tfp.distributions.VectorLaplaceDiag

Class `VectorLaplaceDiag`

The vectorization of the Laplace distribution on `R^k`.

The vector laplace distribution is defined over `R^k`, and parameterized by a (batch of) length-`k` `loc` vector (the means) and a (batch of) `k x k` `scale` matrix: `covariance = 2 * scale @ scale.T`, where `@` denotes matrix-multiplication.

Mathematical Details

The probability density function (pdf) is,

``````pdf(x; loc, scale) = exp(-||y||_1) / Z,
y = inv(scale) @ (x - loc),
Z = 2**k |det(scale)|,
``````

where:

• `loc` is a vector in `R^k`,
• `scale` is a linear operator in `R^{k x k}`, `cov = scale @ scale.T`,
• `Z` denotes the normalization constant, and,
• `||y||_1` denotes the `l1` norm of `y`, `sum_i |y_i|.

A (non-batch) `scale` matrix is:

``````scale = diag(scale_diag + scale_identity_multiplier * ones(k))
``````

where:

• `scale_diag.shape = [k]`, and,
• `scale_identity_multiplier.shape = []`.

If both `scale_diag` and `scale_identity_multiplier` are `None`, then `scale` is the Identity matrix.

The VectorLaplace distribution is a member of the location-scale family, i.e., it can be constructed as,

``````X = (X_1, ..., X_k), each X_i ~ Laplace(loc=0, scale=1)
Y = (Y_1, ...,Y_k) = scale @ X + loc
``````

About `VectorLaplace` and `Vector` distributions in TensorFlow.

The `VectorLaplace` is a non-standard distribution that has useful properties.

The marginals `Y_1, ..., Y_k` are not Laplace random variables, due to the fact that the sum of Laplace random variables is not Laplace.

Instead, `Y` is a vector whose components are linear combinations of Laplace random variables. Thus, `Y` lives in the vector space generated by `vectors` of Laplace distributions. This allows the user to decide the mean and covariance (by setting `loc` and `scale`), while preserving some properties of the Laplace distribution. In particular, the tails of `Y_i` will be (up to polynomial factors) exponentially decaying.

To see this last statement, note that the pdf of `Y_i` is the convolution of the pdf of `k` independent Laplace random variables. One can then show by induction that distributions with exponential (up to polynomial factors) tails are closed under convolution.

Examples

``````tfd = tfp.distributions

# Initialize a single 2-variate VectorLaplace.
vla = tfd.VectorLaplaceDiag(
loc=[1., -1],
scale_diag=[1, 2.])

vla.mean().eval()
# ==> [1., -1]

vla.stddev().eval()
# ==> [1., 2] * sqrt(2)

# Evaluate this on an observation in `R^2`, returning a scalar.
vla.prob([-1., 0]).eval()  # shape: []

# Initialize a 3-batch, 2-variate scaled-identity VectorLaplace.
vla = tfd.VectorLaplaceDiag(
loc=[1., -1],
scale_identity_multiplier=[1, 2., 3])

vla.mean().eval()  # shape: [3, 2]
# ==> [[1., -1]
#      [1, -1],
#      [1, -1]]

vla.stddev().eval()  # shape: [3, 2]
# ==> sqrt(2) * [[1., 1],
#                [2, 2],
#                [3, 3]]

# Evaluate this on an observation in `R^2`, returning a length-3 vector.
vla.prob([-1., 0]).eval()  # shape: 

# Initialize a 2-batch of 3-variate VectorLaplace's.
vla = tfd.VectorLaplaceDiag(
loc=[[1., 2, 3],
[11, 22, 33]]           # shape: [2, 3]
scale_diag=[[1., 2, 3],
[0.5, 1, 1.5]])  # shape: [2, 3]

# Evaluate this on a two observations, each in `R^3`, returning a length-2
# vector.
x = [[-1., 0, 1],
[-11, 0, 11.]]   # shape: [2, 3].
vla.prob(x).eval()    # shape: 
``````

`__init__`

``````__init__(
loc=None,
scale_diag=None,
scale_identity_multiplier=None,
validate_args=False,
allow_nan_stats=True,
name='VectorLaplaceDiag'
)
``````

Construct Vector Laplace distribution on `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 = 2 * scale @ scale.T`.

``````scale = diag(scale_diag + scale_identity_multiplier * ones(k))
``````

where:

• `scale_diag.shape = [k]`, and,
• `scale_identity_multiplier.shape = []`.

If both `scale_diag` and `scale_identity_multiplier` are `None`, then `scale` is the Identity matrix.

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_diag`: Non-zero, floating-point `Tensor` representing a diagonal matrix added to `scale`. May have shape `[B1, ..., Bb, k]`, `b >= 0`, and characterizes `b`-batches of `k x k` diagonal matrices added to `scale`. When both `scale_identity_multiplier` and `scale_diag` are `None` then `scale` is the `Identity`.
• `scale_identity_multiplier`: Non-zero, floating-point `Tensor` representing a scaled-identity-matrix added to `scale`. May have shape `[B1, ..., Bb]`, `b >= 0`, and characterizes `b`-batches of scaled `k x k` identity matrices added to `scale`. When both `scale_identity_multiplier` and `scale_diag` are `None` then `scale` is the `Identity`.
• `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:

• `ValueError`: if at most `scale_identity_multiplier` is specified.

Properties

`allow_nan_stats`

Python `bool` describing behavior when a stat is undefined.

Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is undefined (no clear way to say it is either + or - infinity), so the variance = E[(X - mean)**2] is also undefined.

Returns:

• `allow_nan_stats`: Python `bool`.

`batch_shape`

Shape of a single sample from a single event index as a `TensorShape`.

May be partially defined or unknown.

The batch dimensions are indexes into independent, non-identical parameterizations of this distribution.

Returns:

• `batch_shape`: `TensorShape`, possibly unknown.

`bijector`

Function transforming x => y.

`distribution`

Base distribution, p(x).

`dtype`

The `DType` of `Tensor`s handled by this `Distribution`.

`event_shape`

Shape of a single sample from a single batch as a `TensorShape`.

May be partially defined or unknown.

Returns:

• `event_shape`: `TensorShape`, possibly unknown.

`loc`

The `loc` `Tensor` in `Y = scale @ X + loc`.

`name`

Name prepended to all ops created by this `Distribution`.

`parameters`

Dictionary of parameters used to instantiate this `Distribution`.

`reparameterization_type`

Describes how samples from the distribution are reparameterized.

Currently this is one of the static instances `tfd.FULLY_REPARAMETERIZED` or `tfd.NOT_REPARAMETERIZED`.

Returns:

An instance of `ReparameterizationType`.

`scale`

The `scale` `LinearOperator` in `Y = scale @ X + loc`.

`validate_args`

Python `bool` indicating possibly expensive checks are enabled.

Methods

`batch_shape_tensor`

``````batch_shape_tensor(name='batch_shape_tensor')
``````

Shape of a single sample from a single event index as a 1-D `Tensor`.

The batch dimensions are indexes into independent, non-identical parameterizations of this distribution.

Args:

• `name`: name to give to the op

Returns:

• `batch_shape`: `Tensor`.

`cdf`

``````cdf(
value,
name='cdf'
)
``````

Cumulative distribution function.

Given random variable `X`, the cumulative distribution function `cdf` is:

``````cdf(x) := P[X <= x]
``````

Args:

• `value`: `float` or `double` `Tensor`.
• `name`: Python `str` prepended to names of ops created by this function.

Returns:

• `cdf`: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with values of type `self.dtype`.

`copy`

``````copy(**override_parameters_kwargs)
``````

Creates a deep copy of the distribution.

Args:

• `**override_parameters_kwargs`: String/value dictionary of initialization arguments to override with new values.

Returns:

• `distribution`: A new instance of `type(self)` initialized from the union of self.parameters and override_parameters_kwargs, i.e., `dict(self.parameters, **override_parameters_kwargs)`.

`covariance`

``````covariance(name='covariance')
``````

Covariance.

Covariance is (possibly) defined only for non-scalar-event distributions.

For example, for a length-`k`, vector-valued distribution, it is calculated as,

``````Cov[i, j] = Covariance(X_i, X_j) = E[(X_i - E[X_i]) (X_j - E[X_j])]
``````

where `Cov` is a (batch of) `k x k` matrix, `0 <= (i, j) < k`, and `E` denotes expectation.

Alternatively, for non-vector, multivariate distributions (e.g., matrix-valued, Wishart), `Covariance` shall return a (batch of) matrices under some vectorization of the events, i.e.,

``````Cov[i, j] = Covariance(Vec(X)_i, Vec(X)_j) = [as above]
``````

where `Cov` is a (batch of) `k' x k'` matrices, `0 <= (i, j) < k' = reduce_prod(event_shape)`, and `Vec` is some function mapping indices of this distribution's event dimensions to indices of a length-`k'` vector.

Args:

• `name`: Python `str` prepended to names of ops created by this function.

Returns:

• `covariance`: Floating-point `Tensor` with shape `[B1, ..., Bn, k', k']` where the first `n` dimensions are batch coordinates and `k' = reduce_prod(self.event_shape)`.

`cross_entropy`

``````cross_entropy(
other,
name='cross_entropy'
)
``````

Computes the (Shannon) cross entropy.

Denote this distribution (`self`) by `P` and the `other` distribution by `Q`. Assuming `P, Q` are absolutely continuous with respect to one another and permit densities `p(x) dr(x)` and `q(x) dr(x)`, (Shannon) cross entropy is defined as:

``````H[P, Q] = E_p[-log q(X)] = -int_F p(x) log q(x) dr(x)
``````

where `F` denotes the support of the random variable `X ~ P`.

Returns:

• `cross_entropy`: `self.dtype` `Tensor` with shape `[B1, ..., Bn]` representing `n` different calculations of (Shannon) cross entropy.

`entropy`

``````entropy(name='entropy')
``````

Shannon entropy in nats.

`event_shape_tensor`

``````event_shape_tensor(name='event_shape_tensor')
``````

Shape of a single sample from a single batch as a 1-D int32 `Tensor`.

Args:

• `name`: name to give to the op

Returns:

• `event_shape`: `Tensor`.

`is_scalar_batch`

``````is_scalar_batch(name='is_scalar_batch')
``````

Indicates that `batch_shape == []`.

Args:

• `name`: Python `str` prepended to names of ops created by this function.

Returns:

• `is_scalar_batch`: `bool` scalar `Tensor`.

`is_scalar_event`

``````is_scalar_event(name='is_scalar_event')
``````

Indicates that `event_shape == []`.

Args:

• `name`: Python `str` prepended to names of ops created by this function.

Returns:

• `is_scalar_event`: `bool` scalar `Tensor`.

`kl_divergence`

``````kl_divergence(
other,
name='kl_divergence'
)
``````

Computes the Kullback--Leibler divergence.

Denote this distribution (`self`) by `p` and the `other` distribution by `q`. Assuming `p, q` are absolutely continuous with respect to reference measure `r`, the KL divergence is defined as:

``````KL[p, q] = E_p[log(p(X)/q(X))]
= -int_F p(x) log q(x) dr(x) + int_F p(x) log p(x) dr(x)
= H[p, q] - H[p]
``````

where `F` denotes the support of the random variable `X ~ p`, `H[., .]` denotes (Shannon) cross entropy, and `H[.]` denotes (Shannon) entropy.

Returns:

• `kl_divergence`: `self.dtype` `Tensor` with shape `[B1, ..., Bn]` representing `n` different calculations of the Kullback-Leibler divergence.

`log_cdf`

``````log_cdf(
value,
name='log_cdf'
)
``````

Log cumulative distribution function.

Given random variable `X`, the cumulative distribution function `cdf` is:

``````log_cdf(x) := Log[ P[X <= x] ]
``````

Often, a numerical approximation can be used for `log_cdf(x)` that yields a more accurate answer than simply taking the logarithm of the `cdf` when `x << -1`.

Args:

• `value`: `float` or `double` `Tensor`.
• `name`: Python `str` prepended to names of ops created by this function.

Returns:

• `logcdf`: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with values of type `self.dtype`.

`log_prob`

``````log_prob(
value,
name='log_prob'
)
``````

Log probability density/mass function.

Additional documentation from `VectorLaplaceLinearOperator`:

`value` is a batch vector with compatible shape if `value` is a `Tensor` whose shape can be broadcast up to either:

``````self.batch_shape + self.event_shape
``````

or

``````[M1, ..., Mm] + self.batch_shape + self.event_shape
``````

Args:

• `value`: `float` or `double` `Tensor`.
• `name`: Python `str` prepended to names of ops created by this function.

Returns:

• `log_prob`: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with values of type `self.dtype`.

`log_survival_function`

``````log_survival_function(
value,
name='log_survival_function'
)
``````

Log survival function.

Given random variable `X`, the survival function is defined:

``````log_survival_function(x) = Log[ P[X > x] ]
= Log[ 1 - P[X <= x] ]
= Log[ 1 - cdf(x) ]
``````

Typically, different numerical approximations can be used for the log survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`.

Args:

• `value`: `float` or `double` `Tensor`.
• `name`: Python `str` prepended to names of ops created by this function.

Returns:

`Tensor` of shape `sample_shape(x) + self.batch_shape` with values of type `self.dtype`.

`mean`

``````mean(name='mean')
``````

Mean.

`mode`

``````mode(name='mode')
``````

Mode.

`param_shapes`

``````param_shapes(
cls,
sample_shape,
name='DistributionParamShapes'
)
``````

Shapes of parameters given the desired shape of a call to `sample()`.

This is a class method that describes what key/value arguments are required to instantiate the given `Distribution` so that a particular shape is returned for that instance's call to `sample()`.

Subclasses should override class method `_param_shapes`.

Args:

• `sample_shape`: `Tensor` or python list/tuple. Desired shape of a call to `sample()`.
• `name`: name to prepend ops with.

Returns:

`dict` of parameter name to `Tensor` shapes.

`param_static_shapes`

``````param_static_shapes(
cls,
sample_shape
)
``````

param_shapes with static (i.e. `TensorShape`) shapes.

This is a class method that describes what key/value arguments are required to instantiate the given `Distribution` so that a particular shape is returned for that instance's call to `sample()`. Assumes that the sample's shape is known statically.

Subclasses should override class method `_param_shapes` to return constant-valued tensors when constant values are fed.

Args:

• `sample_shape`: `TensorShape` or python list/tuple. Desired shape of a call to `sample()`.

Returns:

`dict` of parameter name to `TensorShape`.

Raises:

• `ValueError`: if `sample_shape` is a `TensorShape` and is not fully defined.

`prob`

``````prob(
value,
name='prob'
)
``````

Probability density/mass function.

Additional documentation from `VectorLaplaceLinearOperator`:

`value` is a batch vector with compatible shape if `value` is a `Tensor` whose shape can be broadcast up to either:

``````self.batch_shape + self.event_shape
``````

or

``````[M1, ..., Mm] + self.batch_shape + self.event_shape
``````

Args:

• `value`: `float` or `double` `Tensor`.
• `name`: Python `str` prepended to names of ops created by this function.

Returns:

• `prob`: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with values of type `self.dtype`.

`quantile`

``````quantile(
value,
name='quantile'
)
``````

Quantile function. Aka "inverse cdf" or "percent point function".

Given random variable `X` and `p in [0, 1]`, the `quantile` is:

``````quantile(p) := x such that P[X <= x] == p
``````

Args:

• `value`: `float` or `double` `Tensor`.
• `name`: Python `str` prepended to names of ops created by this function.

Returns:

• `quantile`: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with values of type `self.dtype`.

`sample`

``````sample(
sample_shape=(),
seed=None,
name='sample'
)
``````

Generate samples of the specified shape.

Note that a call to `sample()` without arguments will generate a single sample.

Args:

• `sample_shape`: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
• `seed`: Python integer seed for RNG
• `name`: name to give to the op.

Returns:

• `samples`: a `Tensor` with prepended dimensions `sample_shape`.

`stddev`

``````stddev(name='stddev')
``````

Standard deviation.

Standard deviation is defined as,

``````stddev = E[(X - E[X])**2]**0.5
``````

where `X` is the random variable associated with this distribution, `E` denotes expectation, and `stddev.shape = batch_shape + event_shape`.

Args:

• `name`: Python `str` prepended to names of ops created by this function.

Returns:

• `stddev`: Floating-point `Tensor` with shape identical to `batch_shape + event_shape`, i.e., the same shape as `self.mean()`.

`survival_function`

``````survival_function(
value,
name='survival_function'
)
``````

Survival function.

Given random variable `X`, the survival function is defined:

``````survival_function(x) = P[X > x]
= 1 - P[X <= x]
= 1 - cdf(x).
``````

Args:

• `value`: `float` or `double` `Tensor`.
• `name`: Python `str` prepended to names of ops created by this function.

Returns:

`Tensor` of shape `sample_shape(x) + self.batch_shape` with values of type `self.dtype`.

`variance`

``````variance(name='variance')
``````

Variance.

Variance is defined as,

``````Var = E[(X - E[X])**2]
``````

where `X` is the random variable associated with this distribution, `E` denotes expectation, and `Var.shape = batch_shape + event_shape`.

Args:

• `name`: Python `str` prepended to names of ops created by this function.

Returns:

• `variance`: Floating-point `Tensor` with shape identical to `batch_shape + event_shape`, i.e., the same shape as `self.mean()`.