
    Ǆg                     t    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 dlmZ dgZ G d	 de      Zy)
    N)constraints)Exponential)euler_constant)TransformedDistribution)AffineTransformPowerTransform)broadcast_allWeibullc                        e Zd ZdZej
                  ej
                  dZej
                  Zd	 fd	Zd	 fd	Z	e
d        Ze
d        Ze
d        Zd Z xZS )
r
   a  
    Samples from a two-parameter Weibull distribution.

    Example:

        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
        >>> m = Weibull(torch.tensor([1.0]), torch.tensor([1.0]))
        >>> m.sample()  # sample from a Weibull distribution with scale=1, concentration=1
        tensor([ 0.4784])

    Args:
        scale (float or Tensor): Scale parameter of distribution (lambda).
        concentration (float or Tensor): Concentration parameter of distribution (k/shape).
    )scaleconcentrationc                 H   t        ||      \  | _        | _        | j                  j                         | _        t        t        j                  | j                        |      }t        | j                        t        d| j                        g}t        | -  |||       y )Nvalidate_argsexponentr   locr   )r	   r   r   
reciprocalconcentration_reciprocalr   torch	ones_liker   r   super__init__)selfr   r   r   	base_dist
transforms	__class__s         c/home/mcse/projects/flask_80/flask-venv/lib/python3.12/site-packages/torch/distributions/weibull.pyr   zWeibull.__init__#   s    )6um)L&
D&(,(:(:(E(E(G%OODJJ'}
	 D$A$AB4

 	JmL    c                    | j                  t        |      }| j                  j                  |      |_        | j                  j                  |      |_        |j                  j                         |_        | j                  j                  |      }t        |j                        t        d|j                        g}t        t        |/  ||d       | j                  |_        |S )Nr   r   r   Fr   )_get_checked_instancer
   r   expandr   r   r   r   r   r   r   r   _validate_args)r   batch_shape	_instancenewr   r   r   s         r   r#   zWeibull.expand/   s    (()<JJ%%k2	 ..55kB'*'8'8'C'C'E$NN))+6	C$@$@A3

 	gs$Y
%$P!00
r    c                     | j                   t        j                  t        j                  d| j                  z               z  S N   )r   r   explgammar   r   s    r   meanzWeibull.mean=   s.    zzEIIell1t7T7T3T&UVVVr    c                     | j                   | j                  dz
  | j                  z  | j                  j                         z  z  S r)   )r   r   r   r-   s    r   modezWeibull.modeA   sE     JJ""Q&$*<*<<!!,,.//	
r    c           	         | j                   j                  d      t        j                  t        j                  dd| j
                  z  z               t        j                  dt        j                  d| j
                  z         z        z
  z  S )N   r*   )r   powr   r+   r,   r   r-   s    r   variancezWeibull.varianceI   sl    zz~~a IIell1q4+H+H'H#HIJiiELLT-J-J)JKKLM
 	
r    c                     t         d| j                  z
  z  t        j                  | j                  | j                  z        z   dz   S r)   )r   r   r   logr   r-   s    r   entropyzWeibull.entropyP   sC    a$"?"??@ii

T%B%BBCD	
r    )N)__name__
__module____qualname____doc__r   positivearg_constraintssupportr   r#   propertyr.   r0   r4   r7   __classcell__)r   s   @r   r
   r
      s~     %%$--O ""G
M W W 
 
 
 

r    )r   torch.distributionsr   torch.distributions.exponentialr   torch.distributions.gumbelr   ,torch.distributions.transformed_distributionr   torch.distributions.transformsr   r   torch.distributions.utilsr	   __all__r
    r    r   <module>rI      s2     + 7 5 P J 3 +G
% G
r    