mindspore.nn.probability.distribution.Logistic

class mindspore.nn.probability.distribution.Logistic(loc=None, scale=None, seed=None, dtype=mstype.float32, name='Logistic')[source]

Logistic distribution.

Parameters

loc (int, float, list, numpy.ndarray, Tensor) – The location of the Logistic distribution. Default: None.
scale (int, float, list, numpy.ndarray, Tensor) – The scale of the Logistic distribution. Default: None.
seed (int) – The seed used 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: ‘Logistic’.

Supported Platforms:: Ascend GPU

Note

scale must be greater than zero. dist_spec_args are loc and scale. dtype must be a float type because Logistic distributions are continuous.

Examples

>>> import mindspore
>>> import mindspore.nn as nn
>>> import mindspore.nn.probability.distribution as msd
>>> from mindspore import Tensor
>>> # To initialize a Logistic distribution of loc 3.0 and scale 4.0.
>>> l1 = msd.Logistic(3.0, 4.0, dtype=mindspore.float32)
>>> # A Logistic distribution can be initialized without arguments.
>>> # In this case, `loc` and `scale` must be passed in through arguments.
>>> l2 = msd.Logistic(dtype=mindspore.float32)
>>>
>>> # Here are some tensors used below for testing
>>> value = Tensor([1.0, 2.0, 3.0], dtype=mindspore.float32)
>>> loc_a = Tensor([2.0], dtype=mindspore.float32)
>>> scale_a = Tensor([2.0, 2.0, 2.0], dtype=mindspore.float32)
>>> loc_b = Tensor([1.0], dtype=mindspore.float32)
>>> scale_b = Tensor([1.0, 1.5, 2.0], 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 as follows.
>>> # Args:
>>> #     value (Tensor): the value to be evaluated.
>>> #     loc (Tensor): the location of the distribution. Default: self.loc.
>>> #     scale (Tensor): the scale of the distribution. Default: self.scale.
>>> # Examples of `prob`.
>>> # Similar calls can be made to other probability functions
>>> # by replacing 'prob' by the name of the function
>>> ans = l1.prob(value)
>>> print(ans.shape)
(3,)
>>> # Evaluate with respect to distribution b.
>>> ans = l1.prob(value, loc_b, scale_b)
>>> print(ans.shape)
(3,)
>>> # `loc` and `scale` must be passed in during function calls
>>> ans = l1.prob(value, loc_a, scale_a)
>>> print(ans.shape)
(3,)
>>> # Functions `mean`, `mode`, `sd`, `var`, and `entropy` have the same arguments.
>>> # Args:
>>> #     loc (Tensor): the location of the distribution. Default: self.loc.
>>> #     scale (Tensor): the scale of the distribution. Default: self.scale.
>>> # Example of `mean`. `mode`, `sd`, `var`, and `entropy` are similar.
>>> ans = l1.mean()
>>> print(ans.shape)
()
>>> ans = l1.mean(loc_b, scale_b)
>>> print(ans.shape)
(3,)
>>> # `loc` and `scale` must be passed in during function calls.
>>> ans = l1.mean(loc_a, scale_a)
>>> print(ans.shape)
(3,)
>>> # Examples of `sample`.
>>> # Args:
>>> #     shape (tuple): the shape of the sample. Default: ()
>>> #     loc (Tensor): the location of the distribution. Default: self.loc.
>>> #     scale (Tensor): the scale of the distribution. Default: self.scale.
>>> ans = l1.sample()
>>> print(ans.shape)
()
>>> ans = l1.sample((2,3))
>>> print(ans.shape)
(2, 3)
>>> ans = l1.sample((2,3), loc_b, scale_b)
>>> print(ans.shape)
(2, 3, 3)
>>> ans = l1.sample((2,3), loc_a, scale_a)
>>> print(ans.shape)
(2, 3, 3)

extend_repr()[source]: Display instance object as string.

property loc: Return the location of the distribution after casting to dtype.

property scale: Return the scale of the distribution after casting to dtype.