rhU+ddlZddlmZddlZddlmZddlmZddlmZddl m Z m Z ddl m Z dgZd Zd Zd ZGd deZy) N)Optional)Tensor) constraints) Distribution)_standard_normal lazy_property)_sizeMultivariateNormalcjtj||jdjdS)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. )torchmatmul unsqueezesqueeze)bmatbvecs z/var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/torch/distributions/multivariate_normal.py _batch_mvrs) <<dnnR0 1 9 9" ==c$|jd}|jdd}t|}|jdz }||z }||z}|d|zz}|jd|} t |jdd|j|dD]\} } | | | z| fz } | |fz } |j | }t t|t t||dzt t|dz|dz|gz} |j| }|j d||} |j d| jd|}|jddd}tjj| |djdjd}|j}|j |jdd}t t|}t|D]}|||z||zgz }|j|}|j |S) aK Computes the squared Mahalanobis distance :math:`\mathbf{x}^\top\mathbf{M}^{-1}\mathbf{x}` for a factored :math:`\mathbf{M} = \mathbf{L}\mathbf{L}^\top`. Accepts batches for both bL and bx. They are not necessarily assumed to have the same batch shape, but `bL` one should be able to broadcasted to `bx` one. r NrFupper)sizeshapelendimzipreshapelistrangepermuter linalgsolve_triangularpowsumt)bLbxnbx_batch_shape bx_batch_dims bL_batch_dimsouter_batch_dimsold_batch_dimsnew_batch_dims bx_new_shapesLsx permute_dimsflat_Lflat_x flat_x_swapM_swapM permuted_Mpermute_inv_dimsi reshaped_Ms r_batch_mahalanobisr@s0  AXXcr]N'MFFHqLM$}4% 5N%M(99N88--.LbhhsmRXX.>r%BC'Br2& 'QDL L !B U# $% u%~q9 : ; u%)>1= > ?    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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`. If :attr:`covariance_matrix` or :attr:`precision_matrix` is passed instead, it is only used to compute the corresponding lower triangular matrices using a Cholesky decomposition. )loccovariance_matrixprecision_matrix scale_trilTNrPrQrRrS validate_argsreturnc|jdkr td|du|duz|duzdk7r td|h|jdkr tdtj|jdd|jdd}|j |dz|_n|h|jdkr td tj|jdd|jdd}|j |dz|_nk|J|jdkr td tj|jdd|jdd}|j |dz|_|j |d z|_ |jjdd}t|-||| |||_ y|%tjj||_ yt||_ y) Nrz%loc must be at least one-dimensional.zTExactly one of covariance_matrix or precision_matrix or scale_tril may be specified.rzZscale_tril matrix must be at least two-dimensional, with optional leading batch dimensionsrr )r r zZcovariance_matrix must be at least two-dimensional, with optional leading batch dimensionszYprecision_matrix must be at least two-dimensional, with optional leading batch dimensions)r rT)r ValueErrorr broadcast_shapesrexpandrSrQrRrPsuper__init___unbroadcasted_scale_trilr%rErN) selfrPrQrRrSrT batch_shape event_shape __class__s rr\zMultivariateNormal.__init__s# 779q=DE E T )j.D E D (  f   !~~!# = 001A1A#21F RUSUWK(// h0FGDO  * $$&* = 00!'',ciinK&7%=%=kH>T%UD "#/ //##%) = 00 &&s+SYYs^K%5$;$;K(CN  .#'#8#8#?#? #JC   #/ )) 0 "00 rc|jj|j|jz|jzSN)r]rZ _batch_shape _event_shaper^s rrSzMultivariateNormal.scale_trils:--44    1 1 1D4E4E E  rctj|j|jjj |j |j z|j zSrl)r rr]mTrZrmrnros rrQz$MultivariateNormal.covariance_matrixsQ||  * *D,J,J,M,M &""T%6%669J9JJ K Lrctj|jj|j|j z|j zSrl)r cholesky_inverser]rZrmrnros rrRz#MultivariateNormal.precision_matrixsE%%d&D&DELL    1 1 1D4E4E E  rc|jSrlrPros rmeanzMultivariateNormal.mean xxrc|jSrlruros rmodezMultivariateNormal.moderwrc|jjdjdj|j|j zS)Nrr )r]r'r(rZrmrnros rvariancezMultivariateNormal.variancesA  * * . .q 1 SW VD%%(9(99 : r sample_shapec|j|}t||jj|jj}|jt |j |zS)NrB)_extended_shaperrPrCrDrr])r^r|repss rrsamplezMultivariateNormal.rsamplesL$$\2uDHHNN488??Sxx)D$B$BCHHHrcx|jr|j|||jz }t|j|}|jj ddj jd}d|jdtj dtjzz|zz|z S)Nrr dim1dim2grr) rf_validate_samplerPr@r]diagonallogr(rnmathpi)r^valuediffr; half_log_dets rlog_probzMultivariateNormal.log_probs     ! !% (txx t==t D  * * 3 3" 3 E I I K O OPR S t((+dhhq477{.CCaGH<WWrc^|jjddjjd}d|jdzdt jdt j zzz|z}t|jdk(r|S|j|jS)Nrr rg?rg?r) r]rrr(rnrrrrmrZ)r^rHs rentropyzMultivariateNormal.entropys  * * 3 3" 3 E I I K O OPR S  $##A& &#TWW0E*E F U t  !Q &H88D--. .r)NNNNrl)__name__ __module__ __qualname____doc__r real_vectorpositive_definitelower_choleskyarg_constraintssupport has_rsamplerrboolr\rZrrSrQrRpropertyrvryr{r rdr rrr __classcell__)ras@rr r Ys"J&&(::'99!00 O %%GK /3-1'+(, 8X 8X$F+8X#6* 8X V$ 8X  ~ 8X 8Xt& F  L6LL  &  ff &  -7EJJLIEIVI X/r)rtypingrr rtorch.distributionsr torch.distributions.distributionrtorch.distributions.utilsrr torch.typesr __all__rr@rNr rrrsE  +9E  >/.d t/t/r