
    Vh                     n    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	m
Z
 dgZd Z G d	 de      Zy)
    N)Tensor)constraints)ExponentialFamily)broadcast_all)_Number_sizeGammac                 ,    t        j                  |       S N)torch_standard_gamma)concentrations    I/home/dcms/DCMS/lib/python3.12/site-packages/torch/distributions/gamma.pyr   r      s      //    c                   .    e Zd ZdZej
                  ej
                  dZej                  ZdZ	dZ
edefd       Zedefd       Zedefd       Zd fd		Zd fd
	Z ej&                         fdedefdZd Zd Zedeeef   fd       Zd Zd Z x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   returnc                 4    | j                   | j                  z  S r   r   selfs    r   meanz
Gamma.mean+   s    !!DII--r   c                 Z    | j                   dz
  | j                  z  j                  d      S )N   r   min)r   r   clampr   s    r   modez
Gamma.mode/   s*    ##a'4994;;;BBr   c                 R    | j                   | j                  j                  d      z  S )N   )r   r   powr   s    r   variancezGamma.variance3   s     !!DIIMM!$444r   c                     t        ||      \  | _        | _        t        |t              r%t        |t              rt        j                         }n| j                  j                         }t        | %  ||       y )Nvalidate_args)
r   r   r   
isinstancer   r   Sizesizesuper__init__)r   r   r   r%   batch_shape	__class__s        r   r*   zGamma.__init__7   s]    (5mT(J%DImW-*T72K**,K,,113KMBr   c                 *   | j                  t        |      }t        j                  |      }| j                  j                  |      |_        | j                  j                  |      |_        t        t        |#  |d       | j                  |_	        |S )NFr$   )
_get_checked_instancer	   r   r'   r   expandr   r)   r*   _validate_args)r   r+   	_instancenewr,   s       r   r/   zGamma.expand?   sy    ((	:jj- ..55kB99##K0eS";e"D!00
r   sample_shapec                 6   | j                  |      }t        | j                  j                  |            | j                  j                  |      z  }|j                         j                  t        j                  |j                        j                         |S )Nr   )_extended_shaper   r   r/   r   detachclamp_r   finfodtypetiny)r   r3   shapevalues       r   rsamplezGamma.rsampleH   s    $$\2 2 2 9 9% @ADIIDTDTE
 
 	EKK(-- 	 	
 r   c                    t        j                  || j                  j                  | j                  j                        }| j
                  r| j                  |       t        j                  | j                  | j                        t        j                  | j                  dz
  |      z   | j                  |z  z
  t        j                  | j                        z
  S )N)r9   devicer   )
r   	as_tensorr   r9   r?   r0   _validate_samplexlogyr   lgammar   r<   s     r   log_probzGamma.log_probR   s    TYY__TYYEUEUV!!%(KK**DII6kk$,,q0%89ii%  ll4--./	
r   c                     | j                   t        j                  | j                        z
  t        j                  | j                         z   d| j                   z
  t        j
                  | j                         z  z   S )Ng      ?)r   r   logr   rC   digammar   s    r   entropyzGamma.entropy]   sf    ii		"#ll4--./ T'''5==9K9K+LLM	
r   c                 :    | j                   dz
  | j                   fS Nr   r   r   s    r   _natural_paramszGamma._natural_paramse   s    ""Q&
33r   c                     t        j                  |dz         |dz   t        j                  |j                                z  z   S rK   )r   rC   rG   
reciprocal)r   xys      r   _log_normalizerzGamma._log_normalizeri   s4    ||AE"a!euyy!,,./I%IIIr   c                     | j                   r| j                  |       t        j                  j	                  | j
                  | j                  |z        S r   )r0   rA   r   specialgammaincr   r   rD   s     r   cdfz	Gamma.cdfl   s?    !!%(}}%%d&8&8$))e:KLLr   r   )__name__
__module____qualname____doc__r   positivearg_constraintsnonnegativesupporthas_rsample_mean_carrier_measurepropertyr   r   r   r"   r*   r/   r   r'   r   r=   rE   rI   tuplerL   rQ   rU   __classcell__)r,   s   @r   r	   r	      s    $ %--$$O %%GK.f . . Cf C C 5& 5 5C -7EJJL E V 	

 4vv~!6 4 4JMr   )r   r   torch.distributionsr   torch.distributions.exp_familyr   torch.distributions.utilsr   torch.typesr   r   __all__r   r	    r   r   <module>ri      s6      + < 3 & )0^M ^Mr   