tf.distributions.DirichletMultinomial

Class `DirichletMultinomial`

Inherits From: Distribution

Aliases:

Class tf.contrib.distributions.DirichletMultinomial
Class tf.distributions.DirichletMultinomial

Defined in tensorflow/python/ops/distributions/dirichlet_multinomial.py.

Dirichlet-Multinomial compound distribution.

The Dirichlet-Multinomial distribution is parameterized by a (batch of) length-K concentration vectors (K > 1) and a total_count number of trials, i.e., the number of trials per draw from the DirichletMultinomial. It is defined over a (batch of) length-K vector counts such that tf.reduce_sum(counts, -1) = total_count. The Dirichlet-Multinomial is identically the Beta-Binomial distribution when K = 2.

Mathematical Details

The Dirichlet-Multinomial is a distribution over K-class counts, i.e., a length-K vector of non-negative integer counts = n = [n_0, ..., n_{K-1}].

The probability mass function (pmf) is,

pmf(n; alpha, N) = Beta(alpha + n) / (prod_j n_j!) / Z
Z = Beta(alpha) / N!

where:

concentration = alpha = [alpha_0, ..., alpha_{K-1}], alpha_j > 0,
total_count = N, N a positive integer,
N! is N factorial, and,
Beta(x) = prod_j Gamma(x_j) / Gamma(sum_j x_j) is the multivariate beta function, and,
Gamma is the gamma function.

Dirichlet-Multinomial is a compound distribution, i.e., its samples are generated as follows.

Choose class probabilities: probs = [p_0,...,p_{K-1}] ~ Dir(concentration)
Draw integers: counts = [n_0,...,n_{K-1}] ~ Multinomial(total_count, probs)

The last concentration dimension parametrizes a single Dirichlet-Multinomial distribution. When calling distribution functions (e.g., dist.prob(counts)), concentration, total_count and counts are broadcast to the same shape. The last dimension of counts corresponds single Dirichlet-Multinomial distributions.

Distribution parameters are automatically broadcast in all functions; see examples for details.

Pitfalls

The number of classes, K, must not exceed: - the largest integer representable by self.dtype, i.e., 2**(mantissa_bits+1) (IEE754), - the maximum Tensor index, i.e., 2**31-1.

In other words,

K <= min(2**31-1, {
  tf.float16: 2**11,
  tf.float32: 2**24,
  tf.float64: 2**53 }[param.dtype])

Examples

alpha = [1., 2., 3.]
n = 2.
dist = DirichletMultinomial(n, alpha)

Creates a 3-class distribution, with the 3rd class is most likely to be drawn. The distribution functions can be evaluated on counts.

# counts same shape as alpha.
counts = [0., 0., 2.]
dist.prob(counts)  # Shape []

# alpha will be broadcast to [[1., 2., 3.], [1., 2., 3.]] to match counts.
counts = [[1., 1., 0.], [1., 0., 1.]]
dist.prob(counts)  # Shape [2]

# alpha will be broadcast to shape [5, 7, 3] to match counts.
counts = [[...]]  # Shape [5, 7, 3]
dist.prob(counts)  # Shape [5, 7]

Creates a 2-batch of 3-class distributions.

alpha = [[1., 2., 3.], [4., 5., 6.]]  # Shape [2, 3]
n = [3., 3.]
dist = DirichletMultinomial(n, alpha)

# counts will be broadcast to [[2., 1., 0.], [2., 1., 0.]] to match alpha.
counts = [2., 1., 0.]
dist.prob(counts)  # Shape [2]

`init`

__init__(
    total_count,
    concentration,
    validate_args=False,
    allow_nan_stats=True,
    name='DirichletMultinomial'
)

Initialize a batch of DirichletMultinomial distributions. (deprecated)

Args:

total_count: Non-negative floating point tensor, whose dtype is the same as concentration. The shape is broadcastable to [N1,..., Nm] with m >= 0. Defines this as a batch of N1 x ... x Nm different Dirichlet multinomial distributions. Its components should be equal to integer values.
concentration: Positive floating point tensor, whose dtype is the same as n with shape broadcastable to [N1,..., Nm, K] m >= 0. Defines this as a batch of N1 x ... x Nm different K class Dirichlet multinomial distributions.
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.

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.

`concentration`

Concentration parameter; expected prior counts for that coordinate.

`dtype`

The DType of Tensors 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.

`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 distributions.FULLY_REPARAMETERIZED or distributions.NOT_REPARAMETERIZED.

Returns:

An instance of ReparameterizationType.

`total_concentration`

Sum of last dim of concentration parameter.

`total_count`

Number of trials used to construct a sample.

`validate_args`

Python bool indicating possibly expensive checks are enabled.

Methods

`tf.distributions.DirichletMultinomial.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.

`tf.distributions.DirichletMultinomial.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.

`tf.distributions.DirichletMultinomial.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).

`tf.distributions.DirichletMultinomial.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.

Additional documentation from DirichletMultinomial:

The covariance for each batch member is defined as the following:

Var(X_j) = n * alpha_j / alpha_0 * (1 - alpha_j / alpha_0) *
(n + alpha_0) / (1 + alpha_0)

where concentration = alpha and total_concentration = alpha_0 = sum_j alpha_j.

The covariance between elements in a batch is defined as:

Cov(X_i, X_j) = -n * alpha_i * alpha_j / alpha_0 ** 2 *
(n + alpha_0) / (1 + alpha_0)

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).

`tf.distributions.DirichletMultinomial.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), (Shanon) 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.

Args:

other: tfp.distributions.Distribution instance.
name: Python str prepended to names of ops created by this function.

Returns:

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

`tf.distributions.DirichletMultinomial.entropy`

entropy(name='entropy')

Shannon entropy in nats.

`tf.distributions.DirichletMultinomial.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.

`tf.distributions.DirichletMultinomial.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.

`tf.distributions.DirichletMultinomial.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.

`tf.distributions.DirichletMultinomial.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 (Shanon) cross entropy, and H[.] denotes (Shanon) entropy.

Args:

other: tfp.distributions.Distribution instance.
name: Python str prepended to names of ops created by this function.

Returns:

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

`tf.distributions.DirichletMultinomial.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.

`tf.distributions.DirichletMultinomial.log_prob`

log_prob(
    value,
    name='log_prob'
)

Log probability density/mass function.

Additional documentation from DirichletMultinomial:

For each batch of counts, value = [n_0, ..., n_{K-1}], P[value] is the probability that after sampling self.total_count draws from this Dirichlet-Multinomial distribution, the number of draws falling in class j is n_j. Since this definition is exchangeable; different sequences have the same counts so the probability includes a combinatorial coefficient.

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.

`tf.distributions.DirichletMultinomial.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.

`tf.distributions.DirichletMultinomial.mean`

mean(name='mean')

Mean.

`tf.distributions.DirichletMultinomial.mode`

mode(name='mode')

Mode.

`tf.distributions.DirichletMultinomial.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.

`tf.distributions.DirichletMultinomial.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.

`tf.distributions.DirichletMultinomial.prob`

prob(
    value,
    name='prob'
)

Probability density/mass function.

Additional documentation from DirichletMultinomial:

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.

`tf.distributions.DirichletMultinomial.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.

`tf.distributions.DirichletMultinomial.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.

`tf.distributions.DirichletMultinomial.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().

`tf.distributions.DirichletMultinomial.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.

`tf.distributions.DirichletMultinomial.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().

tf.distributions.DirichletMultinomial

Class DirichletMultinomial

Aliases:

Mathematical Details

Pitfalls

Examples

__init__

Args:

Properties

allow_nan_stats

Returns:

batch_shape

Returns:

concentration

dtype

event_shape

Returns:

name

parameters

reparameterization_type

Returns:

total_concentration

total_count

validate_args

Methods

tf.distributions.DirichletMultinomial.batch_shape_tensor

Args:

Returns:

tf.distributions.DirichletMultinomial.cdf

Args:

Returns:

tf.distributions.DirichletMultinomial.copy

Args:

Returns:

tf.distributions.DirichletMultinomial.covariance

Args:

Returns:

tf.distributions.DirichletMultinomial.cross_entropy

Args:

Returns:

tf.distributions.DirichletMultinomial.entropy

tf.distributions.DirichletMultinomial.event_shape_tensor

Args:

Returns:

tf.distributions.DirichletMultinomial.is_scalar_batch

Args:

Returns:

tf.distributions.DirichletMultinomial.is_scalar_event

Args:

Returns:

tf.distributions.DirichletMultinomial.kl_divergence

Args:

Returns:

tf.distributions.DirichletMultinomial.log_cdf

Args:

Returns:

tf.distributions.DirichletMultinomial.log_prob

Args:

Returns:

tf.distributions.DirichletMultinomial.log_survival_function

Args:

Returns:

tf.distributions.DirichletMultinomial.mean

tf.distributions.DirichletMultinomial.mode

tf.distributions.DirichletMultinomial.param_shapes

Args:

Returns:

tf.distributions.DirichletMultinomial.param_static_shapes

Args:

Returns:

Raises:

tf.distributions.DirichletMultinomial.prob

Args:

Returns:

tf.distributions.DirichletMultinomial.quantile

Args:

Returns:

tf.distributions.DirichletMultinomial.sample

Args:

Returns:

Class `DirichletMultinomial`

`init`

`allow_nan_stats`

`batch_shape`

`concentration`

`dtype`

`event_shape`

`name`

`parameters`

`reparameterization_type`

`total_concentration`

`total_count`

`validate_args`

`tf.distributions.DirichletMultinomial.batch_shape_tensor`

`tf.distributions.DirichletMultinomial.cdf`

`tf.distributions.DirichletMultinomial.copy`

`tf.distributions.DirichletMultinomial.covariance`

`tf.distributions.DirichletMultinomial.cross_entropy`

`tf.distributions.DirichletMultinomial.entropy`

`tf.distributions.DirichletMultinomial.event_shape_tensor`

`tf.distributions.DirichletMultinomial.is_scalar_batch`

`tf.distributions.DirichletMultinomial.is_scalar_event`

`tf.distributions.DirichletMultinomial.kl_divergence`

`tf.distributions.DirichletMultinomial.log_cdf`

`tf.distributions.DirichletMultinomial.log_prob`

`tf.distributions.DirichletMultinomial.log_survival_function`

`tf.distributions.DirichletMultinomial.mean`

`tf.distributions.DirichletMultinomial.mode`

`tf.distributions.DirichletMultinomial.param_shapes`

`tf.distributions.DirichletMultinomial.param_static_shapes`

`tf.distributions.DirichletMultinomial.prob`

`tf.distributions.DirichletMultinomial.quantile`

`tf.distributions.DirichletMultinomial.sample`

`tf.distributions.DirichletMultinomial.stddev`

`tf.distributions.DirichletMultinomial.survival_function`

`tf.distributions.DirichletMultinomial.variance`