def maybe_assert_bernoulli_param_correctness(is_init, validate_args, probs,
probits):
"""Return assertions for `ProbitBernoulli`-type distributions."""
if is_init:
x, name = (probs, 'probs') if probits is None else (probits, 'probits')
if not dtype_util.is_floating(x.dtype):
raise TypeError(
'Argument `{}` must having floating type.'.format(name))
if not validate_args:
return []
assertions = []
if probs is not None:
if is_init != tensor_util.is_ref(probs):
probs = tf.convert_to_tensor(probs)
one = tf.constant(1., probs.dtype)
assertions += [
assert_util.assert_non_negative(
probs, message='probs has components less than 0.'),
assert_util.assert_less_equal(
probs, one, message='probs has components greater than 1.')
]
return assertions
def _prob(self, event):
samples = tf.convert_to_tensor(self._samples)
num_samples = self._compute_num_samples(samples)
event = tf.convert_to_tensor(event, name='event', dtype=self.dtype)
event, samples = _broadcast_event_and_samples(
event, samples, event_ndims=self._event_ndims)
prob = tf.reduce_sum(tf.cast(tf.reduce_all(
tf.equal(samples, event), axis=tf.range(-self._event_ndims, 0)),
dtype=tf.int32),
axis=-1) / num_samples
if dtype_util.is_floating(self.dtype):
prob = tf.cast(prob, self.dtype)
return prob
def maybe_assert_categorical_param_correctness(is_init, validate_args, probs,
logits):
"""Return assertions for `Categorical`-type distributions."""
assertions = []
# In init, we can always build shape and dtype checks because
# we assume shape doesn't change for Variable backed args.
if is_init:
x, name = (probs, 'probs') if logits is None else (logits, 'logits')
if not dtype_util.is_floating(x.dtype):
raise TypeError(
'Argument `{}` must having floating type.'.format(name))
msg = 'Argument `{}` must have rank at least 1.'.format(name)
ndims = tensorshape_util.rank(x.shape)
if ndims is not None:
if ndims < 1:
raise ValueError(msg)
elif validate_args:
x = tf.convert_to_tensor(x)
probs = x if logits is None else None # Retain tensor conversion.
logits = x if probs is None else None
assertions.append(
assert_util.assert_rank_at_least(x, 1, message=msg))
if not validate_args:
assert not assertions # Should never happen.
return []
if logits is not None:
if is_init != tensor_util.is_ref(logits):
logits = tf.convert_to_tensor(logits)
assertions.extend(
distribution_util.assert_categorical_event_shape(logits))
if probs is not None:
if is_init != tensor_util.is_ref(probs):
probs = tf.convert_to_tensor(probs)
assertions.extend([
assert_util.assert_non_negative(probs),
assert_util.assert_near(
tf.reduce_sum(probs, axis=-1),
np.array(1, dtype=dtype_util.as_numpy_dtype(probs.dtype)),
message='Argument `probs` must sum to 1.')
])
assertions.extend(
distribution_util.assert_categorical_event_shape(probs))
return assertions
def _maybe_validate_matrix(a, validate_args):
"""Checks that input is a `float` matrix."""
assertions = []
if not dtype_util.is_floating(a.dtype):
raise TypeError('Input `a` must have `float`-like `dtype` '
'(saw {}).'.format(dtype_util.name(a.dtype)))
if tensorshape_util.rank(a.shape) is not None:
if tensorshape_util.rank(a.shape) < 2:
raise ValueError('Input `a` must have at least 2 dimensions '
'(saw: {}).'.format(tensorshape_util.rank(
a.shape)))
elif validate_args:
assertions.append(
assert_util.assert_rank_at_least(
a,
rank=2,
message='Input `a` must have at least 2 dimensions.'))
return assertions
def _parameter_control_dependencies(self, is_init):
"""Checks the validity of the concentration parameter."""
assertions = []
# In init, we can always build shape and dtype checks because
# we assume shape doesn't change for Variable backed args.
if is_init:
if not dtype_util.is_floating(self.concentration.dtype):
raise TypeError('Argument `concentration` must be float type.')
msg = 'Argument `concentration` must have rank at least 1.'
ndims = tensorshape_util.rank(self.concentration.shape)
if ndims is not None:
if ndims < 1:
raise ValueError(msg)
elif self.validate_args:
assertions.append(assert_util.assert_rank_at_least(
self.concentration, 1, message=msg))
msg = 'Argument `concentration` must have `event_size` at least 2.'
event_size = tf.compat.dimension_value(self.concentration.shape[-1])
if event_size is not None:
if event_size < 2:
raise ValueError(msg)
elif self.validate_args:
assertions.append(assert_util.assert_less(
1,
tf.shape(self.concentration)[-1],
message=msg))
if not self.validate_args:
assert not assertions # Should never happen.
return []
if is_init != tensor_util.is_ref(self.concentration):
assertions.append(assert_util.assert_positive(
self.concentration,
message='Argument `concentration` must be positive.'))
return assertions
def maybe_assert_negative_binomial_param_correctness(is_init, validate_args,
total_count, probs,
logits):
"""Return assertions for `NegativeBinomial`-type distributions."""
if is_init:
x, name = (probs, 'probs') if logits is None else (logits, 'logits')
if not dtype_util.is_floating(x.dtype):
raise TypeError(
'Argument `{}` must having floating type.'.format(name))
if not validate_args:
return []
assertions = []
if is_init != tensor_util.is_ref(total_count):
total_count = tf.convert_to_tensor(total_count)
assertions.extend([
assert_util.assert_non_negative(
total_count,
message='`total_count` has components less than 0.'),
distribution_util.assert_integer_form(
total_count,
message='`total_count` has fractional components.')
])
if probs is not None:
if is_init != tensor_util.is_ref(probs):
probs = tf.convert_to_tensor(probs)
one = tf.constant(1., probs.dtype)
assertions.extend([
assert_util.assert_non_negative(
probs, message='`probs` has components less than 0.'),
assert_util.assert_less_equal(
probs,
one,
message='`probs` has components greater than 1.')
])
return assertions
def _broadcast_cat_event_and_params(event, params, base_dtype):
"""Broadcasts the event or distribution parameters."""
if dtype_util.is_integer(event.dtype):
pass
elif dtype_util.is_floating(event.dtype):
# When `validate_args=True` we've already ensured int/float casting
# is closed.
event = tf.cast(event, dtype=tf.int32)
else:
raise TypeError('`value` should have integer `dtype` or '
'`self.dtype` ({})'.format(base_dtype))
shape_known_statically = (
tensorshape_util.rank(params.shape) is not None
and tensorshape_util.is_fully_defined(params.shape[:-1])
and tensorshape_util.is_fully_defined(event.shape))
if not shape_known_statically or params.shape[:-1] != event.shape:
params = params * tf.ones_like(event[..., tf.newaxis],
dtype=params.dtype)
params_shape = tf.shape(params)[:-1]
event = event * tf.ones(params_shape, dtype=event.dtype)
if tensorshape_util.rank(params.shape) is not None:
tensorshape_util.set_shape(event, params.shape[:-1])
return event, params
def _parameter_control_dependencies(self, is_init):
assertions = []
logits = self._logits
probs = self._probs
param, name = (probs, 'probs') if logits is None else (logits,
'logits')
# In init, we can always build shape and dtype checks because
# we assume shape doesn't change for Variable backed args.
if is_init:
if not dtype_util.is_floating(param.dtype):
raise TypeError(
'Argument `{}` must having floating type.'.format(name))
msg = 'Argument `{}` must have rank at least 1.'.format(name)
shape_static = tensorshape_util.dims(param.shape)
if shape_static is not None:
if len(shape_static) < 1:
raise ValueError(msg)
elif self.validate_args:
param = tf.convert_to_tensor(param)
assertions.append(
assert_util.assert_rank_at_least(param, 1, message=msg))
with tf.control_dependencies(assertions):
param = tf.identity(param)
msg1 = 'Argument `{}` must have final dimension >= 1.'.format(name)
msg2 = 'Argument `{}` must have final dimension <= {}.'.format(
name, tf.int32.max)
event_size = shape_static[-1] if shape_static is not None else None
if event_size is not None:
if event_size < 1:
raise ValueError(msg1)
if event_size > tf.int32.max:
raise ValueError(msg2)
elif self.validate_args:
param = tf.convert_to_tensor(param)
assertions.append(
assert_util.assert_greater_equal(tf.shape(param)[-1],
1,
message=msg1))
# NOTE: For now, we leave out a runtime assertion that
# `tf.shape(param)[-1] <= tf.int32.max`. An earlier `tf.shape` call
# will fail before we get to this point.
if not self.validate_args:
assert not assertions # Should never happen.
return []
if probs is not None:
probs = param # reuse tensor conversion from above
if is_init != tensor_util.is_ref(probs):
probs = tf.convert_to_tensor(probs)
one = tf.ones([], dtype=probs.dtype)
assertions.extend([
assert_util.assert_non_negative(probs),
assert_util.assert_less_equal(probs, one),
assert_util.assert_near(
tf.reduce_sum(probs, axis=-1),
one,
message='Argument `probs` must sum to 1.'),
])
return assertions
def _sample_n(self, num_samples, seed=None, name=None):
"""Returns a Tensor of samples from an LKJ distribution.
Args:
num_samples: Python `int`. The number of samples to draw.
seed: Python integer seed for RNG
name: Python `str` name prefixed to Ops created by this function.
Returns:
samples: A Tensor of correlation matrices with shape `[n, B, D, D]`,
where `B` is the shape of the `concentration` parameter, and `D`
is the `dimension`.
Raises:
ValueError: If `dimension` is negative.
"""
if self.dimension < 0:
raise ValueError(
'Cannot sample negative-dimension correlation matrices.')
# Notation below: B is the batch shape, i.e., tf.shape(concentration)
seed = SeedStream(seed, 'sample_lkj')
with tf.name_scope('sample_lkj' or name):
concentration = tf.convert_to_tensor(self.concentration)
if not dtype_util.is_floating(concentration.dtype):
raise TypeError(
'The concentration argument should have floating type, not '
'{}'.format(dtype_util.name(concentration.dtype)))
concentration = _replicate(num_samples, concentration)
concentration_shape = tf.shape(concentration)
if self.dimension <= 1:
# For any dimension <= 1, there is only one possible correlation matrix.
shape = tf.concat(
[concentration_shape, [self.dimension, self.dimension]],
axis=0)
return tf.ones(shape=shape, dtype=concentration.dtype)
beta_conc = concentration + (self.dimension - 2.) / 2.
beta_dist = beta.Beta(concentration1=beta_conc,
concentration0=beta_conc)
# Note that the sampler below deviates from [1], by doing the sampling in
# cholesky space. This does not change the fundamental logic of the
# sampler, but does speed up the sampling.
# This is the correlation coefficient between the first two dimensions.
# This is also `r` in reference [1].
corr12 = 2. * beta_dist.sample(seed=seed()) - 1.
# Below we construct the Cholesky of the initial 2x2 correlation matrix,
# which is of the form:
# [[1, 0], [r, sqrt(1 - r**2)]], where r is the correlation between the
# first two dimensions.
# This is the top-left corner of the cholesky of the final sample.
first_row = tf.concat([
tf.ones_like(corr12)[..., tf.newaxis],
tf.zeros_like(corr12)[..., tf.newaxis]
],
axis=-1)
second_row = tf.concat([
corr12[..., tf.newaxis],
tf.sqrt(1 - corr12**2)[..., tf.newaxis]
],
axis=-1)
chol_result = tf.concat([
first_row[..., tf.newaxis, :], second_row[..., tf.newaxis, :]
],
axis=-2)
for n in range(2, self.dimension):
# Loop invariant: on entry, result has shape B + [n, n]
beta_conc = beta_conc - 0.5
# norm is y in reference [1].
norm = beta.Beta(concentration1=n / 2.,
concentration0=beta_conc).sample(seed=seed())
# distance shape: B + [1] for broadcast
distance = tf.sqrt(norm)[..., tf.newaxis]
# direction is u in reference [1].
# direction shape: B + [n]
direction = _uniform_unit_norm(n, concentration_shape,
concentration.dtype, seed)
# raw_correlation is w in reference [1].
raw_correlation = distance * direction # shape: B + [n]
# This is the next row in the cholesky of the result,
# which differs from the construction in reference [1].
# In the reference, the new row `z` = chol_result @ raw_correlation^T
# = C @ raw_correlation^T (where as short hand we use C = chol_result).
# We prove that the below equation is the right row to add to the
# cholesky, by showing equality with reference [1].
# Let S be the sample constructed so far, and let `z` be as in
# reference [1]. Then at this iteration, the new sample S' will be
# [[S z^T]
# [z 1]]
# In our case we have the cholesky decomposition factor C, so
# we want our new row x (same size as z) to satisfy:
# [[S z^T] [[C 0] [[C^T x^T] [[CC^T Cx^T]
# [z 1]] = [x k]] [0 k]] = [xC^t xx^T + k**2]]
# Since C @ raw_correlation^T = z = C @ x^T, and C is invertible,
# we have that x = raw_correlation. Also 1 = xx^T + k**2, so k
# = sqrt(1 - xx^T) = sqrt(1 - |raw_correlation|**2) = sqrt(1 -
# distance**2).
new_row = tf.concat(
[raw_correlation,
tf.sqrt(1. - norm[..., tf.newaxis])],
axis=-1)
# Finally add this new row, by growing the cholesky of the result.
chol_result = tf.concat([
chol_result,
tf.zeros_like(chol_result[..., 0][..., tf.newaxis])
],
axis=-1)
chol_result = tf.concat(
[chol_result, new_row[..., tf.newaxis, :]], axis=-2)
if self.input_output_cholesky:
return chol_result
result = tf.matmul(chol_result, chol_result, transpose_b=True)
# The diagonal for a correlation matrix should always be ones. Due to
# numerical instability the matmul might not achieve that, so manually set
# these to ones.
result = tf.linalg.set_diag(
result, tf.ones(shape=tf.shape(result)[:-1],
dtype=result.dtype))
# This sampling algorithm can produce near-PSD matrices on which standard
# algorithms such as `tf.cholesky` or `tf.linalg.self_adjoint_eigvals`
# fail. Specifically, as documented in b/116828694, around 2% of trials
# of 900,000 5x5 matrices (distributed according to 9 different
# concentration parameter values) contained at least one matrix on which
# the Cholesky decomposition failed.
return result
def __init__(self,
df,
loc,
scale,
validate_args=False,
allow_nan_stats=True,
name='MultivariateStudentTLinearOperator'):
"""Construct Multivariate Student's t-distribution on `R^k`.
The `batch_shape` is the broadcast shape between `df`, `loc` and `scale`
arguments.
The `event_shape` is given by last dimension of the matrix implied by
`scale`. The last dimension of `loc` must broadcast with this.
Additional leading dimensions (if any) will index batches.
Args:
df: A positive floating-point `Tensor`. Has shape `[B1, ..., Bb]` where `b
>= 0`.
loc: Floating-point `Tensor`. Has shape `[B1, ..., Bb, k]` where `k` is
the event size.
scale: Instance of `LinearOperator` with a floating `dtype` and shape
`[B1, ..., Bb, k, k]`.
validate_args: Python `bool`, default `False`. Whether to validate input
with asserts. If `validate_args` is `False`, and the inputs are invalid,
correct behavior is not guaranteed.
allow_nan_stats: Python `bool`, default `True`. If `False`, raise an
exception if a statistic (e.g. mean/variance/etc...) is undefined for
any batch member If `True`, batch members with valid parameters leading
to undefined statistics will return NaN for this statistic.
name: The name to give Ops created by the initializer.
Raises:
TypeError: if not `scale.dtype.is_floating`.
ValueError: if not `scale.is_positive_definite`.
"""
parameters = dict(locals())
if not dtype_util.is_floating(scale.dtype):
raise TypeError('`scale` must have floating-point dtype.')
if validate_args and not scale.is_positive_definite:
raise ValueError('`scale` must be positive definite.')
with tf.name_scope(name) as name:
dtype = dtype_util.common_dtype([df, loc, scale],
dtype_hint=tf.float32)
self._df = tensor_util.convert_nonref_to_tensor(df,
name='df',
dtype=dtype)
self._loc = tensor_util.convert_nonref_to_tensor(loc,
name='loc',
dtype=dtype)
self._scale = scale
super(MultivariateStudentTLinearOperator,
self).__init__(dtype=dtype,
reparameterization_type=reparameterization.
FULLY_REPARAMETERIZED,
parameters=parameters,
name=name,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats)
self._parameters = parameters
def __init__(self,
rate=None,
log_rate=None,
interpolate_nondiscrete=True,
validate_args=False,
allow_nan_stats=True,
name='Poisson'):
"""Initialize a batch of Poisson distributions.
Args:
rate: Floating point tensor, the rate parameter. `rate` must be positive.
Must specify exactly one of `rate` and `log_rate`.
log_rate: Floating point tensor, the log of the rate parameter.
Must specify exactly one of `rate` and `log_rate`.
interpolate_nondiscrete: Python `bool`. When `False`,
`log_prob` returns `-inf` (and `prob` returns `0`) for non-integer
inputs. When `True`, `log_prob` evaluates the continuous function
`k * log_rate - lgamma(k+1) - rate`, which matches the Poisson pmf
at integer arguments `k` (note that this function is not itself
a normalized probability log-density).
Default value: `True`.
validate_args: Python `bool`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
Default value: `False`.
allow_nan_stats: Python `bool`. When `True`, statistics
(e.g., mean, mode, variance) use the value "`NaN`" to indicate the
result is undefined. When `False`, an exception is raised if one or
more of the statistic's batch members are undefined.
Default value: `True`.
name: Python `str` name prefixed to Ops created by this class.
Raises:
ValueError: if none or both of `rate`, `log_rate` are specified.
TypeError: if `rate` is not a float-type.
TypeError: if `log_rate` is not a float-type.
"""
parameters = dict(locals())
if (rate is None) == (log_rate is None):
raise ValueError(
'Must specify exactly one of `rate` and `log_rate`.')
with tf.name_scope(name) as name:
dtype = dtype_util.common_dtype([rate, log_rate],
dtype_hint=tf.float32)
if not dtype_util.is_floating(dtype):
raise TypeError(
'[log_]rate.dtype ({}) is a not a float-type.'.format(
dtype_util.name(dtype)))
self._rate = tensor_util.convert_nonref_to_tensor(rate,
name='rate',
dtype=dtype)
self._log_rate = tensor_util.convert_nonref_to_tensor(
log_rate, name='log_rate', dtype=dtype)
self._interpolate_nondiscrete = interpolate_nondiscrete
super(Poisson, self).__init__(
dtype=dtype,
reparameterization_type=reparameterization.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
name=name)
def __init__(self,
loc=None,
scale=None,
validate_args=False,
allow_nan_stats=True,
name="VectorExponentialLinearOperator"):
"""Construct Vector Exponential distribution supported on a subset of `R^k`.
The `batch_shape` is the broadcast shape between `loc` and `scale`
arguments.
The `event_shape` is given by last dimension of the matrix implied by
`scale`. The last dimension of `loc` (if provided) must broadcast with this.
Recall that `covariance = scale @ scale.T`.
Additional leading dimensions (if any) will index batches.
Args:
loc: Floating-point `Tensor`. If this is set to `None`, `loc` is
implicitly `0`. When specified, may have shape `[B1, ..., Bb, k]` where
`b >= 0` and `k` is the event size.
scale: Instance of `LinearOperator` with same `dtype` as `loc` and shape
`[B1, ..., Bb, k, k]`.
validate_args: Python `bool`, default `False`. Whether to validate input
with asserts. If `validate_args` is `False`, and the inputs are
invalid, correct behavior is not guaranteed.
allow_nan_stats: Python `bool`, default `True`. If `False`, raise an
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
batch member If `True`, batch members with valid parameters leading to
undefined statistics will return NaN for this statistic.
name: The name to give Ops created by the initializer.
Raises:
ValueError: if `scale` is unspecified.
TypeError: if not `scale.dtype.is_floating`
"""
parameters = dict(locals())
if scale is None:
raise ValueError("Missing required `scale` parameter.")
if not dtype_util.is_floating(scale.dtype):
raise TypeError("`scale` parameter must have floating-point dtype.")
with tf.name_scope(name) as name:
# Since expand_dims doesn't preserve constant-ness, we obtain the
# non-dynamic value if possible.
loc = loc if loc is None else tf.convert_to_tensor(
loc, name="loc", dtype=scale.dtype)
batch_shape, event_shape = distribution_util.shapes_from_loc_and_scale(
loc, scale)
super(VectorExponentialLinearOperator, self).__init__(
distribution=exponential.Exponential(
rate=tf.ones([], dtype=scale.dtype),
allow_nan_stats=allow_nan_stats),
bijector=affine_linear_operator_bijector.AffineLinearOperator(
shift=loc, scale=scale, validate_args=validate_args),
batch_shape=batch_shape,
event_shape=event_shape,
validate_args=validate_args,
name=name)
self._parameters = parameters
def __init__(self,
df,
scale_operator,
input_output_cholesky=False,
validate_args=False,
allow_nan_stats=True,
name=None):
"""Construct Wishart distributions.
Args:
df: `float` or `double` tensor, the degrees of freedom of the
distribution(s). `df` must be greater than or equal to `k`.
scale_operator: `float` or `double` instance of `LinearOperator`.
input_output_cholesky: Python `bool`. If `True`, functions whose input or
output have the semantics of samples assume inputs are in Cholesky form
and return outputs in Cholesky form. In particular, if this flag is
`True`, input to `log_prob` is presumed of Cholesky form and output from
`sample`, `mean`, and `mode` are of Cholesky form. Setting this
argument to `True` is purely a computational optimization and does not
change the underlying distribution; for instance, `mean` returns the
Cholesky of the mean, not the mean of Cholesky factors. The `variance`
and `stddev` methods are unaffected by this flag.
Default value: `False` (i.e., input/output does not have Cholesky
semantics).
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
(e.g., mean, mode, variance) use the value "`NaN`" to indicate the
result is undefined. When `False`, an exception is raised if one or
more of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class.
Raises:
TypeError: if scale is not floating-type
TypeError: if scale.dtype != df.dtype
ValueError: if df < k, where scale operator event shape is
`(k, k)`
"""
parameters = dict(locals())
self._input_output_cholesky = input_output_cholesky
with tf.name_scope(name) as name:
with tf.name_scope("init"):
if not dtype_util.is_floating(scale_operator.dtype):
raise TypeError(
"scale_operator.dtype=%s is not a floating-point type"
% scale_operator.dtype)
if not scale_operator.is_square:
print(scale_operator.to_dense().eval())
raise ValueError("scale_operator must be square.")
self._scale_operator = scale_operator
self._df = tf.convert_to_tensor(df,
dtype=scale_operator.dtype,
name="df")
dtype_util.assert_same_float_dtype(
[self._df, self._scale_operator])
if tf.compat.dimension_value(
self._scale_operator.shape[-1]) is None:
self._dimension = tf.cast(
self._scale_operator.domain_dimension_tensor(),
dtype=self._scale_operator.dtype,
name="dimension")
else:
self._dimension = tf.convert_to_tensor(
tf.compat.dimension_value(
self._scale_operator.shape[-1]),
dtype=self._scale_operator.dtype,
name="dimension")
df_val = tf.get_static_value(self._df)
dim_val = tf.get_static_value(self._dimension)
if df_val is not None and dim_val is not None:
df_val = np.asarray(df_val)
if not df_val.shape:
df_val = [df_val]
if np.any(df_val < dim_val):
raise ValueError(
"Degrees of freedom (df = %s) cannot be less than "
"dimension of scale matrix (scale.dimension = %s)"
% (df_val, dim_val))
elif validate_args:
assertions = assert_util.assert_less_equal(
self._dimension,
self._df,
message=("Degrees of freedom (df = %s) cannot be "
"less than dimension of scale matrix "
"(scale.dimension = %s)" %
(self._dimension, self._df)))
self._df = distribution_util.with_dependencies(
[assertions], self._df)
super(_WishartLinearOperator, self).__init__(
dtype=self._scale_operator.dtype,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
reparameterization_type=reparameterization.FULLY_REPARAMETERIZED,
parameters=parameters,
name=name)
def __init__(self,
power,
dtype=tf.int32,
interpolate_nondiscrete=True,
sample_maximum_iterations=100,
validate_args=False,
allow_nan_stats=False,
name='Zipf'):
"""Initialize a batch of Zipf distributions.
Args:
power: `Float` like `Tensor` representing the power parameter. Must be
strictly greater than `1`.
dtype: The `dtype` of `Tensor` returned by `sample`.
Default value: `tf.int32`.
interpolate_nondiscrete: Python `bool`. When `False`, `log_prob` returns
`-inf` (and `prob` returns `0`) for non-integer inputs. When `True`,
`log_prob` evaluates the continuous function `-power log(k) -
log(zeta(power))` , which matches the Zipf pmf at integer arguments `k`
(note that this function is not itself a normalized probability
log-density).
Default value: `True`.
sample_maximum_iterations: Maximum number of iterations of allowable
iterations in `sample`. When `validate_args=True`, samples which fail to
reach convergence (subject to this cap) are masked out with
`self.dtype.min` or `nan` depending on `self.dtype.is_integer`.
Default value: `100`.
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
Default value: `False`.
allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
(e.g., mean, mode, variance) use the value "`NaN`" to indicate the
result is undefined. When `False`, an exception is raised if one or more
of the statistic's batch members are undefined.
Default value: `False`.
name: Python `str` name prefixed to Ops created by this class.
Default value: `'Zipf'`.
Raises:
TypeError: if `power` is not `float` like.
"""
parameters = dict(locals())
with tf.name_scope(name) as name:
self._power = tensor_util.convert_nonref_to_tensor(
power,
name='power',
dtype=dtype_util.common_dtype([power], dtype_hint=tf.float32))
if (not dtype_util.is_floating(self._power.dtype) or
dtype_util.base_equal(self._power.dtype, tf.float16)):
raise TypeError(
'power.dtype ({}) is not a supported `float` type.'.format(
dtype_util.name(self._power.dtype)))
self._interpolate_nondiscrete = interpolate_nondiscrete
self._sample_maximum_iterations = sample_maximum_iterations
super(Zipf, self).__init__(
dtype=dtype,
reparameterization_type=reparameterization.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
name=name)
def __init__(self,
outcomes,
logits=None,
probs=None,
rtol=None,
atol=None,
validate_args=False,
allow_nan_stats=True,
name='FiniteDiscrete'):
"""Construct a finite discrete contribution.
Args:
outcomes: A 1-D floating or integer `Tensor`, representing a list of
possible outcomes in strictly ascending order.
logits: A floating N-D `Tensor`, `N >= 1`, representing the log
probabilities of a set of FiniteDiscrete distributions. The first `N -
1` dimensions index into a batch of independent distributions and the
last dimension represents a vector of logits for each discrete value.
Only one of `logits` or `probs` should be passed in.
probs: A floating N-D `Tensor`, `N >= 1`, representing the probabilities
of a set of FiniteDiscrete distributions. The first `N - 1` dimensions
index into a batch of independent distributions and the last dimension
represents a vector of probabilities for each discrete value. Only one
of `logits` or `probs` should be passed in.
rtol: `Tensor` with same `dtype` as `outcomes`. The relative tolerance for
floating number comparison. Only effective when `outcomes` is a floating
`Tensor`. Default is `10 * eps`.
atol: `Tensor` with same `dtype` as `outcomes`. The absolute tolerance for
floating number comparison. Only effective when `outcomes` is a floating
`Tensor`. Default is `10 * eps`.
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may render incorrect outputs.
allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
(e.g., mean, mode, variance) use the value '`NaN`' to indicate the
result is undefined. When `False`, an exception is raised if one or more
of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class.
"""
parameters = dict(locals())
with tf.name_scope(name) as name:
outcomes_dtype = dtype_util.common_dtype(
[outcomes], dtype_hint=tf.float32)
self._outcomes = tensor_util.convert_nonref_to_tensor(
outcomes, dtype_hint=outcomes_dtype, name='outcomes')
if dtype_util.is_floating(self._outcomes.dtype):
eps = np.finfo(dtype_util.as_numpy_dtype(outcomes_dtype)).eps
self._rtol = 10 * eps if rtol is None else rtol
self._atol = 10 * eps if atol is None else atol
else:
self._rtol = None
self._atol = None
self._categorical = categorical.Categorical(
logits=logits,
probs=probs,
dtype=tf.int32,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats)
super(FiniteDiscrete, self).__init__(
dtype=self._outcomes.dtype,
reparameterization_type=reparameterization.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
name=name)
