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mindspore.nn.probability.distribution.HalfNormal

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

HalfNormal distribution. A HalfNormal distribution is a continuous distribution with the range $[\mu ,inf)$ and the probability density function:

$$f(x,\mu ,\sigma )=1/\sigma \sqrt{2\pi }\exp (-(x-\mu {)}^2/2{\sigma }^2).$$

where $\mu ,\sigma$ are the mean and the standard deviation of the half normal distribution respectively.

Parameters

• mean (int, float, list, numpy.ndarray, Tensor) – The mean of the distribution. Default: None.

• sd (int, float, list, numpy.ndarray, Tensor) – The standard deviation of the 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: ‘HalfNormal’.

Note

• sd must be greater than zero.

• dist_spec_args are mean and sd.

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

Raises

• ValueError – When sd <= 0.

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

Supported Platforms:

CPU

Examples

>>> import mindspore
>>> import mindspore.nn as nn
>>> from mindspore.nn.probability.distribution import HalfNormal
>>> from mindspore import Tensor
>>> # To initialize a HalfNormal distribution of the mean 3.0 and the standard deviation 4.0.
>>> n1 = HalfNormal(3.0, 4.0, dtype=mindspore.float32)
>>> # A HalfNormal distribution can be initialized without arguments.
>>> # In this case, `mean` and `sd` must be passed in through arguments.
>>> hn = HalfNormal(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, 2.5], 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 = hn.log_prob(value, mean_a, sd_a)
>>> print(ans.shape)
(3,)

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

the log value of the probability.

Parameters

• value (Tensor) - the value to compute.

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

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

Returns

Tensor, the log value of the probability.