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mindspore.nn.probability.distribution.Laplace

class mindspore.nn.probability.distribution.Laplace(mean=None, sd=None, seed=None, dtype=mstype.float32, name='Laplace')

Laplace distribution. A Laplace distribution is a continuous distribution with the range $(-inf,inf)$ and the probability density function:

$$f(x,\mu ,b)=1/(2\ast b)\ast \exp (-abs(x-\mu )/b).$$

where $\mu ,b$ are the mean and the scale of the laplace distribution respectively.

Parameters

• mean (Union[int, float, list, numpy.ndarray, Tensor], optional) – The mean of the distribution. If this arg is None, then the mean of the distribution will be passed in runtime. Default: None.

• sd (Union[int, float, list, numpy.ndarray, Tensor], optional) – The scale of the distribution. If this arg is None, then the scale of the distribution will be passed in runtime. Default: None.

• seed (int, optional) – The seed used in sampling. The global seed is used if it is None. Default: None.

• dtype (mindspore.dtype, optional) – The type of the event samples. Default: mstype.float32.

• name (str, optional) – The name of the distribution. Default: ‘Laplace’.

Note

• sd must be greater than zero.

• dtype must be a float type because Laplace distributions are continuous.

• If the arg mean or sd is passed in runtime, then it will be used as the parameter value. Otherwise, the value passed in the constructor will be used.

Raises

• ValueError – When sd <= 0.

• TypeError – When the input dtype is not a subclass of float.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import mindspore.nn as nn
>>> from mindspore.nn.probability.distribution import Laplace
>>> from mindspore import Tensor
>>> # To initialize a Laplace distribution of the mean 3.0 and the scale 4.0.
>>> n1 = Laplace(3.0, 4.0, dtype=mindspore.float32)
>>> # A Laplace distribution can be initialized without arguments.
>>> # In this case, `mean` and `sd` must be passed in through arguments.
>>> n2 = Laplace(dtype=mindspore.float32)
>>> # Here are some tensors used below for testing
>>> value = Tensor([1.0, 2.0, 3.0], dtype=mindspore.float32)
>>> mean_a = Tensor([2.0], dtype=mindspore.float32)
>>> sd_a = Tensor([2.0, 2.0, 2.0], dtype=mindspore.float32)
>>> mean_b = Tensor([1.0], dtype=mindspore.float32)
>>> sd_b = Tensor([1.0, 1.5, 2.0], dtype=mindspore.float32)
>>> ans = n1.log_prob(value)
>>> print(ans.shape)
(3,)
>>> # Evaluate with respect to the distribution b.
>>> ans = n1.log_prob(value, mean_b, sd_b)
>>> print(ans.shape)
(3,)
>>> # `mean` and `sd` must be passed in during function calls
>>> ans = n2.log_prob(value, mean_a, sd_a)
>>> print(ans.shape)
(3,)

log_prob(value, mean=None, sd=None)

Evaluate log probability of the value of the Laplace distribution.

Parameters

• value (Tensor) - the value to compute.

• mean (Tensor, optional) - the mean of the distribution. Default: None.

• sd (Tensor, optional) - the standard deviation of the distribution. Default: None.

Returns

Tensor, the log value of the probability.