def _call_reshape_input_output(self, fn, x, extra_kwargs=None):
"""Calls `fn`, appropriately reshaping its input `x` and output."""
# Note: we take `extra_kwargs` as a dict rather than `**extra_kwargs`
# because it is possible the user provided extra kwargs would itself
# have `fn` and/or `x` as a key.
with tf.control_dependencies(self._runtime_assertions +
self._validate_sample_arg(x)):
sample_shape, static_sample_shape = self._sample_shape(x)
old_shape = tf.concat([
sample_shape,
self.distribution.batch_shape_tensor(),
self.event_shape_tensor(),
],
axis=0)
x_reshape = tf.reshape(x, old_shape)
result = fn(x_reshape, **
extra_kwargs) if extra_kwargs else fn(x_reshape)
new_shape = tf.concat([
sample_shape,
self._batch_shape_unexpanded,
],
axis=0)
result = tf.reshape(result, new_shape)
if (tensorshape_util.rank(static_sample_shape) is not None
and tensorshape_util.rank(self.batch_shape) is not None):
new_shape = tensorshape_util.concatenate(
static_sample_shape, self.batch_shape)
tensorshape_util.set_shape(result, new_shape)
return result
def _sparse_tensor_dense_matmul(sp_a, b, **kwargs):
"""Returns (batched) matmul of a SparseTensor with a Tensor.
Args:
sp_a: `SparseTensor` representing a (batch of) matrices.
b: `Tensor` representing a (batch of) matrices, with the same batch shape of
`sp_a`. The shape must be compatible with the shape of `sp_a` and kwargs.
**kwargs: Keyword arguments to `tf.sparse_tensor_dense_matmul`.
Returns:
product: A dense (batch of) matrix-shaped Tensor of the same batch shape and
dtype as `sp_a` and `b`. If `sp_a` or `b` is adjointed through `kwargs` then
the shape is adjusted accordingly.
"""
batch_shape = _get_shape(sp_a)[:-2]
# Reshape the SparseTensor into a rank 3 SparseTensors, with the
# batch shape flattened to a single dimension. If the batch rank is 0, then
# we add a batch dimension of rank 1.
sp_a = tf.sparse.reshape(sp_a,
tf.concat([[-1], _get_shape(sp_a)[-2:]], axis=0))
# Reshape b to stack the batch dimension along the rows.
b = tf.reshape(b, tf.concat([[-1], _get_shape(b)[-1:]], axis=0))
# Convert the SparseTensor to a matrix in block diagonal form with blocks of
# matrices [M, N]. This allow us to use tf.sparse_tensor_dense_matmul which
# only accepts rank 2 (Sparse)Tensors.
out = tf.sparse.sparse_dense_matmul(_sparse_block_diag(sp_a), b, **kwargs)
# Finally retrieve the original batch shape from the resulting rank 2 Tensor.
# Note that we avoid inferring the final shape from `sp_a` or `b` because we
# might have transposed one or both of them.
return tf.reshape(
out,
tf.concat([batch_shape, [-1], _get_shape(out)[-1:]], axis=0))
def cholesky_concat(chol, cols, name=None):
"""Concatenates `chol @ chol.T` with additional rows and columns.
This operation is conceptually identical to:
```python
def cholesky_concat_slow(chol, cols): # cols shaped (n + m) x m = z x m
mat = tf.matmul(chol, chol, adjoint_b=True) # batch of n x n
# Concat columns.
mat = tf.concat([mat, cols[..., :tf.shape(mat)[-2], :]], axis=-1) # n x z
# Concat rows.
mat = tf.concat([mat, tf.linalg.matrix_transpose(cols)], axis=-2) # z x z
return tf.linalg.cholesky(mat)
```
but whereas `cholesky_concat_slow` would cost `O(z**3)` work,
`cholesky_concat` only costs `O(z**2 + m**3)` work.
The resulting (implicit) matrix must be symmetric and positive definite.
Thus, the bottom right `m x m` must be self-adjoint, and we do not require a
separate `rows` argument (which can be inferred from `conj(cols.T)`).
Args:
chol: Cholesky decomposition of `mat = chol @ chol.T`.
cols: The new columns whose first `n` rows we would like concatenated to the
right of `mat = chol @ chol.T`, and whose conjugate transpose we would
like concatenated to the bottom of `concat(mat, cols[:n,:])`. A `Tensor`
with final dims `(n+m, m)`. The first `n` rows are the top right rectangle
(their conjugate transpose forms the bottom left), and the bottom `m x m`
is self-adjoint.
name: Optional name for this op.
Returns:
chol_concat: The Cholesky decomposition of:
```
[ [ mat cols[:n, :] ]
[ conj(cols.T) ] ]
```
"""
with tf.name_scope(name or 'cholesky_extend'):
dtype = dtype_util.common_dtype([chol, cols], dtype_hint=tf.float32)
chol = tf.convert_to_tensor(chol, name='chol', dtype=dtype)
cols = tf.convert_to_tensor(cols, name='cols', dtype=dtype)
n = prefer_static.shape(chol)[-1]
mat_nm, mat_mm = cols[..., :n, :], cols[..., n:, :]
solved_nm = linear_operator_util.matrix_triangular_solve_with_broadcast(
chol, mat_nm)
lower_right_mm = tf.linalg.cholesky(
mat_mm - tf.matmul(solved_nm, solved_nm, adjoint_a=True))
lower_left_mn = tf.math.conj(tf.linalg.matrix_transpose(solved_nm))
out_batch = prefer_static.shape(solved_nm)[:-2]
chol = tf.broadcast_to(
chol,
tf.concat([out_batch, prefer_static.shape(chol)[-2:]], axis=0))
top_right_zeros_nm = tf.zeros_like(solved_nm)
return tf.concat([
tf.concat([chol, top_right_zeros_nm], axis=-1),
tf.concat([lower_left_mn, lower_right_mm], axis=-1)
],
axis=-2)
def _variance(self):
with tf.control_dependencies(self._runtime_assertions):
probs = self._marginal_hidden_probs()
# probs :: num_steps batch_shape num_states
means = self._observation_distribution.mean()
# means :: observation_batch_shape[:-1] num_states
# observation_event_shape
means_shape = tf.concat([
self.batch_shape_tensor(), [self._num_states],
self._observation_distribution.event_shape_tensor()
],
axis=0)
means = tf.broadcast_to(means, means_shape)
# means :: batch_shape num_states observation_event_shape
observation_event_shape = (
self._observation_distribution.event_shape_tensor())
batch_size = tf.reduce_prod(self.batch_shape_tensor())
flat_probs_shape = [self._num_steps, batch_size, self._num_states]
flat_means_shape = [
batch_size, 1, self._num_states,
tf.reduce_prod(observation_event_shape)
]
flat_probs = tf.reshape(probs, flat_probs_shape)
# flat_probs :: num_steps batch_size num_states
flat_means = tf.reshape(means, flat_means_shape)
# flat_means :: batch_size 1 num_states observation_event_size
flat_mean = tf.einsum("ijk,jmkl->jiml", flat_probs, flat_means)
# flat_mean :: batch_size num_steps 1 observation_event_size
variances = self._observation_distribution.variance()
variances = tf.broadcast_to(variances, means_shape)
# variances :: batch_shape num_states observation_event_shape
flat_variances = tf.reshape(variances, flat_means_shape)
# flat_variances :: batch_size 1 num_states observation_event_size
# For a mixture of n distributions with mixture probabilities
# p[i], and where the individual distributions have means and
# variances given by mean[i] and var[i], the variance of
# the mixture is given by:
#
# var = sum i=1..n p[i] * ((mean[i] - mean)**2 + var[i]**2)
flat_variance = tf.einsum("ijk,jikl->jil", flat_probs,
(flat_means - flat_mean)**2 +
flat_variances)
# flat_variance :: batch_size num_steps observation_event_size
unflat_mean_shape = tf.concat([
self.batch_shape_tensor(), [self._num_steps],
observation_event_shape
],
axis=0)
# returns :: batch_shape num_steps observation_event_shape
return tf.reshape(flat_variance, unflat_mean_shape)
def body(m, pchol, perm, matrix_diag):
"""Body of a single `tf.while_loop` iteration."""
# Here is roughly a numpy, non-batched version of what's going to happen.
# (See also Algorithm 1 of Harbrecht et al.)
# 1: maxi = np.argmax(matrix_diag[perm[m:]]) + m
# 2: maxval = matrix_diag[perm][maxi]
# 3: perm[m], perm[maxi] = perm[maxi], perm[m]
# 4: row = matrix[perm[m]][perm[m + 1:]]
# 5: row -= np.sum(pchol[:m][perm[m + 1:]] * pchol[:m][perm[m]]], axis=-2)
# 6: pivot = np.sqrt(maxval); row /= pivot
# 7: row = np.concatenate([[[pivot]], row], -1)
# 8: matrix_diag[perm[m:]] -= row**2
# 9: pchol[m, perm[m:]] = row
# Find the maximal position of the (remaining) permuted diagonal.
# Steps 1, 2 above.
permuted_diag = batch_gather(matrix_diag, perm[..., m:])
maxi = tf.argmax(permuted_diag, axis=-1,
output_type=tf.int64)[..., tf.newaxis]
maxval = batch_gather(permuted_diag, maxi)
maxi = maxi + m
maxval = maxval[..., 0]
# Update perm: Swap perm[...,m] with perm[...,maxi]. Step 3 above.
perm = _swap_m_with_i(perm, m, maxi)
# Step 4.
row = batch_gather(matrix, perm[..., m:m + 1], axis=-2)
row = batch_gather(row, perm[..., m + 1:])
# Step 5.
prev_rows = pchol[..., :m, :]
prev_rows_perm_m_onward = batch_gather(prev_rows, perm[...,
m + 1:])
prev_rows_pivot_col = batch_gather(prev_rows, perm[..., m:m + 1])
row -= tf.reduce_sum(prev_rows_perm_m_onward * prev_rows_pivot_col,
axis=-2)[..., tf.newaxis, :]
# Step 6.
pivot = tf.sqrt(maxval)[..., tf.newaxis, tf.newaxis]
# Step 7.
row = tf.concat([pivot, row / pivot], axis=-1)
# TODO(b/130899118): Pad grad fails with int64 paddings.
# Step 8.
paddings = tf.concat([
tf.zeros([prefer_static.rank(pchol) - 1, 2], dtype=tf.int32),
[[tf.cast(m, tf.int32), 0]]
],
axis=0)
diag_update = tf.pad(row**2, paddings=paddings)[..., 0, :]
reverse_perm = _invert_permutation(perm)
matrix_diag -= batch_gather(diag_update, reverse_perm)
# Step 9.
row = tf.pad(row, paddings=paddings)
# TODO(bjp): Defer the reverse permutation all-at-once at the end?
row = batch_gather(row, reverse_perm)
pchol_shape = pchol.shape
pchol = tf.concat([pchol[..., :m, :], row, pchol[..., m + 1:, :]],
axis=-2)
tensorshape_util.set_shape(pchol, pchol_shape)
return m + 1, pchol, perm, matrix_diag
def _log_prob(self, value):
with tf.control_dependencies(self._runtime_assertions):
# The argument `value` is a tensor of sequences of observations.
# `observation_batch_shape` is the shape of that tensor with the
# sequence part removed.
# `observation_batch_shape` is then broadcast to the full batch shape
# to give the `batch_shape` that defines the shape of the result.
observation_tensor_shape = tf.shape(value)
observation_batch_shape = observation_tensor_shape[:-1 - self.
_underlying_event_rank]
# value :: observation_batch_shape num_steps observation_event_shape
batch_shape = tf.broadcast_dynamic_shape(observation_batch_shape,
self.batch_shape_tensor())
log_init = tf.broadcast_to(
self._log_init,
tf.concat([batch_shape, [self._num_states]], axis=0))
# log_init :: batch_shape num_states
log_transition = self._log_trans
# `observation_event_shape` is the shape of each sequence of observations
# emitted by the model.
observation_event_shape = observation_tensor_shape[
-1 - self._underlying_event_rank:]
working_obs = tf.broadcast_to(
value, tf.concat([batch_shape, observation_event_shape],
axis=0))
# working_obs :: batch_shape observation_event_shape
r = self._underlying_event_rank
# Move index into sequence of observations to front so we can apply
# tf.foldl
working_obs = distribution_util.move_dimension(
working_obs, -1 - r, 0)[..., tf.newaxis]
# working_obs :: num_steps batch_shape underlying_event_shape
observation_probs = (
self._observation_distribution.log_prob(working_obs))
def forward_step(log_prev_step, log_prob_observation):
return _log_vector_matrix(
log_prev_step, log_transition) + log_prob_observation
fwd_prob = tf.foldl(forward_step,
observation_probs,
initializer=log_init)
# fwd_prob :: batch_shape num_states
log_prob = tf.reduce_logsumexp(fwd_prob, axis=-1)
# log_prob :: batch_shape
return log_prob
def _compute_quantiles():
"""Helper to build quantiles."""
# Omit {0, 1} since they might lead to Inf/NaN.
zero = tf.zeros([], dtype=dist.dtype)
edges = tf.linspace(zero, 1., quadrature_size + 3)[1:-1]
# Expand edges so its broadcast across batch dims.
edges = tf.reshape(
edges,
shape=tf.concat(
[[-1], tf.ones([batch_ndims], dtype=tf.int32)], axis=0))
quantiles = dist.quantile(edges)
# Cyclically permute left by one.
perm = tf.concat([tf.range(1, 1 + batch_ndims), [0]], axis=0)
quantiles = tf.transpose(a=quantiles, perm=perm)
return quantiles
def _sample_3d(self, n, seed=None):
"""Specialized inversion sampler for 3D."""
seed = SeedStream(seed, salt='von_mises_fisher_3d')
u_shape = tf.concat([[n], self._batch_shape_tensor()], axis=0)
z = tf.random.uniform(u_shape, seed=seed(), dtype=self.dtype)
# TODO(bjp): Higher-order odd dim analytic CDFs are available in [1], could
# be bisected for bounded sampling runtime (i.e. not rejection sampling).
# [1]: Inversion sampler via: https://ieeexplore.ieee.org/document/7347705/
# The inversion is: u = 1 + log(z + (1-z)*exp(-2*kappa)) / kappa
# We must protect against both kappa and z being zero.
safe_conc = tf.where(self.concentration > 0, self.concentration,
tf.ones_like(self.concentration))
safe_z = tf.where(z > 0, z, tf.ones_like(z))
safe_u = 1 + tf.reduce_logsumexp(
[tf.math.log(safe_z),
tf.math.log1p(-safe_z) - 2 * safe_conc],
axis=0) / safe_conc
# Limit of the above expression as kappa->0 is 2*z-1
u = tf.where(self.concentration > tf.zeros_like(safe_u), safe_u,
2 * z - 1)
# Limit of the expression as z->0 is -1.
u = tf.where(tf.equal(z, 0), -tf.ones_like(u), u)
if not self._allow_nan_stats:
u = tf.debugging.check_numerics(u, 'u in _sample_3d')
return u[..., tf.newaxis]
def _sample_n(self, n, seed=None):
# See https://en.wikipedia.org/wiki/Inverse_Gaussian_distribution or
# https://www.jstor.org/stable/2683801
concentration = tf.convert_to_tensor(self.concentration)
loc = tf.convert_to_tensor(self.loc)
seed = SeedStream(seed, 'inverse_gaussian')
shape = tf.concat(
[[n],
self._batch_shape_tensor(loc=loc, concentration=concentration)],
axis=0)
sampled_chi2 = (tf.random.normal(shape,
mean=0.,
stddev=1.,
seed=seed(),
dtype=self.dtype))**2.
sampled_uniform = tf.random.uniform(shape,
minval=0.,
maxval=1.,
seed=seed(),
dtype=self.dtype)
sampled = (loc + loc**2. * sampled_chi2 / (2. * concentration) - loc /
(2. * concentration) *
(4. * loc * concentration * sampled_chi2 +
(loc * sampled_chi2)**2)**0.5)
return tf.where(sampled_uniform <= loc / (loc + sampled), sampled,
loc**2 / sampled)
def _call_sample_n(self, sample_shape, seed, name, **kwargs):
# We override `_call_sample_n` rather than `_sample_n` so we can ensure that
# the result of `self.bijector.forward` is not modified (and thus caching
# works).
with self._name_and_control_scope(name):
sample_shape = tf.convert_to_tensor(sample_shape,
dtype=tf.int32,
name="sample_shape")
sample_shape, n = self._expand_sample_shape_to_vector(
sample_shape, "sample_shape")
distribution_kwargs, bijector_kwargs = self._kwargs_split_fn(
kwargs)
# First, generate samples. We will possibly generate extra samples in the
# event that we need to reinterpret the samples as part of the
# event_shape.
x = self._sample_n(n, seed, **distribution_kwargs)
# Next, we reshape `x` into its final form. We do this prior to the call
# to the bijector to ensure that the bijector caching works.
batch_event_shape = tf.shape(x)[1:]
final_shape = tf.concat([sample_shape, batch_event_shape], 0)
x = tf.reshape(x, final_shape)
# Finally, we apply the bijector's forward transformation. For caching to
# work, it is imperative that this is the last modification to the
# returned result.
y = self.bijector.forward(x, **bijector_kwargs)
y = self._set_sample_static_shape(y, sample_shape)
return y
def _mode(self, samples=None):
# Samples count can vary by batch member. Use map_fn to compute mode for
# each batch separately.
def _get_mode(samples):
# TODO(b/123985779): Switch to tf.unique_with_counts_v2 when exposed
count = gen_array_ops.unique_with_counts_v2(samples, axis=[0]).count
return tf.argmax(count)
if samples is None:
samples = tf.convert_to_tensor(self._samples)
num_samples = self._compute_num_samples(samples)
# Flatten samples for each batch.
if self._event_ndims == 0:
flattened_samples = tf.reshape(samples, [-1, num_samples])
mode_shape = self._batch_shape_tensor(samples)
else:
event_size = tf.reduce_prod(self._event_shape_tensor(samples))
mode_shape = tf.concat(
[self._batch_shape_tensor(samples),
self._event_shape_tensor(samples)],
axis=0)
flattened_samples = tf.reshape(samples, [-1, num_samples, event_size])
indices = tf.map_fn(_get_mode, flattened_samples, dtype=tf.int64)
full_indices = tf.stack(
[tf.range(tf.shape(indices)[0]),
tf.cast(indices, tf.int32)], axis=1)
mode = tf.gather_nd(flattened_samples, full_indices)
return tf.reshape(mode, mode_shape)
def _call_and_reshape_output(self,
fn,
event_shape_list=None,
static_event_shape_list=None,
extra_kwargs=None):
"""Calls `fn` and appropriately reshapes its output."""
# Note: we take `extra_kwargs` as a dict rather than `**extra_kwargs`
# because it is possible the user provided extra kwargs would itself
# have `fn`, `event_shape_list`, `static_event_shape_list` and/or
# `extra_kwargs` as keys.
with tf.control_dependencies(self._runtime_assertions):
if event_shape_list is None:
event_shape_list = [self._event_shape_tensor()]
if static_event_shape_list is None:
static_event_shape_list = [self.event_shape]
new_shape = tf.concat([self._batch_shape_unexpanded] +
event_shape_list,
axis=0)
result = tf.reshape(
fn(**extra_kwargs) if extra_kwargs else fn(), new_shape)
if (tensorshape_util.rank(self.batch_shape) is not None
and tensorshape_util.rank(self.event_shape) is not None):
event_shape = tf.TensorShape([])
for rss in static_event_shape_list:
event_shape = tensorshape_util.concatenate(
event_shape, rss)
static_shape = tensorshape_util.concatenate(
self.batch_shape, event_shape)
tensorshape_util.set_shape(result, static_shape)
return result
def _sample_n(self, n, seed=None):
low = tf.convert_to_tensor(self.low)
high = tf.convert_to_tensor(self.high)
shape = tf.concat(
[[n], self._batch_shape_tensor(low=low, high=high)], 0)
samples = tf.random.uniform(shape=shape, dtype=self.dtype, seed=seed)
return low + self._range(low=low, high=high) * samples
def _cdf(self, k):
# TODO(b/135263541): Improve numerical precision of categorical.cdf.
probs = self.probs_parameter()
num_categories = self._num_categories(probs)
k, probs = _broadcast_cat_event_and_params(
k, probs, base_dtype=dtype_util.base_dtype(self.dtype))
# Since the lowest number in the support is 0, any k < 0 should be zero in
# the output.
should_be_zero = k < 0
# Will use k as an index in the gather below, so clip it to {0,...,K-1}.
k = tf.clip_by_value(tf.cast(k, tf.int32), 0, num_categories - 1)
batch_shape = tf.shape(k)
# tf.gather(..., batch_dims=batch_dims) requires static batch_dims kwarg, so
# to handle the case where the batch shape is dynamic, flatten the batch
# dims (so we know batch_dims=1).
k_flat_batch = tf.reshape(k, [-1])
probs_flat_batch = tf.reshape(
probs, tf.concat(([-1], [num_categories]), axis=0))
cdf_flat = tf.gather(tf.cumsum(probs_flat_batch, axis=-1),
k_flat_batch[..., tf.newaxis],
batch_dims=1)
cdf = tf.reshape(cdf_flat, shape=batch_shape)
zero = np.array(0, dtype=dtype_util.as_numpy_dtype(cdf.dtype))
return tf.where(should_be_zero, zero, cdf)
def _swap_m_with_i(vecs, m, i):
"""Swaps `m` and `i` on axis -1. (Helper for pivoted_cholesky.)
Given a batch of int64 vectors `vecs`, scalar index `m`, and compatibly shaped
per-vector indices `i`, this function swaps elements `m` and `i` in each
vector. For the use-case below, these are permutation vectors.
Args:
vecs: Vectors on which we perform the swap, int64 `Tensor`.
m: Scalar int64 `Tensor`, the index into which the `i`th element is going.
i: Batch int64 `Tensor`, shaped like vecs.shape[:-1] + [1]; the index into
which the `m`th element is going.
Returns:
vecs: The updated vectors.
"""
vecs = tf.convert_to_tensor(vecs, dtype=tf.int64, name='vecs')
m = tf.convert_to_tensor(m, dtype=tf.int64, name='m')
i = tf.convert_to_tensor(i, dtype=tf.int64, name='i')
trailing_elts = tf.broadcast_to(
tf.range(m + 1,
prefer_static.shape(vecs, out_type=tf.int64)[-1]),
prefer_static.shape(vecs[..., m + 1:]))
trailing_elts = tf.where(tf.equal(trailing_elts, i),
tf.gather(vecs, [m], axis=-1), vecs[..., m + 1:])
# TODO(bjp): Could we use tensor_scatter_nd_update?
vecs_shape = vecs.shape
vecs = tf.concat([
vecs[..., :m],
tf.gather(vecs, i, batch_dims=int(prefer_static.rank(vecs)) - 1),
trailing_elts
],
axis=-1)
tensorshape_util.set_shape(vecs, vecs_shape)
return vecs
def _make_columnar(self, x):
"""Ensures non-scalar input has at least one column.
Example:
If `x = [1, 2, 3]` then the output is `[[1], [2], [3]]`.
If `x = [[1, 2, 3], [4, 5, 6]]` then the output is unchanged.
If `x = 1` then the output is unchanged.
Args:
x: `Tensor`.
Returns:
columnar_x: `Tensor` with at least two dimensions.
"""
if tensorshape_util.rank(x.shape) is not None:
if tensorshape_util.rank(x.shape) == 1:
x = x[tf.newaxis, :]
return x
shape = tf.shape(x)
maybe_expanded_shape = tf.concat([
shape[:-1],
distribution_util.pick_vector(tf.equal(tf.rank(x), 1), [1],
np.array([], dtype=np.int32)),
shape[-1:],
], 0)
return tf.reshape(x, maybe_expanded_shape)
def _marginal_hidden_probs(self):
"""Compute marginal pdf for each individual observable."""
initial_log_probs = tf.broadcast_to(
self._log_init,
tf.concat([self.batch_shape_tensor(), [self._num_states]], axis=0))
# initial_log_probs :: batch_shape num_states
def _scan_multiple_steps():
"""Perform `scan` operation when `num_steps` > 1."""
transition_log_probs = self._log_trans
def forward_step(log_probs, _):
return _log_vector_matrix(log_probs, transition_log_probs)
dummy_index = tf.zeros(self._num_steps - 1, dtype=tf.float32)
forward_log_probs = tf.scan(forward_step,
dummy_index,
initializer=initial_log_probs,
name="forward_log_probs")
return tf.concat([[initial_log_probs], forward_log_probs], axis=0)
forward_log_probs = prefer_static.cond(
self._num_steps > 1, _scan_multiple_steps,
lambda: initial_log_probs[tf.newaxis, ...])
return tf.exp(forward_log_probs)
def _sample_n(self, n, seed=None):
low = tf.convert_to_tensor(self.low)
high = tf.convert_to_tensor(self.high)
peak = tf.convert_to_tensor(self.peak)
stream = SeedStream(seed, salt='triangular')
shape = tf.concat(
[[n], self._batch_shape_tensor(low=low, high=high, peak=peak)],
axis=0)
samples = tf.random.uniform(shape=shape,
dtype=self.dtype,
seed=stream())
# We use Inverse CDF sampling here. Because the CDF is a quadratic function,
# we must use sqrts here.
interval_length = high - low
return tf.where(
# Note the CDF on the left side of the peak is
# (x - low) ** 2 / ((high - low) * (peak - low)).
# If we plug in peak for x, we get that the CDF at the peak
# is (peak - low) / (high - low). Because of this we decide
# which part of the piecewise CDF we should use based on the cdf samples
# we drew.
samples < (peak - low) / interval_length,
# Inverse of (x - low) ** 2 / ((high - low) * (peak - low)).
low + tf.sqrt(samples * interval_length * (peak - low)),
# Inverse of 1 - (high - x) ** 2 / ((high - low) * (high - peak))
high - tf.sqrt((1. - samples) * interval_length * (high - peak)))
def _sample_n(self, n, seed=None):
loc = tf.convert_to_tensor(self.loc)
scale = tf.convert_to_tensor(self.scale)
shape = tf.concat([[n], self._batch_shape_tensor(loc=loc, scale=scale)],
axis=0)
sampled = tf.random.normal(
shape=shape, mean=0., stddev=1., dtype=self.dtype, seed=seed)
return sampled * scale + loc
def _entropy(self, **kwargs):
if not self.bijector.is_constant_jacobian:
raise NotImplementedError("entropy is not implemented")
if not self.bijector._is_injective: # pylint: disable=protected-access
raise NotImplementedError("entropy is not implemented when "
"bijector is not injective.")
distribution_kwargs, bijector_kwargs = self._kwargs_split_fn(kwargs)
# Suppose Y = g(X) where g is a diffeomorphism and X is a continuous rv. It
# can be shown that:
# H[Y] = H[X] + E_X[(log o abs o det o J o g)(X)].
# If is_constant_jacobian then:
# E_X[(log o abs o det o J o g)(X)] = (log o abs o det o J o g)(c)
# where c can by anything.
entropy = self.distribution.entropy(**distribution_kwargs)
if self._is_maybe_event_override:
# H[X] = sum_i H[X_i] if X_i are mutually independent.
# This means that a reduce_sum is a simple rescaling.
entropy = entropy * tf.cast(
tf.reduce_prod(self._override_event_shape),
dtype=dtype_util.base_dtype(entropy.dtype))
if self._is_maybe_batch_override:
new_shape = tf.concat([
prefer_static.ones_like(self._override_batch_shape),
self.distribution.batch_shape_tensor()
], 0)
entropy = tf.reshape(entropy, new_shape)
multiples = tf.concat([
self._override_batch_shape,
prefer_static.ones_like(self.distribution.batch_shape_tensor())
], 0)
entropy = tf.tile(entropy, multiples)
dummy = prefer_static.zeros(shape=tf.concat(
[self.batch_shape_tensor(),
self.event_shape_tensor()], 0),
dtype=self.dtype)
event_ndims = (tensorshape_util.rank(self.event_shape)
if tensorshape_util.rank(self.event_shape) is not None
else tf.size(self.event_shape_tensor()))
ildj = self.bijector.inverse_log_det_jacobian(dummy,
event_ndims=event_ndims,
**bijector_kwargs)
entropy = entropy - tf.cast(ildj, entropy.dtype)
tensorshape_util.set_shape(entropy, self.batch_shape)
return entropy
def _rotate(self, samples):
"""Applies a Householder rotation to `samples`."""
event_dim = (tf.compat.dimension_value(self.event_shape[0])
or self._event_shape_tensor()[0])
basis = tf.concat(
[[1.], tf.zeros([event_dim - 1], dtype=self.dtype)], axis=0),
u = tf.math.l2_normalize(basis - self.mean_direction, axis=-1)
return samples - 2 * tf.reduce_sum(samples * u, axis=-1,
keepdims=True) * u
def _inverse_event_shape_tensor(self, output_shape):
if self.validate_args:
# It is not possible for a negative shape so we need only check <= 1.
dependencies = [assert_util.assert_greater(
output_shape[-1], 1, message="Need last dimension greater than 1.")]
else:
dependencies = []
with tf.control_dependencies(dependencies):
return tf.concat([output_shape[:-1], [output_shape[-1] - 1]], axis=0)
def _sample_n(self, n, seed=None):
del seed # unused
loc = tf.convert_to_tensor(self.loc)
return tf.broadcast_to(
loc,
tf.concat([[n],
self._batch_shape_tensor(loc=loc),
self._event_shape_tensor(loc=loc)],
axis=0))
def _extract_log_probs(num_states, dist):
"""Tabulate log probabilities from a batch of distributions."""
states = tf.reshape(
tf.range(num_states),
tf.concat([[num_states],
tf.ones_like(dist.batch_shape_tensor())],
axis=0))
return distribution_util.move_dimension(dist.log_prob(states), 0, -1)
def _sample_n(self, n, seed=None):
scale = tf.convert_to_tensor(self.scale)
shape = tf.concat([[n], tf.shape(scale)], 0)
sampled = tf.random.normal(shape=shape,
mean=0.,
stddev=1.,
dtype=self.dtype,
seed=seed)
return tf.abs(sampled * scale)
def _uniform_unit_norm(dimension, shape, dtype, seed):
"""Returns a batch of points chosen uniformly from the unit hypersphere."""
# This works because the Gaussian distribution is spherically symmetric.
# raw shape: shape + [dimension]
raw = normal.Normal(loc=dtype_util.as_numpy_dtype(dtype)(0),
scale=dtype_util.as_numpy_dtype(dtype)(1)).sample(
tf.concat([shape, [dimension]], axis=0),
seed=seed())
unit_norm = raw / tf.norm(raw, ord=2, axis=-1)[..., tf.newaxis]
return unit_norm
def _sample_n(self, n, seed=None, **kwargs):
with tf.control_dependencies(self._runtime_assertions):
x = self.distribution.sample(sample_shape=n, seed=seed, **kwargs)
new_shape = tf.concat([
[n],
self._batch_shape_unexpanded,
self.event_shape_tensor(),
],
axis=0)
return tf.reshape(x, new_shape)
def _pad_sample_dims(self, x):
with tf.name_scope("pad_sample_dims"):
ndims = tensorshape_util.rank(x.shape) if tensorshape_util.rank(
x.shape) is not None else tf.rank(x)
shape = tf.shape(x)
d = ndims - self._event_ndims
x = tf.reshape(x,
shape=tf.concat([shape[:d], [1], shape[d:]],
axis=0))
return x
def _sample_n(self, n, seed=None):
loc = tf.convert_to_tensor(self.loc)
scale = tf.convert_to_tensor(self.scale)
batch_shape = self._batch_shape_tensor(loc=loc, scale=scale)
shape = tf.concat([[n], batch_shape], 0)
probs = tf.random.uniform(shape=shape,
minval=0.,
maxval=1.,
dtype=self.dtype,
seed=seed)
return self._quantile(probs, loc=loc, scale=scale)
def _mode_mean_shape(self):
"""Shape for the mode/mean Tensors."""
shape = tensorshape_util.concatenate(self.batch_shape,
self.event_shape)
has_static_shape = tensorshape_util.is_fully_defined(shape)
if not has_static_shape:
shape = tf.concat([
self.batch_shape_tensor(),
self.event_shape_tensor(),
], 0)
return shape