix, mvn.base_dist.loc, mvn.base_dist.scale)

y_gaussian = mvn_to_gaussian(mvn)
P_yy = y_gaussian.precision
neg_P_xy = matrix.matmul(P_yy)
P_xy = -neg_P_xy
P_yx = P_xy.transpose(-1, -2)
P_xx = neg_P_xy.matmul(matrix.transpose(-1, -2))
precision = torch.cat([torch.cat([P_xx, P_xy], -1),
torch.cat([P_yx, P_yy], -1)], -2)
info_y = y_gaussian.info_vec
info_x = -matrix.matmul(info_y.unsqueeze(-1)).squeeze(-1)
info_vec = torch.cat([info_x, info_y], -1)
log_normalizer = y_gaussian.log_normalizer

result = Gaussian(log_normalizer, info_vec, precision)
assert result.batch_shape == batch_shape
assert result.dim() == x_dim + y_dim
return result

[docs]def gaussian_tensordot(x, y, dims=0):
"""
Computes the integral over two gaussians:

(x @ y)(a,c) = log(integral(exp(x(a,b) + y(b,c)), b)),

where x is a gaussian over variables (a,b), y is a gaussian over variables
(b,c), (a,b,c) can each be sets of zero or more variables, and dims is the size of b.

:param x: a Gaussian instance
:param y: a Gaussian instance
:param dims: number of variables to contract
"""
assert isinstance(x, Gaussian)
assert isinstance(y, Gaussian)
na = x.dim() - dims
nb = dims
nc = y.dim() - dims
assert na >= 0
assert nb >= 0
assert nc >= 0

Paa, Pba, Pbb = x.precision[..., :na, :na], x.precision[..., na:, :na], x.precision[..., na:, na:]
Qbb, Qbc, Qcc = y.precision[..., :nb, :nb], y.precision[..., :nb, nb:], y.precision[..., nb:, nb:]
xa, xb = x.info_vec[..., :na], x.info_vec[..., na:]  # x.precision @ x.mean
yb, yc = y.info_vec[..., :nb], y.info_vec[..., nb:]  # y.precision @ y.mean

precision = pad(Paa, (0, nc, 0, nc)) + pad(Qcc, (na, 0, na, 0))
log_normalizer = x.log_normalizer + y.log_normalizer
if nb > 0:
b = xb + yb

# Pbb + Qbb needs to be positive definite, so that we can malginalize out b (to have a finite integral)
L = torch.cholesky(Pbb + Qbb)
LinvB = torch.triangular_solve(B, L, upper=False)[0]
LinvBt = LinvB.transpose(-2, -1)
Linvb = torch.triangular_solve(b.unsqueeze(-1), L, upper=False)[0]

precision = precision - torch.matmul(LinvBt, LinvB)
# NB: precision might not be invertible for getting mean = precision^-1 @ info_vec
if na + nc > 0:
info_vec = info_vec - torch.matmul(LinvBt, Linvb).squeeze(-1)
logdet = torch.diagonal(L, dim1=-2, dim2=-1).log().sum(-1)
diff = 0.5 * nb * math.log(2 * math.pi) + 0.5 * Linvb.squeeze(-1).pow(2).sum(-1) - logdet
log_normalizer = log_normalizer + diff

return Gaussian(log_normalizer, info_vec, precision)