tf.compat.v1.distributions.Uniform

Uniform distribution with low and high parameters.

Inherits From: Distribution

tf.compat.v1.distributions.Uniform(
    low=0.0, high=1.0, validate_args=False, allow_nan_stats=True, name='Uniform'
)

Mathematical Details

The probability density function (pdf) is,

pdf(x; a, b) = I[a <= x < b] / Z
Z = b - a

where

• low = a,
• high = b,
• Z is the normalizing constant, and
• I[predicate] is the indicator function for predicate.

The parameters low and high must be shaped in a way that supports broadcasting (e.g., high - low is a valid operation).

Examples

# Without broadcasting:
u1 = Uniform(low=3.0, high=4.0)  # a single uniform distribution [3, 4]
u2 = Uniform(low=[1.0, 2.0],
             high=[3.0, 4.0])  # 2 distributions [1, 3], [2, 4]
u3 = Uniform(low=[[1.0, 2.0],
                  [3.0, 4.0]],
             high=[[1.5, 2.5],
                   [3.5, 4.5]])  # 4 distributions

# With broadcasting:
u1 = Uniform(low=3.0, high=[5.0, 6.0, 7.0])  # 3 distributions

Args
low	Floating point tensor, lower boundary of the output interval. Must have low < high.
high	Floating point tensor, upper boundary of the output interval. Must have low < high.
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
InvalidArgumentError	if low >= high and validate_args=False.

Attributes
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.
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.
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.
high	Upper boundary of the output interval.
low	Lower boundary of the output interval.
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.
validate_args	Python bool indicating possibly expensive checks are enabled.

Methods

batch_shape_tensor

View source

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

View source

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

View source

copy(
    **override_parameters_kwargs
)

Creates a deep copy of the distribution.

Note: the copy distribution may continue to depend on the original initialization arguments.

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).