U
    9%e                     @   sf   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 dgZG dd deZdS )    N)infnan)Chi2constraints)Distribution)_standard_normalbroadcast_allStudentTc                       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 )r	   a  
    Creates a Student's t-distribution parameterized by degree of
    freedom :attr:`df`, mean :attr:`loc` and scale :attr:`scale`.

    Example::

        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
        >>> 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Zmemory_format   )r   clonetorchcontiguous_formatr   r
   selfm r   [/var/www/html/Darija-Ai-API/env/lib/python3.8/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    zStudentT.variance              ?Nc                    sB   t |||\| _| _| _t| j| _| j }t j||d d S )Nvalidate_args)	r   r
   r   r   r   _chi2sizesuper__init__)r   r
   r   r   r   batch_shape	__class__r   r   r#   ;   s    
zStudentT.__init__c                    sn   |  t|}t|}| j||_| j||_| j||_| j||_t	t|j
|dd | j|_|S )NFr   )Z_get_checked_instancer	   r   Sizer
   expandr   r   r    r"   r#   _validate_args)r   r$   Z	_instancenewr%   r   r   r(   A   s    
zStudentT.expandc                 C   sP   |  |}t|| jj| jjd}| j|}|t|| j  }| j	| j
|  S )N)dtypedevice)Z_extended_shaper   r
   r+   r,   r    rsampler   Zrsqrtr   r   )r   Zsample_shapeshapeXZYr   r   r   r-   L   s
    
zStudentT.rsamplec                 C   s   | j r| | || j | j }| j d| j   dttj  t	d| j  t	d| jd   }d| jd  t
|d | j  | S )N      ?r   g      g       @)r)   Z_validate_sampler   r   logr
   mathpir   lgammalog1p)r   valueyr0   r   r   r   log_probZ   s    
zStudentT.log_probc                 C   s|   t d| j td t d| jd   }| j d| jd  t d| jd  t d| j    d| j   | S )Nr2   r   )r   r6   r
   r4   r   r3   Zdigamma)r   Zlbetar   r   r   entropyg   s$    "zStudentT.entropy)r   r   N)N)__name__
__module____qualname____doc__r   ZpositiverealZarg_constraintsZsupportZhas_rsamplepropertyr   r   r   r#   r(   r   r'   r-   r:   r;   __classcell__r   r   r%   r   r	      s$   


)r4   r   r   r   Ztorch.distributionsr   r   Z torch.distributions.distributionr   Ztorch.distributions.utilsr   r   __all__r	   r   r   r   r   <module>   s   