# tf.contrib.distributions.Dirichlet

### class tf.contrib.distributions.Dirichlet

Dirichlet distribution.

This distribution is parameterized by a vector alpha of concentration parameters for k classes.

#### Mathematical details

The Dirichlet is a distribution over the standard n-simplex, where the standard n-simplex is defined by: { (x_1, ..., x_n) in R^(n+1) | sum_j x_j = 1 and x_j >= 0 for all j }. The distribution has hyperparameters alpha = (alpha_1,...,alpha_k), and probability mass function (prob):

prob(x) = 1 / Beta(alpha) * prod_j x_j^(alpha_j - 1)

where Beta(x) = prod_j Gamma(x_j) / Gamma(sum_j x_j) is the multivariate beta function.

This class provides methods to create indexed batches of Dirichlet distributions. If the provided alpha is rank 2 or higher, for every fixed set of leading dimensions, the last dimension represents one single Dirichlet distribution. When calling distribution functions (e.g. dist.prob(x)), alpha and x are broadcast to the same shape (if possible). In all cases, the last dimension of alpha/x represents single Dirichlet distributions.

#### Examples

alpha = [1, 2, 3]
dist = Dirichlet(alpha)


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

# x same shape as alpha.
x = [.2, .3, .5]
dist.prob(x)  # Shape []

# alpha will be broadcast to [[1, 2, 3], [1, 2, 3]] to match x.
x = [[.1, .4, .5], [.2, .3, .5]]
dist.prob(x)  # Shape [2]

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


Creates a 2-batch of 3-class distributions.

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

# x will be broadcast to [[2, 1, 0], [2, 1, 0]] to match alpha.
x = [.2, .3, .5]
dist.prob(x)  # Shape [2]


## Properties

### allow_nan_stats

Python boolean 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 boolean.

Shape parameter.

### alpha_sum

Sum of shape parameter.

### dtype

The DType of Tensors handled by this Distribution.

### name

Name prepended to all ops created by this Distribution.

### parameters

Dictionary of parameters used to instantiate this Distribution.

### validate_args

Python boolean indicated possibly expensive checks are enabled.

## Methods

### __init__(alpha, validate_args=False, allow_nan_stats=True, name='Dirichlet')

Initialize a batch of Dirichlet distributions.

#### Args:

• alpha: Positive floating point tensor with shape broadcastable to [N1,..., Nm, k] m >= 0. Defines this as a batch of N1 x ... x Nm different k class Dirichlet distributions.
• validate_args: Boolean, default False. Whether to assert valid values for parameters alpha and x in prob and log_prob. If False, correct behavior is not guaranteed.
• allow_nan_stats: Boolean, 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 prefix Ops created by this distribution class.

Examples:

# Define 1-batch of 2-class Dirichlet distributions,
# also known as a Beta distribution.
dist = Dirichlet([1.1, 2.0])

# Define a 2-batch of 3-class distributions.
dist = Dirichlet([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])


### batch_shape(name='batch_shape')

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

The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents.

#### Args:

• name: name to give to the op

#### Returns:

• batch_shape: Tensor.

### cdf(value, name='cdf', **condition_kwargs)

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: The name to give this op. **condition_kwargs: Named arguments forwarded to subclass implementation.

#### Returns:

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

### 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) intitialized from the union of self.parameters and override_parameters_kwargs, i.e., dict(self.parameters, **override_parameters_kwargs).

### entropy(name='entropy')

Shannon entropy in nats.

### event_shape(name='event_shape')

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.

### get_batch_shape()

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

Same meaning as batch_shape. May be only partially defined.

#### Returns:

• batch_shape: TensorShape, possibly unknown.

### get_event_shape()

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

Same meaning as event_shape. May be only partially defined.

#### Returns:

• event_shape: TensorShape, possibly unknown.

### is_scalar_batch(name='is_scalar_batch')

Indicates that batch_shape == [].

#### Args:

• name: The name to give this op.

#### Returns:

• is_scalar_batch: Boolean scalar Tensor.

### is_scalar_event(name='is_scalar_event')

Indicates that event_shape == [].

#### Args:

• name: The name to give this op.

#### Returns:

• is_scalar_event: Boolean scalar Tensor.

### log_cdf(value, name='log_cdf', **condition_kwargs)

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: The name to give this op. **condition_kwargs: Named arguments forwarded to subclass implementation.

#### Returns:

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

### log_pdf(value, name='log_pdf', **condition_kwargs)

Log probability density function.

#### Args:

• value: float or double Tensor.
• name: The name to give this op. **condition_kwargs: Named arguments forwarded to subclass implementation.

#### Returns:

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

#### Raises:

• TypeError: if not is_continuous.

### log_pmf(value, name='log_pmf', **condition_kwargs)

Log probability mass function.

#### Args:

• value: float or double Tensor.
• name: The name to give this op. **condition_kwargs: Named arguments forwarded to subclass implementation.

#### Returns:

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

#### Raises:

• TypeError: if is_continuous.

### log_prob(value, name='log_prob', **condition_kwargs)

Log probability density/mass function (depending on is_continuous).

Additional documentation from Dirichlet:

Note that the input must be a non-negative tensor with dtype dtype and whose shape can be broadcast with self.alpha. For fixed leading dimensions, the last dimension represents counts for the corresponding Dirichlet distribution in self.alpha. x is only legal if it sums up to one.

#### Args:

• value: float or double Tensor.
• name: The name to give this op. **condition_kwargs: Named arguments forwarded to subclass implementation.

#### Returns:

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

### log_survival_function(value, name='log_survival_function', **condition_kwargs)

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: The name to give this op. **condition_kwargs: Named arguments forwarded to subclass implementation.

#### Returns:

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

Mean.

### mode(name='mode')

Mode.

Additional documentation from Dirichlet:

Note that the mode for the Dirichlet distribution is only defined when alpha > 1. This returns the mode when alpha > 1, and NaN otherwise. If self.allow_nan_stats is False, an exception will be raised rather than returning NaN.

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

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

Subclasses should override static 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(cls, sample_shape)

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

#### 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.

### pdf(value, name='pdf', **condition_kwargs)

Probability density function.

#### Args:

• value: float or double Tensor.
• name: The name to give this op. **condition_kwargs: Named arguments forwarded to subclass implementation.

#### Returns:

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

#### Raises:

• TypeError: if not is_continuous.

### pmf(value, name='pmf', **condition_kwargs)

Probability mass function.

#### Args:

• value: float or double Tensor.
• name: The name to give this op. **condition_kwargs: Named arguments forwarded to subclass implementation.

#### Returns:

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

#### Raises:

• TypeError: if is_continuous.

### prob(value, name='prob', **condition_kwargs)

Probability density/mass function (depending on is_continuous).

Additional documentation from Dirichlet:

Note that the input must be a non-negative tensor with dtype dtype and whose shape can be broadcast with self.alpha. For fixed leading dimensions, the last dimension represents counts for the corresponding Dirichlet distribution in self.alpha. x is only legal if it sums up to one.

#### Args:

• value: float or double Tensor.
• name: The name to give this op. **condition_kwargs: Named arguments forwarded to subclass implementation.

#### Returns:

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

### sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)

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. **condition_kwargs: Named arguments forwarded to subclass implementation.

#### Returns:

• samples: a Tensor with prepended dimensions sample_shape.

### std(name='std')

Standard deviation.

### survival_function(value, name='survival_function', **condition_kwargs)

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: The name to give this op. **condition_kwargs: Named arguments forwarded to subclass implementation.

#### Returns:

Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype.

Variance.