o
    h                     @   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memory_format   )r   clonetorchcontiguous_formatr   r
   selfm r   R/var/www/html/ai/venv/lib/python3.10/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    )_get_checked_instancer	   r   Sizer
   expandr   r   r"   r$   r%   _validate_args)r   r&   	_instancenewr'   r   r   r+   A   s   
zStudentT.expandc                 C   sP   |  |}t|| jj| jjd}| j|}|t|| j  }| j	| j
|  S )N)dtypedevice)_extended_shaper   r
   r/   r0   r"   rsampler   rsqrtr   r   )r   sample_shapeshapeXZYr   r   r   r2   L   s
   
zStudentT.rsamplec                 C   s   | j r| | || j | j }| j d| j   dttj  t	d| j  t	d| jd   }d| jd  t
|d | j  | S )N      ?r   g      g       @)r,   _validate_sampler   r   logr
   mathpir   lgammalog1p)r   valueyr7   r   r   r   log_probZ   s   
&zStudentT.log_probc                 C   s|   t d| j td t d| jd   }| j d| jd  t d| jd  t d| j    d| j   | S )Nr9   r   )r   r>   r
   r<   r   r;   digamma)r   lbetar   r   r   entropyg   s$   "zStudentT.entropy)r   r   Nr   )__name__
__module____qualname____doc__r   positiverealarg_constraintssupporthas_rsamplepropertyr   r   r   r%   r+   r   r*   r2   rB   rE   __classcell__r   r   r'   r   r	      s&    


)r<   r   r   r   torch.distributionsr   r    torch.distributions.distributionr   torch.distributions.utilsr   r   __all__r	   r   r   r   r   <module>   s    