o
    ei                     @   sZ   d dl mZ d dlmZ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ZdS )	    )Tensor)constraintsIndependent)Normal)TransformedDistribution)StickBreakingTransformLogisticNormalc                	       s   e Zd ZU dZejejdZejZ	dZ
ee ed< 	ddeeB deeB dedB d	df fd
dZd fdd	Zed	efddZed	efddZ  ZS )r   a9  
    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-deterministic")
        >>> m = LogisticNormal(torch.tensor([0.0] * 3), torch.tensor([1.0] * 3))
        >>> m.sample()
        tensor([ 0.7653,  0.0341,  0.0579,  0.1427])

    )locscaleT	base_distNr	   r
   validate_argsreturnc                    s8   t |||d}|js|dg}t j|t |d d S )N)r      )r   batch_shapeexpandsuper__init__r   )selfr	   r
   r   r   	__class__ m/var/www/addictedbytheproject.nl/epg/venv/lib/python3.10/site-packages/torch/distributions/logistic_normal.pyr   ,   s   
zLogisticNormal.__init__c                    s   |  t|}t j||dS )N)	_instance)_get_checked_instancer   r   r   )r   r   r   newr   r   r   r   9   s   zLogisticNormal.expandc                 C   
   | j j jS N)r   r	   r   r   r   r   r	   =      
zLogisticNormal.locc                 C   r   r   )r   r
   r   r   r   r   r
   A   r   zLogisticNormal.scaler   )__name__
__module____qualname____doc__r   realpositivearg_constraintssimplexsupporthas_rsampler   r   __annotations__r   floatboolr   r   propertyr	   r
   __classcell__r   r   r   r   r      s*   
 N)torchr   torch.distributionsr   r   torch.distributions.normalr   ,torch.distributions.transformed_distributionr   torch.distributions.transformsr   __all__r   r   r   r   r   <module>   s   