o
    ei"                     @   sV   d dl Z d dl mZ d dlmZmZ d dlmZ d dlmZ dgZ	G dd deZ
dS )    N)Tensor)Categoricalconstraints)MixtureSameFamilyConstraint)DistributionMixtureSameFamilyc                	       s   e Zd ZU dZi Zeeejf e	d< dZ
	d#dedededB ddf fd	d
Zd# fdd	Zejdd ZedefddZedefddZedefddZedefddZdd Zdd Ze fddZdd Zdd  Zd!d" Z  Z S )$r   a  
    The `MixtureSameFamily` distribution implements a (batch of) mixture
    distribution where all component are from different parameterizations of
    the same distribution type. It is parameterized by a `Categorical`
    "selecting distribution" (over `k` component) and a component
    distribution, i.e., a `Distribution` with a rightmost batch shape
    (equal to `[k]`) which indexes each (batch of) component.

    Examples::

        >>> # xdoctest: +SKIP("undefined vars")
        >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally
        >>> # weighted normal distributions
        >>> mix = D.Categorical(torch.ones(5,))
        >>> comp = D.Normal(torch.randn(5,), torch.rand(5,))
        >>> gmm = MixtureSameFamily(mix, comp)

        >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally
        >>> # weighted bivariate normal distributions
        >>> mix = D.Categorical(torch.ones(5,))
        >>> comp = D.Independent(D.Normal(
        ...          torch.randn(5,2), torch.rand(5,2)), 1)
        >>> gmm = MixtureSameFamily(mix, comp)

        >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each
        >>> # consisting of 5 random weighted bivariate normal distributions
        >>> mix = D.Categorical(torch.rand(3,5))
        >>> comp = D.Independent(D.Normal(
        ...         torch.randn(3,5,2), torch.rand(3,5,2)), 1)
        >>> gmm = MixtureSameFamily(mix, comp)

    Args:
        mixture_distribution: `torch.distributions.Categorical`-like
            instance. Manages the probability of selecting component.
            The number of categories must match the rightmost batch
            dimension of the `component_distribution`. Must have either
            scalar `batch_shape` or `batch_shape` matching
            `component_distribution.batch_shape[:-1]`
        component_distribution: `torch.distributions.Distribution`-like
            instance. Right-most batch dimension indexes component.
    arg_constraintsFNmixture_distributioncomponent_distributionvalidate_argsreturnc                    s  || _ || _t| j tstdt| jtstd| j j}| jjd d }tt|t|D ]\}}|dkrJ|dkrJ||krJtd| d| dq/| j j	j
d }| jjd }	|d uro|	d uro||	krotd| d	|	 d|| _| jj}
t|
| _t j||
|d
 d S )NzU The Mixture distribution needs to be an  instance of torch.distributions.CategoricalzUThe Component distribution need to be an instance of torch.distributions.Distribution   z$`mixture_distribution.batch_shape` (z>) is not compatible with `component_distribution.batch_shape`()z"`mixture_distribution component` (z;) does not equal `component_distribution.batch_shape[-1]` (batch_shapeevent_shaper   )_mixture_distribution_component_distribution
isinstancer   
ValueErrorr   r   zipreversedlogitsshape_num_componentr   len_event_ndimssuper__init__)selfr	   r
   r   mdbscdbssize1size2kmkcr   	__class__ q/var/www/addictedbytheproject.nl/epg/venv/lib/python3.10/site-packages/torch/distributions/mixture_same_family.pyr   ;   sH   

zMixtureSameFamily.__init__c                    sx   t |}|| jf }| t|}| j||_| j||_| j|_| j|_|jj	}t
t|j||dd | j|_|S )NFr   )torchSizer   _get_checked_instancer   r   expandr   r   r   r   r   _validate_args)r    r   	_instancebatch_shape_compnewr   r'   r)   r*   r.   o   s   

zMixtureSameFamily.expandc                 C   s   t | jjS N)r   r   supportr    r)   r)   r*   r4      s   zMixtureSameFamily.supportc                 C      | j S r3   )r   r5   r)   r)   r*   r	         z&MixtureSameFamily.mixture_distributionc                 C   r6   r3   )r   r5   r)   r)   r*   r
      r7   z(MixtureSameFamily.component_distributionc                 C   s*   |  | jj}tj|| jj d| j dS Nr   dim)_pad_mixture_dimensionsr	   probsr+   sumr
   meanr   )r    r<   r)   r)   r*   r>      s   zMixtureSameFamily.meanc                 C   s`   |  | jj}tj|| jj d| j d}tj|| jj| 	| j 
d d| j d}|| S )Nr   r9   g       @)r;   r	   r<   r+   r=   r
   variancer   r>   _padpow)r    r<   mean_cond_varvar_cond_meanr)   r)   r*   r?      s   zMixtureSameFamily.variancec                 C   s0   |  |}| j|}| jj}tj|| ddS r8   )r@   r
   cdfr	   r<   r+   r=   )r    xcdf_xmix_probr)   r)   r*   rD      s   
zMixtureSameFamily.cdfc                 C   sJ   | j r| | | |}| j|}tj| jjdd}tj	|| ddS r8   )
r/   _validate_sampler@   r
   log_probr+   log_softmaxr	   r   	logsumexp)r    rE   
log_prob_xlog_mix_probr)   r)   r*   rI      s   

zMixtureSameFamily.log_probc              	   C   s   t  Y t|}t| j}|| }| j}| j|}|j}| j|}|	|t 
dgt|d   }	|	t 
dgt| t 
dg | }	t |||	}
|
|W  d    S 1 s`w   Y  d S )Nr   )r+   no_gradr   r   r   r	   sampler   r
   reshaper,   repeatgathersqueeze)r    sample_shape
sample_len	batch_len
gather_dimes
mix_sample	mix_shapecomp_samplesmix_sample_rsamplesr)   r)   r*   rO      s"   

"$zMixtureSameFamily.samplec                 C   s   | d| j S )Nr   )	unsqueezer   )r    rE   r)   r)   r*   r@      s   zMixtureSameFamily._padc                 C   st   t | j}t | jj}|dkrdn|| }|j}||d d t|dg  |dd   t| jdg  }|S )Nr   r   r   )r   r   r	   r   rP   r+   r,   r   )r    rE   dist_batch_ndimscat_batch_ndims	pad_ndimsxsr)   r)   r*   r;      s   


z)MixtureSameFamily._pad_mixture_dimensionsc                 C   s    d| j  d| j }d| d S )Nz
  z,
  zMixtureSameFamily(r   )r	   r
   )r    args_stringr)   r)   r*   __repr__   s   zMixtureSameFamily.__repr__r3   )!__name__
__module____qualname____doc__r   dictstrr   
Constraint__annotations__has_rsampler   r   boolr   r.   dependent_propertyr4   propertyr	   r
   r   r>   r?   rD   rI   r+   r,   rO   r@   r;   rd   __classcell__r)   r)   r'   r*   r      s>   
 *4

)r+   r   torch.distributionsr   r   torch.distributions.constraintsr    torch.distributions.distributionr   __all__r   r)   r)   r)   r*   <module>   s   