a
    MSic                     @   sd   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	 d dl
mZ G dd	 d	eZdS )
    N)inf)Binomial)Distribution)Categorical)constraints)broadcast_allc                       s   e Zd ZU dZejejdZee	d< e
dd Ze
dd Zd  fd
d	Zd! fdd	Zdd Zejddddd Ze
dd Ze
dd Ze
dd Ze fddZdd Zdd Z  ZS )"Multinomiala#  
    Creates a Multinomial distribution parameterized by :attr:`total_count` and
    either :attr:`probs` or :attr:`logits` (but not both). The innermost dimension of
    :attr:`probs` indexes over categories. All other dimensions index over batches.

    Note that :attr:`total_count` need not be specified if only :meth:`log_prob` is
    called (see example below)

    .. note:: The `probs` argument must be non-negative, finite and have a non-zero sum,
              and it will be normalized to sum to 1 along the last dimension. :attr:`probs`
              will return this normalized value.
              The `logits` argument will be interpreted as unnormalized log probabilities
              and can therefore be any real number. It will likewise be normalized so that
              the resulting probabilities sum to 1 along the last dimension. :attr:`logits`
              will return this normalized value.

    -   :meth:`sample` requires a single shared `total_count` for all
        parameters and samples.
    -   :meth:`log_prob` allows different `total_count` for each parameter and
        sample.

    Example::

        >>> m = Multinomial(100, torch.tensor([ 1., 1., 1., 1.]))
        >>> x = m.sample()  # equal probability of 0, 1, 2, 3
        tensor([ 21.,  24.,  30.,  25.])

        >>> Multinomial(probs=torch.tensor([1., 1., 1., 1.])).log_prob(x)
        tensor([-4.1338])

    Args:
        total_count (int): number of trials
        probs (Tensor): event probabilities
        logits (Tensor): event log probabilities (unnormalized)
    probslogitstotal_countc                 C   s   | j | j S N)r
   r   self r   [/var/www/html/django/DPS/env/lib/python3.9/site-packages/torch/distributions/multinomial.pymean2   s    zMultinomial.meanc                 C   s   | j | j d| j  S )N   r   r
   r   r   r   r   variance6   s    zMultinomial.variancer   Nc                    sh   t |tstd|| _t||d| _t|| jd| _| jj	}| jj
dd  }tt| j|||d d S )Nz*inhomogeneous total_count is not supportedr	   r   validate_args)
isinstanceintNotImplementedErrorr   r   _categoricalr   r
   	_binomialbatch_shapeparam_shapesuperr   __init__)r   r   r
   r   r   r   event_shape	__class__r   r   r!   :   s    
zMultinomial.__init__c                    sP   |  t|}t|}| j|_| j||_tt|j|| j	dd | j
|_
|S )NFr   )_get_checked_instancer   torchSizer   r   expandr    r!   r"   _validate_args)r   r   	_instancenewr#   r   r   r(   D   s    
zMultinomial.expandc                 O   s   | j j|i |S r   )r   _new)r   argskwargsr   r   r   r,   M   s    zMultinomial._newT)is_discrete	event_dimc                 C   s   t | jS r   )r   multinomialr   r   r   r   r   supportP   s    zMultinomial.supportc                 C   s   | j jS r   )r   r   r   r   r   r   r   T   s    zMultinomial.logitsc                 C   s   | j jS r   )r   r
   r   r   r   r   r
   X   s    zMultinomial.probsc                 C   s   | j jS r   )r   r   r   r   r   r   r   \   s    zMultinomial.param_shapec                 C   s   t |}| jt | jf| }tt| }||	d |j
| }|| | }|d|t | || jS )Nr   r   )r&   r'   r   sampler   listrangedimappendpoppermuter+   _extended_shapezero_scatter_add_	ones_liketype_asr
   )r   sample_shapesamplesZshifted_idxcountsr   r   r   r3   `   s    

zMultinomial.samplec                 C   s|   t | j}| j }|| t |d  }| jjdddd  }t | j	|}t |d }|| 
ddg}|| S )Nr   F)r(   r   r   )r&   tensorr   r   entropylgammar   enumerate_supportexplog_probsum)r   nZcat_entropyterm1r2   Zbinomial_probsweightsterm2r   r   r   rC   l   s    
zMultinomial.entropyc                 C   s   | j r| | t| j|\}}|jtjd}t|dd }t|d d}d||dk|t	 k@ < || d}|| | S )N)memory_formatr   r   r   )
r)   _validate_sampler   r   cloner&   contiguous_formatrD   rH   r   )r   valuer   log_factorial_nZlog_factorial_xsZ
log_powersr   r   r   rG   y   s    
zMultinomial.log_prob)r   NNN)N)__name__
__module____qualname____doc__r   simplexreal_vectorarg_constraintsr   __annotations__propertyr   r   r!   r(   r,   dependent_propertyr2   r   r
   r   r&   r'   r3   rC   rG   __classcell__r   r   r#   r   r   
   s.   
#


	



r   )r&   Z
torch._sixr   Ztorch.distributions.binomialr    torch.distributions.distributionr   Ztorch.distributionsr   r   torch.distributions.utilsr   r   r   r   r   r   <module>   s   