+ j:,^RIt^RIt^RIHt^RIHt^RIHt^RIHtH t ^RI H t R.t Rt R tR t!R R]4tR#) NTensor) constraints) Distribution)_standard_normal lazy_property)_sizeMultivariateNormalcj\P!WPR44PR4#)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&&h/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/distributions/multivariate_normal.py _batch_mvrs' <<nnR0 1 9 9" ==cVPR4pVPRRp\V4pVP4^, pWE, pWe,pV^V,,pVPRVp \ VPRRVPVR4FwrWV ,V 3, p K W3, p VP V 4p\ \V44\ \Wh^44,\ \V^,V^44,V.,p VPV 4pVP RW"4p VP RV P^4V4pVP^^^4p\PPWRR7P^4PR4pVP4pVP VPRR4p\ \V44p\V4FpVVV,VV,., pK VPV4pVP V4#)a: 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 be broadcasted to `bx` one. NFupperr )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>s  AXXcr]N'MFFHqLM$4%5N%M(99N88--.LbhhsmRXX.>r%BCr2& DDL L !B U# $% u%q9 : ; u%)>1= > ?    L !B ZZA !F ZZFKKNA .F..Aq)K %%f%GKKANRRSUV   A288CR=)JE"234 = !-1>A3EFF"##$45J   n --rc\PP\P!VR44p\P!\P!VR4RR4p\P !VP R,VPVPR7p\PPW#RR7pV#)dtypedeviceFrrr )rr ) r r#choleskyflip transposeeyerrBrCr$)PLfL_invIdLs& r_precision_to_scale_trilrMOs~   uzz!X6 7B OOEJJr84b" =E 1772;aggahh ?B %%eu%=A Hrcaa]tRt^XtoRtR]P R]PR]PR]P/t ]P t Rt RV3RlV3Rlllt RV3R llt ]V3R lR l4t]V3R lR l4t]V3RlRl4t]V3RlRl4t]V3RlRl4t]V3RlRl4t]P.!43V3RlRlltRtRtRtVtV;t#)r a) Creates a multivariate normal (also called Gaussian) distribution parameterized by a mean vector and a covariance matrix. The multivariate normal distribution can be parameterized either in terms of a positive definite covariance matrix :math:`\mathbf{\Sigma}` or a positive definite precision matrix :math:`\mathbf{\Sigma}^{-1}` or a lower-triangular matrix :math:`\mathbf{L}` with positive-valued diagonal entries, such that :math:`\mathbf{\Sigma} = \mathbf{L}\mathbf{L}^\top`. This triangular matrix can be obtained via e.g. Cholesky decomposition of the covariance. 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_trilTc t<V^8dQhRS[RS[R,RS[R,RS[R,RS[R,RR/#)r@rOrPNrQrR validate_argsreturn)rbool)format __classdict__s"r __annotate__MultivariateNormal.__annotate__s_=X=X =X"D==X!4- =X TM =X d{ =X =Xrct<VP4^8d \R4hVRJVRJ,VRJ,^8wd \R4hVepVP4^8d \R4h\P!VPRR VPRR 4pVP VR ,4VnMVepVP4^8d \R4h\P!VPRR VPRR 4pVP VR ,4VnM}Vf \R4hVP4^8d \R4h\P!VPRR VPRR 4pVP VR ,4Vn VP VR ,4Vn VPPR Rp\SV`1WgVR7Ve W@n R#Ve'\PPV4Vn R#\!V4Vn R#) z%loc must be at least one-dimensional.NzTExactly one of covariance_matrix or precision_matrix or scale_tril may be specified.zZscale_tril matrix must be at least two-dimensional, with optional leading batch dimensionszZcovariance_matrix must be at least two-dimensional, with optional leading batch dimensionsz%precision_matrix is unexpectedly NonezYprecision_matrix must be at least two-dimensional, with optional leading batch dimensionsrTrr )r r )r )r ValueErrorr broadcast_shapesrexpandrRrPAssertionErrorrQrOsuper__init___unbroadcasted_scale_trilr#rDrM) selfrOrPrQrRrT batch_shape event_shape __class__s &&&&&& rrcMultivariateNormal.__init__s% 779q=DE E T )j.D E D (  f   !~~!# = 001A1A#21F RUSUWK(// h0FGDO  * $$&* = 00!'',ciinK&7%=%=kH>T%UD "'$%LMM##%) = 00 &&s+SYYs^K%5$;$;K(CN  .#'#8#8#?#? #JC   #/ )) 0 "00 rc <V^8dQhRS[/#r@rUr)rWrXs"rrYrZs  F rcVPPVPVP,VP,4#N)rdr` _batch_shape _event_shaperes&rrRMultivariateNormal.scale_trils:--44    1 1 1D4E4E E  rc <V^8dQhRS[/#rur)rWrXs"rrYrZsLL6Lrc\P!VPVPP4P VP VP ,VP ,4#rw)r rrdmTr`rxryrzs&rrP$MultivariateNormal.covariance_matrixsQ||  * *D,J,J,M,M &""T%6%669J9JJ K Lrc <V^8dQhRS[/#rur)rWrXs"rrYrZs  & rc\P!VP4PVPVP ,VP ,4#rw)r cholesky_inverserdr`rxryrzs&rrQ#MultivariateNormal.precision_matrixsE%%d&D&DELL    1 1 1D4E4E E  rc <V^8dQhRS[/#rur)rWrXs"rrYrZfrcVP#rwrOrzs&rmeanMultivariateNormal.mean xxrc <V^8dQhRS[/#rur)rWrXs"rrYrZrrcVP#rwrrzs&rmodeMultivariateNormal.moderrc <V^8dQhRS[/#rur)rWrXs"rrYrZs  & rcVPP^4PR4PVPVP ,4#)r@r )rdr%r&r`rxryrzs&rvarianceMultivariateNormal.variancesA  * * . .q 1 SW VD%%(9(99 : rc&<V^8dQhRS[RS[/#)r@ sample_shaperU)r r)rWrXs"rrYrZsIIEIVIrcVPV4p\W PPVPPR7pVP\ VP V4,#)rA)_extended_shaperrOrBrCrrd)rerrepss&& rrsampleMultivariateNormal.rsamplesJ$$\2uHHNN488??Sxx)D$B$BCHHHrcVP'dVPV4WP, p\VPV4pVPP RRR7P 4PR4pRVP^,\P !^\P,4,V,,V, #)r@dim1dim2rr g) rn_validate_samplerOr>rddiagonallogr&rymathpi)revaluediffr9 half_log_dets&& rlog_probMultivariateNormal.log_probs      ! !% (xx t==t D  * * 3 3" 3 E I I K O OPR S t((+dhhq477{.CCaGH<WWrcVPPRRR7P4PR4pRVP^,,R\ P!^\ P ,4,,V,p\VP4^8XdV#VPVP4#)r@rg?g?rr ) rdrrr&ryrrrrxr`)rerHs& rentropyMultivariateNormal.entropy s  * * 3 3" 3 E I I K O OPR S  $##A& &#TWW0E*E F U t  !Q &H88D--. .r)rdrPrOrQrR)NNNNrw)__name__ __module__ __qualname____firstlineno____doc__r real_vectorpositive_definitelower_choleskyarg_constraintssupport has_rsamplercr`rrRrPrQpropertyrrrr rlrrr__static_attributes____classdictcell__ __classcell__)rhrXs@@rr r Xs"L {&&[::K99k00 O %%GK=X=X~&  LL     -2JJLII X//r)rr rtorch.distributionsr torch.distributions.distributionrtorch.distributions.utilsrr torch.typesr __all__rr>rMr rrrsB +9E  >/.d z/z/r