U
    ©Ê¹jœ  ã                   @   sJ   d dl mZ d dlmZ d dlmZ d dlmZ dgZG dd„ deƒZ	dS )é    )Úconstraints)ÚNormal)ÚTransformedDistribution)ÚStickBreakingTransformÚLogisticNormalc                       s`   e Zd ZdZejejdœZejZ	dZ
d‡ fdd„	Zd‡ fdd„	Zed	d
„ ƒZedd„ ƒZ‡  ZS )r   a8  
    Creates a logistic-normal distribution parameterized by :attr:`loc` and :attr:`scale`
    that define the base `Normal` distribution transformed with the
    `StickBreakingTransform` such that::

        X ~ LogisticNormal(loc, scale)
        Y = log(X / (1 - X.cumsum(-1)))[..., :-1] ~ Normal(loc, scale)

    Args:
        loc (float or Tensor): mean of the base distribution
        scale (float or Tensor): standard deviation of the base distribution

    Example::

        >>> # logistic-normal distributed with mean=(0, 0, 0) and stddev=(1, 1, 1)
        >>> # of the base Normal distribution
        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
        >>> m = LogisticNormal(torch.tensor([0.0] * 3), torch.tensor([1.0] * 3))
        >>> m.sample()
        tensor([ 0.7653,  0.0341,  0.0579,  0.1427])

    )ÚlocÚscaleTNc                    s8   t |||d�}|js | dg¡}tƒ j|tƒ |d� d S )N)Úvalidate_argsé   )r   Úbatch_shapeÚexpandÚsuperÚ__init__r   )Úselfr   r   r	   Ú	base_dist©Ú	__class__© úb/var/www/html/bwave/python/venv/lib/python3.8/site-packages/torch/distributions/logistic_normal.pyr   $   s      ÿzLogisticNormal.__init__c                    s   |   t|¡}tƒ j||d�S )N)Ú	_instance)Z_get_checked_instancer   r   r   )r   r   r   Únewr   r   r   r   ,   s    zLogisticNormal.expandc                 C   s
   | j j jS ©N)r   r   ©r   r   r   r   r   0   s    zLogisticNormal.locc                 C   s
   | j j jS r   )r   r   r   r   r   r   r   4   s    zLogisticNormal.scale)N)N)Ú__name__Ú
__module__Ú__qualname__Ú__doc__r   ÚrealZpositiveZarg_constraintsZsimplexZsupportZhas_rsampler   r   Úpropertyr   r   Ú__classcell__r   r   r   r   r   	   s   
N)
Ztorch.distributionsr   Ztorch.distributions.normalr   Z,torch.distributions.transformed_distributionr   Ztorch.distributions.transformsr   Ú__all__r   r   r   r   r   Ú<module>   s
   