a hU+@sddlZddlmZddlZddlmZddlmZddlmZddl m Z m Z ddl m Z dgZd d Zd d Zd dZGdddeZdS)N)Optional)Tensor) constraints) Distribution)_standard_normal lazy_property)_sizeMultivariateNormalcCst||ddS)a Performs a batched matrix-vector product, with compatible but different batch shapes. This function takes as input `bmat`, containing :math:`n \times n` matrices, and `bvec`, containing length :math:`n` vectors. Both `bmat` and `bvec` may have any number of leading dimensions, which correspond to a batch shape. They are not necessarily assumed to have the same batch shape, just ones which can be broadcasted. )torchmatmulZ unsqueezeZsqueeze)ZbmatZbvecr U/var/www/auris/lib/python3.9/site-packages/torch/distributions/multivariate_normal.py _batch_mvs rcCs|d}|jdd}t|}|d}||}||}|d|}|jd|} t|jdd|j|dD]\} } | | | | f7} qt| |f7} || }tt|tt||dtt|d|d|g} || }|d||} |d| d|}|ddd}t j j | |dd d d}|}||jdd}tt|}t|D]}|||||g7}q`||}||S) aK Computes the squared Mahalanobis distance :math:`\mathbf{x}^\top\mathbf{M}^{-1}\mathbf{x}` for a factored :math:`\mathbf{M} = \mathbf{L}\mathbf{L}^\top`. Accepts batches for both bL and bx. They are not necessarily assumed to have the same batch shape, but `bL` one should be able to broadcasted to `bx` one. r NrFupper)sizeshapelendimzipZreshapelistrangeZpermuter linalgsolve_triangularpowsumt)ZbLbxnZbx_batch_shapeZ bx_batch_dimsZ bL_batch_dimsZouter_batch_dimsZold_batch_dimsZnew_batch_dimsZ bx_new_shapeZsLZsxZ permute_dimsZflat_LZflat_xZ flat_x_swapZM_swapMZ permuted_MZpermute_inv_dimsiZ reshaped_Mr r r_batch_mahalanobissB   &       r%cCsZtjt|d}tt|ddd}tj|jd|j|jd}tjj ||dd}|S)N)rr rr dtypedeviceFr) r rcholeskyZflipZ transposeZeyerr'r(r)PZLfZL_invZIdLr r r_precision_to_scale_trilPs r,cseZdZdZejejejejdZejZ dZ de e e e e e e e e ddfdd Zdfdd Zee d d d Zee d d dZee d ddZee d ddZee d ddZee d ddZefee dddZddZddZZS) r a Creates a multivariate normal (also called Gaussian) distribution parameterized by a mean vector and a covariance matrix. 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Example: >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) >>> # xdoctest: +IGNORE_WANT("non-deterministic") >>> m = MultivariateNormal(torch.zeros(2), torch.eye(2)) >>> m.sample() # normally distributed with mean=`[0,0]` and covariance_matrix=`I` tensor([-0.2102, -0.5429]) Args: loc (Tensor): mean of the distribution covariance_matrix (Tensor): positive-definite covariance matrix precision_matrix (Tensor): positive-definite precision matrix scale_tril (Tensor): lower-triangular factor of covariance, with positive-valued diagonal Note: Only one of :attr:`covariance_matrix` or :attr:`precision_matrix` or :attr:`scale_tril` can be specified. Using :attr:`scale_tril` will be more efficient: all computations internally are based on :attr:`scale_tril`. 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