mindspore.nn.probability.distribution.Uniform

class mindspore.nn.probability.distribution.Uniform(low=None, high=None, seed=None, dtype=mstype.float32, name='Uniform')[source]

Uniform Distribution. A Uniform distributio is a continuous distribution with the range \([a, b]\) and the probability density function:

\[f(x, a, b) = 1 / (b - a)\]

Where \(a, b\) are the lower and upper bound respectively.

Parameters

low (int, float, list, numpy.ndarray, Tensor) – The lower bound of the distribution. Default: None .
high (int, float, list, numpy.ndarray, Tensor) – The upper bound of the distribution. Default: None .
seed (int) – The seed uses in sampling. The global seed is used if it is None . Default: None .
dtype (mindspore.dtype) – The type of the event samples. Default: mstype.float32 .
name (str) – The name of the distribution. Default: 'Uniform' .

Note

low must be strictly less than high. dist_spec_args are high and low. dtype must be float type because Uniform distributions are continuous.

Raises

ValueError – When high <= low.
TypeError – When the input dtype is not a subclass of float.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> import mindspore.nn as nn
>>> import mindspore.nn.probability.distribution as msd
>>> from mindspore import Tensor
>>> # To initialize a Uniform distribution of the lower bound 0.0 and the higher bound 1.0.
>>> u1 = msd.Uniform(0.0, 1.0, dtype=mindspore.float32)
>>> # A Uniform distribution can be initialized without arguments.
>>> # In this case, `high` and `low` must be passed in through arguments during function calls.
>>> u2 = msd.Uniform(dtype=mindspore.float32)
>>>
>>> # Here are some tensors used below for testing
>>> value = Tensor([0.5, 0.8], dtype=mindspore.float32)
>>> low_a = Tensor([0., 0.], dtype=mindspore.float32)
>>> high_a = Tensor([2.0, 4.0], dtype=mindspore.float32)
>>> low_b = Tensor([-1.5], dtype=mindspore.float32)
>>> high_b = Tensor([2.5, 5.], dtype=mindspore.float32)
>>> # Private interfaces of probability functions corresponding to public interfaces, including
>>> # `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`, have the same arguments.
>>> # Args:
>>> #     value (Tensor): the value to be evaluated.
>>> #     low (Tensor): the lower bound of the distribution. Default: self.low.
>>> #     high (Tensor): the higher bound of the distribution. Default: self.high.
>>> # Examples of `prob`.
>>> # Similar calls can be made to other probability functions
>>> # by replacing 'prob' by the name of the function.
>>> ans = u1.prob(value)
>>> print(ans.shape)
(2,)
>>> # Evaluate with respect to distribution b.
>>> ans = u1.prob(value, low_b, high_b)
>>> print(ans.shape)
(2,)
>>> # `high` and `low` must be passed in during function calls.
>>> ans = u2.prob(value, low_a, high_a)
>>> print(ans.shape)
(2,)
>>> # Functions `mean`, `sd`, `var`, and `entropy` have the same arguments.
>>> # Args:
>>> #     low (Tensor): the lower bound of the distribution. Default: self.low.
>>> #     high (Tensor): the higher bound of the distribution. Default: self.high.
>>> # Examples of `mean`. `sd`, `var`, and `entropy` are similar.
>>> ans = u1.mean() # return 0.5
>>> print(ans.shape)
()
>>> ans = u1.mean(low_b, high_b) # return (low_b + high_b) / 2
>>> print(ans.shape)
(2,)
>>> # `high` and `low` must be passed in during function calls.
>>> ans = u2.mean(low_a, high_a)
>>> print(ans.shape)
(2,)
>>> # Interfaces of 'kl_loss' and 'cross_entropy' are the same.
>>> # Args:
>>> #     dist (str): the type of the distributions. Should be "Uniform" in this case.
>>> #     low_b (Tensor): the lower bound of distribution b.
>>> #     high_b (Tensor): the upper bound of distribution b.
>>> #     low_a (Tensor): the lower bound of distribution a. Default: self.low.
>>> #     high_a (Tensor): the upper bound of distribution a. Default: self.high.
>>> # Examples of `kl_loss`. `cross_entropy` is similar.
>>> ans = u1.kl_loss('Uniform', low_b, high_b)
>>> print(ans.shape)
(2,)
>>> ans = u1.kl_loss('Uniform', low_b, high_b, low_a, high_a)
>>> print(ans.shape)
(2,)
>>> # Additional `high` and `low` must be passed in.
>>> ans = u2.kl_loss('Uniform', low_b, high_b, low_a, high_a)
>>> print(ans.shape)
(2,)
>>> # Examples of `sample`.
>>> # Args:
>>> #     shape (tuple): the shape of the sample. Default: ()
>>> #     low (Tensor): the lower bound of the distribution. Default: self.low.
>>> #     high (Tensor): the upper bound of the distribution. Default: self.high.
>>> ans = u1.sample()
>>> print(ans.shape)
()
>>> ans = u1.sample((2,3))
>>> print(ans.shape)
(2, 3)
>>> ans = u1.sample((2,3), low_b, high_b)
>>> print(ans.shape)
(2, 3, 2)
>>> ans = u2.sample((2,3), low_a, high_a)
>>> print(ans.shape)
(2, 3, 2)

property high

Return the upper bound of the distribution.

Returns: Tensor, the upper bound of the distribution.

property low

Return the upper bound of the distribution.

Returns: Tensor, the lower bound of the distribution.

cdf(value, high, low)

Compute the cumulatuve distribution function(CDF) of the given value.

Parameters

value (Tensor) - the value to compute.
high (Tensor) - the upper bound of the distribution. Default: None .
low (Tensor) - the lower bound of the distribution. Default: None .

Returns

Tensor, the value of the cumulatuve distribution function for the given input.

cross_entropy(dist, high_b, low_b, high, low)

Compute the cross entropy of two distribution.

Parameters

dist (str) - the type of the other distribution.
high_b (Tensor) - the upper bound of the other distribution.
low_b (Tensor) - the lower bound of the other distribution.
high (Tensor) - the upper bound of the distribution. Default: None .
low (Tensor) - the lower bound of the distribution. Default: None .

Returns

Tensor, the value of the cross entropy.

entropy(high, low)

Compute the value of the entropy.

Parameters

high (Tensor) - the upper bound of the distribution. Default: None .
low (Tensor) - the lower bound of the distribution. Default: None .

Returns

Tensor, the value of the entropy.

kl_loss(dist, high_b, low_b, high, low)

Compute the value of the K-L loss between two distribution, namely KL(a||b).

Parameters

dist (str) - the type of the other distribution.
high_b (Tensor) - the upper bound of the other distribution.
low_b (Tensor) - the lower bound of the other distribution.
high (Tensor) - the upper bound of the distribution. Default: None .
low (Tensor) - the lower bound of the distribution. Default: None .

Returns

Tensor, the value of the K-L loss.

log_cdf(value, high, low)

Compute the log value of the cumulatuve distribution function.

Parameters

value (Tensor) - the value to compute.
high (Tensor) - the upper bound of the distribution. Default: None .
low (Tensor) - the lower bound of the distribution. Default: None .

Returns

Tensor, the log value of the cumulatuve distribution function.

log_prob(value, high, low)

the log value of the probability.

Parameters

value (Tensor) - the value to compute.
high (Tensor) - the upper bound of the distribution. Default: None .
low (Tensor) - the lower bound of the distribution. Default: None .

Returns

Tensor, the log value of the probability.

log_survival(value, high, low)

Compute the log value of the survival function.

Parameters

value (Tensor) - the value to compute.
high (Tensor) - the upper bound of the distribution. Default: None .
low (Tensor) - the lower bound of the distribution. Default: None .

Returns

Tensor, the value of the K-L loss.

mean(high, low)

Compute the mean value of the distribution.

Parameters

high (Tensor) - the upper bound of the distribution. Default: None .
low (Tensor) - the lower bound of the distribution. Default: None .

Returns

Tensor, the mean of the distribution.

mode(high, low)

Compute the mode value of the distribution.

Parameters

high (Tensor) - the upper bound of the distribution. Default: None .
low (Tensor) - the lower bound of the distribution. Default: None .

Returns

Tensor, the mode of the distribution.

prob(value, high, low)

The probability of the given value. For the continuous distribution, it is the probability density function.

Parameters

value (Tensor) - the value to compute.
high (Tensor) - the upper bound of the distribution. Default: None .
low (Tensor) - the lower bound of the distribution. Default: None .

Returns

Tensor, the value of the probability.

sample(shape, high, low)

Generate samples.

Parameters

shape (tuple) - the shape of the tensor.
high (Tensor) - the upper bound of the distribution. Default: None .
low (Tensor) - the lower bound of the distribution. Default: None .

Returns

Tensor, the sample following the distribution.

sd(high, low)

The standard deviation.

Parameters

high (Tensor) - the upper bound of the distribution. Default: None .
low (Tensor) - the lower bound of the distribution. Default: None .

Returns

Tensor, the standard deviation of the distribution.

survival_function(value, high, low)

Compute the value of the survival function.

Parameters

value (Tensor) - the value to compute.
high (Tensor) - the upper bound of the distribution. Default: None .
low (Tensor) - the lower bound of the distribution. Default: None .

Returns

Tensor, the value of the survival function.

var(high, low)

Compute the variance of the distribution.

Parameters

high (Tensor) - the upper bound of the distribution. Default: None .
low (Tensor) - the lower bound of the distribution. Default: None .

Returns

Tensor, the variance of the distribution.