mindspore.nn.probability.distribution.Beta
- class mindspore.nn.probability.distribution.Beta(concentration1=None, concentration0=None, seed=None, dtype=mstype.float32, name='Beta')[source]
Beta distribution. A Beta distributio is a continuous distribution with the range \([0, 1]\) and the probability density function:
\[f(x, \alpha, \beta) = x^\alpha (1-x)^{\beta - 1} / B(\alpha, \beta),\]where \(B\) is the Beta function.
- Parameters
concentration1 (int, float, list, numpy.ndarray, Tensor) – The concentration1, also know as alpha of the Beta distribution. Default: None.
concentration0 (int, float, list, numpy.ndarray, Tensor) – The concentration0, also know as beta of the Beta 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: ‘Beta’.
- Inputs and Outputs of APIs:
The accessible APIs of the Beta distribution are defined in the base class, including:
prob and log_prob
mean, sd, var, and entropy
kl_loss and cross_entropy
sample
For more details of all APIs, including the inputs and outputs of APIs of the Beta distribution please refer to
mindspore.nn.probability.distribution.Distribution, and examples below.- Supported Platforms:
Ascend
Note
concentration1 and concentration0 must be greater than zero. dist_spec_args are concentration1 and concentration0. dtype must be a float type because Beta distributions are continuous.
- Raises
ValueError – When concentration1 <= 0 or concentration0 >=1.
TypeError – When the input dtype is not a subclass of float.
Examples
>>> import mindspore >>> import mindspore.nn as nn >>> import mindspore.nn.probability.distribution as msd >>> from mindspore import Tensor >>> # To initialize a Beta distribution of the concentration1 3.0 and the concentration0 4.0. >>> b1 = msd.Beta([3.0], [4.0], dtype=mindspore.float32) >>> # A Beta distribution can be initialized without arguments. >>> # In this case, `concentration1` and `concentration0` must be passed in through arguments. >>> b2 = msd.Beta(dtype=mindspore.float32) >>> # Here are some tensors used below for testing >>> value = Tensor([0.1, 0.5, 0.8], dtype=mindspore.float32) >>> concentration1_a = Tensor([2.0], dtype=mindspore.float32) >>> concentration0_a = Tensor([2.0, 2.0, 2.0], dtype=mindspore.float32) >>> concentration1_b = Tensor([1.0], dtype=mindspore.float32) >>> concentration0_b = Tensor([1.0, 1.5, 2.0], dtype=mindspore.float32) >>> # Private interfaces of probability functions corresponding to public interfaces, including >>> # `prob` and `log_prob`, have the same arguments as follows. >>> # Args: >>> # value (Tensor): the value to be evaluated. >>> # concentration1 (Tensor): the concentration1 of the distribution. Default: self._concentration1. >>> # concentration0 (Tensor): the concentration0 of the distribution. Default: self._concentration0. >>> # Examples of `prob`. >>> # Similar calls can be made to other probability functions >>> # by replacing 'prob' by the name of the function >>> ans = b1.prob(value) >>> print(ans.shape) (3,) >>> # Evaluate with respect to the distribution b. >>> ans = b1.prob(value, concentration1_b, concentration0_b) >>> print(ans.shape) (3,) >>> # `concentration1` and `concentration0` must be passed in during function calls >>> ans = b2.prob(value, concentration1_a, concentration0_a) >>> print(ans.shape) (3,) >>> # Functions `mean`, `sd`, `mode`, `var`, and `entropy` have the same arguments. >>> # Args: >>> # concentration1 (Tensor): the concentration1 of the distribution. Default: self._concentration1. >>> # concentration0 (Tensor): the concentration0 of the distribution. Default: self._concentration0. >>> # Example of `mean`, `sd`, `mode`, `var`, and `entropy` are similar. >>> ans = b1.mean() >>> print(ans.shape) (1,) >>> ans = b1.mean(concentration1_b, concentration0_b) >>> print(ans.shape) (3,) >>> # `concentration1` and `concentration0` must be passed in during function calls. >>> ans = b2.mean(concentration1_a, concentration0_a) >>> print(ans.shape) (3,) >>> # Interfaces of 'kl_loss' and 'cross_entropy' are the same: >>> # Args: >>> # dist (str): the type of the distributions. Only "Beta" is supported. >>> # concentration1_b (Tensor): the concentration1 of distribution b. >>> # concentration0_b (Tensor): the concentration0 of distribution b. >>> # concentration1_a (Tensor): the concentration1 of distribution a. >>> # Default: self._concentration1. >>> # concentration0_a (Tensor): the concentration0 of distribution a. >>> # Default: self._concentration0. >>> # Examples of `kl_loss`. `cross_entropy` is similar. >>> ans = b1.kl_loss('Beta', concentration1_b, concentration0_b) >>> print(ans.shape) (3,) >>> ans = b1.kl_loss('Beta', concentration1_b, concentration0_b, concentration1_a, concentration0_a) >>> print(ans.shape) (3,) >>> # Additional `concentration1` and `concentration0` must be passed in. >>> ans = b2.kl_loss('Beta', concentration1_b, concentration0_b, concentration1_a, concentration0_a) >>> print(ans.shape) (3,) >>> # Examples of `sample`. >>> # Args: >>> # shape (tuple): the shape of the sample. Default: () >>> # concentration1 (Tensor): the concentration1 of the distribution. Default: self._concentration1. >>> # concentration0 (Tensor): the concentration0 of the distribution. Default: self._concentration0. >>> ans = b1.sample() >>> print(ans.shape) (1,) >>> ans = b1.sample((2,3)) >>> print(ans.shape) (2, 3, 1) >>> ans = b1.sample((2,3), concentration1_b, concentration0_b) >>> print(ans.shape) (2, 3, 3) >>> ans = b2.sample((2,3), concentration1_a, concentration0_a) >>> print(ans.shape) (2, 3, 3)
- property concentration0
Return the concentration0, also know as the beta of the Beta distribution, after casting to dtype.
- Output:
Tensor, the concentration2 parameter of the distribution.
- property concentration1
Return the concentration1, also know as the alpha of the Beta distribution, after casting to dtype.
- Output:
Tensor, the concentration1 parameter of the distribution.