+ &j"^RIt!RR4tR#)Ncza]tRt^toRt^tRtRRltRV3RlRlltRV3RlRllt R t R t R t R t R tVtR#) SobolEnginea The :class:`torch.quasirandom.SobolEngine` is an engine for generating (scrambled) Sobol sequences. Sobol sequences are an example of low discrepancy quasi-random sequences. This implementation of an engine for Sobol sequences is capable of sampling sequences up to a maximum dimension of 21201. It uses direction numbers from https://web.maths.unsw.edu.au/~fkuo/sobol/ obtained using the search criterion D(6) up to the dimension 21201. This is the recommended choice by the authors. References: - Art B. Owen. Scrambling Sobol and Niederreiter-Xing points. Journal of Complexity, 14(4):466-489, December 1998. - I. M. Sobol. The distribution of points in a cube and the accurate evaluation of integrals. Zh. Vychisl. Mat. i Mat. Phys., 7:784-802, 1967. Args: dimension (Int): The dimensionality of the sequence to be drawn scramble (bool, optional): Setting this to ``True`` will produce scrambled Sobol sequences. Scrambling is capable of producing better Sobol sequences. Default: ``False``. seed (Int, optional): This is the seed for the scrambling. The seed of the random number generator is set to this, if specified. Otherwise, it uses a random seed. Default: ``None`` Examples:: >>> # xdoctest: +SKIP("unseeded random state") >>> soboleng = torch.quasirandom.SobolEngine(dimension=5) >>> soboleng.draw(3) tensor([[0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.5000, 0.5000, 0.5000, 0.5000, 0.5000], [0.7500, 0.2500, 0.2500, 0.2500, 0.7500]]) iRNcWP8gV^8d\RVP R24hW0nW nWn\ P !R4p\ P!WPV\ PR7Vn \ P!VPVP4VP'g8\ P!VPV\ PR7Vn MVP4VPP\ PR7VnVP ^VP,, P#^R4Vn^VnR#)z9Supported range of dimensionality for SobolEngine is [1, ]cpu)devicedtype) memory_formatN)MAXDIM ValueErrorseedscramble dimensiontorchr zerosMAXBITlong sobolstate_sobol_engine_initialize_state_shift _scrambleclonecontiguous_formatquasireshape _first_point num_generated)selfrrrrs&&&& i/Users/jameslopez/projects/CWCArchive/cwc-podcast/.venv/lib/python3.14/site-packages/torch/quasirandom.py__init__SobolEngine.__init__2s {{ "i!m**.++a9    "ll5!++ {{3ejj  --doot~~N}}}T^^CuzzRDJ NN ZZ%%E4K4K%L !ZZ!T[[.8AA!RHc<V^8dQhRS[RS[PR,RS[PR,RS[P/#)noutNr returnintrTensorr )format __classdict__s"r! __annotate__SobolEngine.__annotate__MsI33 3\\D 3{{T! 3  3r$c  Vf\P!4pVP^8XdV^8XdVPP V4pM\P !VP V^, VPVPVPVR7wq@n\P!VPP V4V3RR7pMR\P !VP VVPVPVP^, VR7wq@nV;PV, unVe#VPV4PV4V#V#)a Function to draw a sequence of :attr:`n` points from a Sobol sequence. Note that the samples are dependent on the previous samples. The size of the result is :math:`(n, dimension)`. Args: n (Int, optional): The length of sequence of points to draw. Default: 1 out (Tensor, optional): The output tensor dtype (:class:`torch.dtype`, optional): the desired data type of the returned tensor. Default: ``None`` )r )dim) rget_default_dtyperrto_sobol_engine_drawrrrcat resize_as_copy_)r r'r(r results&&&& r!drawSobolEngine.drawMs& =++-E    "Av**--e4%*%=%=JJEOONN&& &" D$5$5$8$8$?#HbQ!&!9!9 ""Q& " FJ a ? NN6 " ( ( 0J r$c<V^8dQhRS[RS[PR,RS[PR,RS[P/#)r&mr(Nr r)r*)r-r.s"r!r/r0sI44 4\\D 4{{T! 4  4r$c  ^V,pVPV,pWU^, ,^8Xg-\RVP RVP RV RV R2 4hVPWBVR7#)a Function to draw a sequence of :attr:`2**m` points from a Sobol sequence. Note that the samples are dependent on the previous samples. The size of the result is :math:`(2**m, dimension)`. Args: m (Int): The (base2) exponent of the number of points to draw. out (Tensor, optional): The output tensor dtype (:class:`torch.dtype`, optional): the desired data type of the returned tensor. Default: ``None`` zFThe balance properties of Sobol' points require n to be a power of 2. z0 points have been previously generated, then: n=z+2**=zH. If you still want to do this, please use 'SobolEngine.draw()' instead.)r'r(r )rrr;)r r>r(r r'total_ns&&&& r! draw_base2SobolEngine.draw_base2s$ qD$$q(Q;'1,))-););(<=1151C1C0DD1WIV00 yy1Uy33r$c ^VPPVP4^VnV#)z6 Function to reset the ``SobolEngine`` to base state. )rr9rr)r s&r!resetSobolEngine.resets' $ r$c ~VP^8XdK\P!VPV^, VPVP VP4MH\P!VPWPVP VP^, 4V;PV, unV#)z Function to fast-forward the state of the ``SobolEngine`` by :attr:`n` steps. This is equivalent to drawing :attr:`n` samples without using the samples. Args: n (Int): The number of steps to fast-forward by. )rr_sobol_engine_ff_rrr)r r's&&r! fast_forwardSobolEngine.fast_forwards    "  # # AE4??DNNDDVDV   # # A@R@RUV@V  a r$c RpVPe1\P!4pVPVP4\P!R4p\P !^VP VP3W!R7p\P!V\P!^\P!^VPVR744Vn VP VPVP3p\P !^WBVR7P4p\P!VPWPP 4R#)Nr)r generator)r )rr Generator manual_seedr randintrrmvpowarangertril_sobol_engine_scramble_r)r gr shift_intsltm_dimsltms& r!rSobolEngine._scrambles$( 99 !A MM$)) $ll5!]]  ,S XX  !U\\!T[[%MN  NNDKK=mmAxqAFFH %%doosNNKr$cRVP 2.pVP'd VR., pVPeVRVP 2., pVPPR,RP V4,R,#)z dimension=z scramble=Truezseed=(z, ))rrr __class____name__join)r fmt_strings& r!__repr__SobolEngine.__repr__sv"4>>"234 === ?+ +J 99 U499+./ /J~~&&,tyy/DDsJJr$)rrrrrrrr)FN)rNN)NN)r^ __module__ __qualname____firstlineno____doc__rr r"r;rBrErIrra__static_attributes____classdictcell__)r.s@r!rrsM&PF F633j44<(L,KKr$r)rrr$r!rjs RKRKr$