o
    ߗiy                     @   sf   d dl m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gZdd	 ZG d
d deZdS )    )NumberN)constraints)ExponentialFamily)broadcast_all)_sizeGammac                 C   s
   t | S N)torch_standard_gamma)concentration r   W/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/torch/distributions/gamma.pyr
      s   
r
   c                       s   e Zd ZdZejejdZejZdZ	dZ
edd Zedd Zed	d
 Zd fdd	Zd fdd	Ze fdedejfddZdd Zdd Zedd Zdd Zdd Z  ZS )r   aS  
    Creates a Gamma distribution parameterized by shape :attr:`concentration` and :attr:`rate`.

    Example::

        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
        >>> m = Gamma(torch.tensor([1.0]), torch.tensor([1.0]))
        >>> m.sample()  # Gamma distributed with concentration=1 and rate=1
        tensor([ 0.1046])

    Args:
        concentration (float or Tensor): shape parameter of the distribution
            (often referred to as alpha)
        rate (float or Tensor): rate parameter of the distribution
            (often referred to as beta), rate = 1 / scale
    r   rateTr   c                 C   s   | j | j S r   r   selfr   r   r   mean+   s   z
Gamma.meanc                 C   s   | j d | j jddS )N   r   min)r   r   clampr   r   r   r   mode/   s   z
Gamma.modec                 C   s   | j | jd S )N   )r   r   powr   r   r   r   variance3      zGamma.varianceNc                    sN   t ||\| _| _t|trt|trt }n| j }t j	||d d S )Nvalidate_args)
r   r   r   
isinstancer   r	   Sizesizesuper__init__)r   r   r   r   batch_shape	__class__r   r   r"   7   s
   

zGamma.__init__c                    sR   |  t|}t|}| j||_| j||_tt|j|dd | j	|_	|S )NFr   )
_get_checked_instancer   r	   r   r   expandr   r!   r"   _validate_args)r   r#   	_instancenewr$   r   r   r'   ?   s   
zGamma.expandsample_shapereturnc                 C   sD   |  |}t| j|| j| }| jt|j	j
d |S )Nr   )_extended_shaper
   r   r'   r   detachclamp_r	   finfodtypetiny)r   r+   shapevaluer   r   r   rsampleH   s   
zGamma.rsamplec                 C   s`   t j|| jj| jjd}| jr| | t | j| jt | jd | | j|  t 	| j S )N)r1   devicer   )
r	   	as_tensorr   r1   r6   r(   _validate_samplexlogyr   lgammar   r4   r   r   r   log_probR   s   

zGamma.log_probc                 C   s4   | j t| j t| j  d| j  t| j   S )Ng      ?)r   r	   logr   r:   digammar   r   r   r   entropy]   s   

zGamma.entropyc                 C   s   | j d | j fS Nr   r   r   r   r   r   _natural_paramse   r   zGamma._natural_paramsc                 C   s&   t |d |d t |    S r@   )r	   r:   r=   
reciprocal)r   xyr   r   r   _log_normalizeri   s   &zGamma._log_normalizerc                 C   s&   | j r| | tj| j| j| S r   )r(   r8   r	   specialgammaincr   r   r;   r   r   r   cdfl   s   
z	Gamma.cdfr   )__name__
__module____qualname____doc__r   positivearg_constraintsnonnegativesupporthas_rsample_mean_carrier_measurepropertyr   r   r   r"   r'   r	   r   r   Tensorr5   r<   r?   rA   rE   rH   __classcell__r   r   r$   r   r      s.    


	

)numbersr   r	   torch.distributionsr   torch.distributions.exp_familyr   torch.distributions.utilsr   torch.typesr   __all__r
   r   r   r   r   r   <module>   s   