
    Vh                     p    d dl Z d dlZd dlmZ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e	      Zy)	    N)infTensor)constraints)Normal)TransformedDistribution)AbsTransform
HalfNormalc                        e Zd ZdZdej
                  iZej                  ZdZ	d fd	Z
d fd	Zedefd       Zedefd       Zedefd	       Zedefd
       Zd Zd Zd Zd Z x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
    scaleTc                 V    t        d|d      }t        | 	  |t               |       y )Nr   F)validate_args)r   super__init__r   )selfr   r   	base_dist	__class__s       O/home/dcms/DCMS/lib/python3.12/site-packages/torch/distributions/half_normal.pyr   zHalfNormal.__init__%   s'    1e59	LN-P    c                 R    | j                  t        |      }t        |   ||      S )N)	_instance)_get_checked_instancer	   r   expand)r   batch_shaper   newr   s       r   r   zHalfNormal.expand)   s(    ((Y?w~kS~99r   returnc                 .    | j                   j                  S N)r   r   r   s    r   r   zHalfNormal.scale-   s    ~~###r   c                 h    | j                   t        j                  dt        j                  z        z  S N   )r   mathsqrtpir   s    r   meanzHalfNormal.mean1   s"    zzDIIa$''k222r   c                 @    t        j                  | j                        S r   )torch
zeros_liker   r   s    r   modezHalfNormal.mode5   s    

++r   c                 f    | j                   j                  d      ddt        j                  z  z
  z  S Nr!      )r   powr"   r$   r   s    r   variancezHalfNormal.variance9   s&    zz~~a ADGGO44r   c                     | j                   r| j                  |       | j                  j                  |      t	        j
                  d      z   }t        j                  |dk\  |t               }|S )Nr!   r   )	_validate_args_validate_sampler   log_probr"   logr'   wherer   )r   valuer2   s      r   r2   zHalfNormal.log_prob=   sW    !!%(>>**51DHHQK?;;uz8cT:r   c                 ~    | j                   r| j                  |       d| j                  j                  |      z  dz
  S r+   )r0   r1   r   cdf)r   r5   s     r   r7   zHalfNormal.cdfD   s8    !!%(4>>%%e,,q00r   c                 D    | j                   j                  |dz   dz        S )Nr,   r!   )r   icdf)r   probs     r   r9   zHalfNormal.icdfI   s    ~~""D1H>22r   c                 b    | j                   j                         t        j                  d      z
  S r    )r   entropyr"   r3   r   s    r   r<   zHalfNormal.entropyL   s"    ~~%%'$((1+55r   r   )__name__
__module____qualname____doc__r   positivearg_constraintsnonnegativesupporthas_rsampler   r   propertyr   r   r%   r)   r.   r2   r7   r9   r<   __classcell__)r   s   @r   r	   r	      s    "  4 45O%%GKQ: $v $ $ 3f 3 3 ,f , , 5& 5 51
36r   )r"   r'   r   r   torch.distributionsr   torch.distributions.normalr   ,torch.distributions.transformed_distributionr   torch.distributions.transformsr   __all__r	    r   r   <module>rN      s0       + - P 7 .>6( >6r   