def sample_n(self, n):
if self._is_gpu:
eps = cuda.cupy.random.standard_normal(
(n,)+self.loc.shape, dtype=self.loc.dtype)
else:
eps = numpy.random.standard_normal(
(n,)+self.loc.shape).astype(numpy.float32)
noise = repeat.repeat(
expand_dims.expand_dims(self.scale, axis=0), n, axis=0) * eps
noise += repeat.repeat(expand_dims.expand_dims(
self.loc, axis=0), n, axis=0)
return noise
def sample_n(self, n):
if self._is_gpu:
eps = cuda.cupy.random.standard_normal((n, ) + self.loc.shape,
dtype=self.loc.dtype)
else:
eps = numpy.random.standard_normal((n, ) + self.loc.shape).astype(
numpy.float32)
noise = repeat.repeat(
expand_dims.expand_dims(self.scale, axis=0), n, axis=0) * eps
noise += repeat.repeat(expand_dims.expand_dims(self.loc, axis=0),
n,
axis=0)
return noise
def sample_n(self, n):
if self._is_gpu:
eps = cuda.cupy.random.standard_normal(
(n,)+self.loc.shape+(1,), dtype=self.loc.dtype)
else:
eps = numpy.random.standard_normal(
(n,)+self.loc.shape+(1,)).astype(numpy.float32)
noise = matmul.matmul(repeat.repeat(
expand_dims.expand_dims(self.scale_tril, axis=0), n, axis=0), eps)
noise = squeeze.squeeze(noise, axis=-1)
noise += repeat.repeat(expand_dims.expand_dims(
self.loc, axis=0), n, axis=0)
return noise
def sample_n(self, n):
if self._is_gpu:
eps = cuda.cupy.random.standard_normal(
(n,)+self.loc.shape+(1,), dtype=self.loc.dtype)
else:
eps = numpy.random.standard_normal(
(n,)+self.loc.shape+(1,)).astype(numpy.float32)
noise = matmul.matmul(repeat.repeat(
expand_dims.expand_dims(self.scale_tril, axis=0), n, axis=0), eps)
noise = squeeze.squeeze(noise, axis=-1)
noise += repeat.repeat(expand_dims.expand_dims(
self.loc, axis=0), n, axis=0)
return noise
def covariance(self):
""" The covariance of the independent distribution.
By definition, the covariance of the new
distribution becomes block diagonal matrix. Let
:math:`\\Sigma_{\\mathbf{x}}` be the covariance matrix of the original
random variable :math:`\\mathbf{x} \\in \\mathbb{R}^d`, and
:math:`\\mathbf{x}^{(1)}, \\mathbf{x}^{(2)}, \\cdots \\mathbf{x}^{(m)}`
be the :math:`m` i.i.d. random variables, new covariance matrix
:math:`\\Sigma_{\\mathbf{y}}` of :math:`\\mathbf{y} =
[\\mathbf{x}^{(1)}, \\mathbf{x}^{(2)}, \\cdots, \\mathbf{x}^{(m)}] \\in
\\mathbb{R}^{md}` can be written as
.. math::
\\left[\\begin{array}{ccc}
\\Sigma_{\\mathbf{x}^{1}} & & 0 \\\\
& \\ddots & \\\\
0 & & \\Sigma_{\\mathbf{x}^{m}}
\\end{array} \\right].
Note that this relationship holds only if the covariance matrix of the
original distribution is given analytically.
Returns:
~chainer.Variable: The covariance of the distribution.
"""
num_repeat = array.size_of_shape(
self.distribution.batch_shape[-self.reinterpreted_batch_ndims:])
dim = array.size_of_shape(self.distribution.event_shape)
cov = repeat.repeat(reshape.reshape(self.distribution.covariance,
((self.batch_shape) +
(1, num_repeat, dim, dim))),
num_repeat,
axis=-4)
cov = reshape.reshape(
transpose.transpose(cov,
axes=(tuple(range(len(self.batch_shape))) +
(-4, -2, -3, -1))),
self.batch_shape + (num_repeat * dim, num_repeat * dim))
block_indicator = self.xp.reshape(
self._block_indicator,
tuple([1] * len(self.batch_shape)) + self._block_indicator.shape)
return cov * block_indicator
def covariance(self):
""" The covariance of the independent distribution.
By definition, the covariance of the new
distribution becomes block diagonal matrix. Let
:math:`\\Sigma_{\\mathbf{x}}` be the covariance matrix of the original
random variable :math:`\\mathbf{x} \\in \\mathbb{R}^d`, and
:math:`\\mathbf{x}^{(1)}, \\mathbf{x}^{(2)}, \\cdots \\mathbf{x}^{(m)}`
be the :math:`m` i.i.d. random variables, new covariance matrix
:math:`\\Sigma_{\\mathbf{y}}` of :math:`\\mathbf{y} =
[\\mathbf{x}^{(1)}, \\mathbf{x}^{(2)}, \\cdots, \\mathbf{x}^{(m)}] \\in
\\mathbb{R}^{md}` can be written as
.. math::
\\left[\\begin{array}{ccc}
\\Sigma_{\\mathbf{x}^{1}} & & 0 \\\\
& \\ddots & \\\\
0 & & \\Sigma_{\\mathbf{x}^{m}}
\\end{array} \\right].
Note that this relationship holds only if the covariance matrix of the
original distribution is given analytically.
Returns:
~chainer.Variable: The covariance of the distribution.
"""
num_repeat = array.size_of_shape(
self.distribution.batch_shape[-self.reinterpreted_batch_ndims:])
dim = array.size_of_shape(self.distribution.event_shape)
cov = repeat.repeat(
reshape.reshape(
self.distribution.covariance,
((self.batch_shape) + (1, num_repeat, dim, dim))),
num_repeat, axis=-4)
cov = reshape.reshape(
transpose.transpose(
cov, axes=(
tuple(range(len(self.batch_shape))) + (-4, -2, -3, -1))),
self.batch_shape + (num_repeat * dim, num_repeat * dim))
block_indicator = self.xp.reshape(
self._block_indicator,
tuple([1] * len(self.batch_shape)) + self._block_indicator.shape)
return cov * block_indicator