
    קg                         d dl 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 dlmZmZ dgZd	 Z G d
 de          ZdS )    N)nan)constraints)TransformedDistribution)AffineTransformPowerTransform)Uniform)broadcast_alleuler_constantKumaraswamyc                     d|| z  z   }t          j        |          t          j        |          z   t          j        ||z             z
  }|t          j        |          z  S )zE
    Computes nth moment of Kumaraswamy using using torch.lgamma
       )torchlgammaexp)abnarg1	log_values        [/var/www/html/ai-engine/env/lib/python3.11/site-packages/torch/distributions/kumaraswamy.py_momentsr      sU     q1u9DT""U\!__4u|D1H7M7MMIuy####    c                        e Zd ZdZej        ej        dZej        ZdZ	d fd	Z
d fd	Zed             Zed             Zed	             Zd
 Z xZS )r   aS  
    Samples from a Kumaraswamy distribution.

    Example::

        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
        >>> m = Kumaraswamy(torch.tensor([1.0]), torch.tensor([1.0]))
        >>> m.sample()  # sample from a Kumaraswamy distribution with concentration alpha=1 and beta=1
        tensor([ 0.1729])

    Args:
        concentration1 (float or Tensor): 1st concentration parameter of the distribution
            (often referred to as alpha)
        concentration0 (float or Tensor): 2nd concentration parameter of the distribution
            (often referred to as beta)
    )concentration1concentration0TNc                    t          ||          \  | _        | _        t          j        | j        j                  }t          t          j        | j        d          t          j        | j        d          |          }t          | j        	                                          t          dd          t          | j        	                                          g}t                                          |||           d S )Nr   r   )validate_args)exponentg      ?g      )locscale)r	   r   r   r   finfodtyper   	full_liker   
reciprocalr   super__init__)selfr   r   r   r!   	base_dist
transforms	__class__s          r   r&   zKumaraswamy.__init__/   s    3@N4
 4
0T0 D/566OD/33OD/33'
 
 
	 D$7$B$B$D$DEEE4000D$7$B$B$D$DEEE


 	JmLLLLLr   c                     |                      t          |          }| j                            |          |_        | j                            |          |_        t                                          ||          S )N)	_instance)_get_checked_instancer   r   expandr   r%   )r'   batch_shaper,   newr*   s       r   r.   zKumaraswamy.expand@   sd    ((i@@!077DD!077DDww~~kS~999r   c                 8    t          | j        | j        d          S Nr   )r   r   r   r'   s    r   meanzKumaraswamy.meanF   s    +T-@!DDDr   c                    | j                                         | j                                          z  | j          | j        z                                  z
  }t          || j         dk     | j        dk     z  <   |                                S r2   )r   r$   log1pr   r   r   )r'   log_modes     r   modezKumaraswamy.modeJ   s     **,,1D0D/K/K/M/MM##d&99@@BBC 	 KN$%)d.AA.EFG||~~r   c                 n    t          | j        | j        d          t          j        | j        d          z
  S )N   )r   r   r   r   powr4   r3   s    r   variancezKumaraswamy.varianceT   s8    +T-@!DDuyIqH
 H
 
 	
r   c                 2   d| j                                         z
  }d| j                                        z
  }t          j        | j        dz             t
          z   }|||z  z   t          j        | j                   z
  t          j        | j                  z
  S r2   )r   r$   r   r   digammar
   log)r'   t1t0H0s       r   entropyzKumaraswamy.entropyZ   s    $//111$//111]4.233nD2gi+,,- i+,,-	
r   )N)__name__
__module____qualname____doc__r   positivearg_constraintsunit_intervalsupporthas_rsampler&   r.   propertyr4   r8   r<   rC   __classcell__)r*   s   @r   r   r      s         " &.%. O 'GKM M M M M M": : : : : : E E XE   X 
 
 X

	
 	
 	
 	
 	
 	
 	
r   )r   r   torch.distributionsr   ,torch.distributions.transformed_distributionr   torch.distributions.transformsr   r   torch.distributions.uniformr   torch.distributions.utilsr	   r
   __all__r   r    r   r   <module>rV      s           + + + + + + P P P P P P J J J J J J J J / / / / / / C C C C C C C C /$ $ $L
 L
 L
 L
 L
) L
 L
 L
 L
 L
r   