Chi2¶

class paddle.distribution. Chi2 ( df: float | Tensor ) [source]

Creates a Chi-squared distribution parameterized by shape parameter. This is exactly equivalent to Gamma(concentration=0.5*df, rate=0.5), Gamma.

Parameters: df (float or Tensor) – The degree of freedom of the distribution, which should be non-negative. If the input data type is Tensor, it indicates the batch creation of distributions with multiple different parameters, and the batch_shape (refer to the Distribution base class) is the parameter.

Example

>>> import paddle
>>> m = paddle.distribution.Chi2(paddle.to_tensor([1.0]))
>>> sample = m.sample()
>>> sample.shape
[1]

property batch_shape : Sequence[int]

Returns batch shape of distribution

entropy ( ) → Tensor entropy¶

Entropy of gamma distribution

property event_shape : Sequence[int]

Returns event shape of distribution

kl_divergence ( other: Gamma ) → Tensor [source] kl_divergence¶

The KL-divergence between two gamma distributions.

log_prob ( value: float | Tensor ) → Tensor log_prob¶

Log probability density function evaluated at value

property mean : Tensor

Mean of gamma distribution.

prob ( value: float | Tensor ) → Tensor prob¶

Probability density function evaluated at value

probs ( value: Tensor ) → Tensor probs¶: Probability density/mass function.

Note

This method will be deprecated in the future, please use prob instead.

rsample ( shape: Sequence[int] = [] ) → Tensor rsample¶

Generate reparameterized samples of the specified shape.

Parameters: shape (Sequence[int], optional) – Shape of the generated samples.
Returns: A tensor with prepended dimensions shape.The data type is float32.
Return type: Tensor

sample ( shape: Sequence[int] = [] ) → Tensor sample¶

Generate samples of the specified shape.

Parameters: shape (Sequence[int], optional) – Shape of the generated samples.
Returns: Tensor, A tensor with prepended dimensions shape.The data type is float32.

property variance : Tensor

Variance of gamma distribution.