o
    ߗi                     @   sf   d dl Z d dlZd dlmZ 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ZdS )	    N)inf)constraints)Normal)TransformedDistribution)AbsTransform
HalfNormalc                       s   e Zd ZdZdejiZejZdZ	d fdd	Z
d fdd	Zed	d
 Zedd Zedd Zedd Zdd Zdd Zdd Zdd Z  ZS )r   a  
    Creates a half-normal distribution parameterized by `scale` where::

        X ~ Normal(0, scale)
        Y = |X| ~ HalfNormal(scale)

    Example::

        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
        >>> m = HalfNormal(torch.tensor([1.0]))
        >>> m.sample()  # half-normal distributed with scale=1
        tensor([ 0.1046])

    Args:
        scale (float or Tensor): scale of the full Normal distribution
    scaleTNc                    s&   t d|dd}t j|t |d d S )Nr   F)validate_args)r   super__init__r   )selfr   r	   	base_dist	__class__ ]/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/torch/distributions/half_normal.pyr   $   s   zHalfNormal.__init__c                    s   |  t|}t j||dS )N)	_instance)_get_checked_instancer   r
   expand)r   batch_shaper   newr   r   r   r   (   s   zHalfNormal.expandc                 C   s   | j jS N)r   r   r   r   r   r   r   ,   s   zHalfNormal.scalec                 C   s   | j tdtj  S N   )r   mathsqrtpir   r   r   r   mean0   s   zHalfNormal.meanc                 C   s   t | jS r   )torch
zeros_liker   r   r   r   r   mode4   s   zHalfNormal.modec                 C   s   | j dddtj   S Nr      )r   powr   r   r   r   r   r   variance8   s   zHalfNormal.variancec                 C   s>   | j r| | | j|td }t|dk|t }|S )Nr   r   )	_validate_args_validate_sampler   log_probr   logr   wherer   )r   valuer(   r   r   r   r(   <   s
   
zHalfNormal.log_probc                 C   s$   | j r| | d| j| d S r"   )r&   r'   r   cdf)r   r+   r   r   r   r,   C   s   
zHalfNormal.cdfc                 C   s   | j |d d S )Nr#   r   )r   icdf)r   probr   r   r   r-   H      zHalfNormal.icdfc                 C   s   | j  td S r   )r   entropyr   r)   r   r   r   r   r0   K   r/   zHalfNormal.entropyr   )__name__
__module____qualname____doc__r   positivearg_constraintsnonnegativesupporthas_rsampler   r   propertyr   r   r!   r%   r(   r,   r-   r0   __classcell__r   r   r   r   r      s&    




)r   r   r   torch.distributionsr   torch.distributions.normalr   ,torch.distributions.transformed_distributionr   torch.distributions.transformsr   __all__r   r   r   r   r   <module>   s   