a
    MSic                     @   s`   d dl Z d dlZd dlmZmZ d dlmZmZ d dlm	Z	 d dl
mZmZ G dd de	ZdS )    N)infnan)Chi2constraints)Distribution)_standard_normalbroadcast_allc                       s   e Zd ZdZejejejdZej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dZdd Zdd Z  ZS )StudentTa  
    Creates a Student's t-distribution parameterized by degree of
    freedom :attr:`df`, mean :attr:`loc` and scale :attr:`scale`.

    Example::

        >>> m = StudentT(torch.tensor([2.0]))
        >>> m.sample()  # Student's t-distributed with degrees of freedom=2
        tensor([ 0.1046])

    Args:
        df (float or Tensor): degrees of freedom
        loc (float or Tensor): mean of the distribution
        scale (float or Tensor): scale of the distribution
    )dflocscaleTc                 C   s"   | j jtjd}t|| jdk< |S )Nmemory_format   )r   clonetorchcontiguous_formatr   r
   selfm r   X/var/www/html/django/DPS/env/lib/python3.9/site-packages/torch/distributions/studentT.pymean   s    zStudentT.meanc                 C   s   | j S )N)r   )r   r   r   r   mode$   s    zStudentT.modec                 C   s~   | j jtjd}| j| j dk d| j | j dk  | j | j dk d  || j dk< t|| j dk| j dk@ < t|| j dk< |S )Nr      r   )r
   r   r   r   r   powr   r   r   r   r   r   variance(   s
    DzStudentT.variance              ?Nc                    sF   t |||\| _| _| _t| j| _| j }tt| j	||d d S )Nvalidate_args)
r   r
   r   r   r   _chi2sizesuperr	   __init__)r   r
   r   r   r    batch_shape	__class__r   r   r$   0   s    
zStudentT.__init__c                    sn   |  t|}t|}| j||_| j||_| j||_| j||_t	t|j
|dd | j|_|S )NFr   )_get_checked_instancer	   r   Sizer
   expandr   r   r!   r#   r$   _validate_args)r   r%   	_instancenewr&   r   r   r*   6   s    
zStudentT.expandc                 C   sP   |  |}t|| jj| jjd}| j|}|t|| j  }| j	| j
|  S )N)dtypedevice)_extended_shaper   r
   r.   r/   r!   rsampler   rsqrtr   r   )r   sample_shapeshapeXZYr   r   r   r1   A   s
    
zStudentT.rsamplec                 C   s   | j r| | || j | j }| j d| j   dttj  t	d| j  t	d| jd   }d| jd  t
|d | j  | S )N      ?r   g      g       @)r+   _validate_sampler   r   logr
   mathpir   lgammalog1p)r   valueyr6   r   r   r   log_probO   s    
zStudentT.log_probc                 C   s|   t d| j td t d| jd   }| j d| jd  t d| jd  t d| j    d| j   | S )Nr8   r   )r   r=   r
   r;   r   r:   digamma)r   Zlbetar   r   r   entropyZ   s    ."zStudentT.entropy)r   r   N)N)__name__
__module____qualname____doc__r   positiverealarg_constraintssupporthas_rsamplepropertyr   r   r   r$   r*   r   r)   r1   rA   rC   __classcell__r   r   r&   r   r	   
   s   


r	   )r;   r   Z
torch._sixr   r   Ztorch.distributionsr   r    torch.distributions.distributionr   torch.distributions.utilsr   r   r	   r   r   r   r   <module>   s   