[Th ,SSKJr SSKr"SS5rg))OptionalNc \rSrSrSrSrSrSSjrSS\S\ \ RS \ \ RS \ R4S jjr SS \S\ \ RS \ \ RS \ R4S jjrSrSrSrSrSrg) 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]]) iRNcXR:dUS:a[SURS35eX0lX lXl[ R "S5n[ R"XRU[ RS9Ul [ R"URUR5 UR(d4[ R"URU[ RS9Ul OUR5 URR[ RS9UlUR SUR-- R#SS5UlS Ulg) Nz9Supported range of dimensionality for SobolEngine is [1, ]cpu)devicedtype) memory_formatr)MAXDIM ValueErrorseedscramble dimensiontorchr zerosMAXBITlong sobolstate_sobol_engine_initialize_state_shift _scrambleclonecontiguous_formatquasireshape _first_point num_generated)selfrrrr s I/var/www/auris/envauris/lib/python3.13/site-packages/torch/quasirandom.py__init__SobolEngine.__init__3s {{ "i!m**.++a9    "ll5!++ {{3ejj  --doot~~N}}T^^CuzzRDJ NN ZZ%%E4K4K%L !ZZ!T[[.8AA!RHnoutr returnc Uc[R"5nURS:XaUS:XaURR U5nO[R "UR US- URURURUS9uo@l[R"URR U5U4SS9nOK[R "UR UURURURS- US9uo@lU=RU- slUb"URU5RU5 U$U$)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`` rr )r )dim) rget_default_dtyper#r"to_sobol_engine_drawr rrcat resize_as_copy_)r$r)r*r results r%drawSobolEngine.drawNs& =++-E    "Av**--e4%*%=%=JJEOONN&& &" D$5$5$8$8$?#HbQ!&!9!9 ""Q& " FJ a ? NN6 " ( ( 0J r(mc SU-nURU-nXUS- -S:Xd,[SURSURSUSUS3 5eURXBUS 9$) a Function to draw a sequence of :attr:`2**m` points from a Sobol sequence. Note that the samples are dependent on the previous samples. The size of the result is :math:`(2**m, dimension)`. Args: m (Int): The (base2) exponent of the number of points to draw. out (Tensor, optional): The output tensor dtype (:class:`torch.dtype`, optional): the desired data type of the returned tensor. Default: ``None`` rr rzFThe balance properties of Sobol' points require n to be a power of 2. z0 points have been previously generated, then: n=z+2**=zH. If you still want to do this, please use 'SobolEngine.draw()' instead.)r)r*r )r#rr6)r$r8r*r r)total_ns r% draw_base2SobolEngine.draw_base2s$ qD$$q(Q;'1,))-););(<=1151C1C0DD1WIV00 yy1Uy33r(c^URRUR5 SUlU$)z6 Function to reset the ``SobolEngine`` to base state. r)r r4rr#)r$s r%resetSobolEngine.resets' $ r(cdURS:XaF[R"URUS- URUR UR5 OD[R"URXRUR URS- 5 U=RU- slU$)z Function to fast-forward the state of the ``SobolEngine`` by :attr:`n` steps. This is equivalent to drawing :attr:`n` samples without using the samples. Args: n (Int): The number of steps to fast-forward by. rr )r#r_sobol_engine_ff_r rr)r$r)s r% fast_forwardSobolEngine.fast_forwards    "  # # AE4??DNNDDVDV   # # A@R@RUV@V  a r(c SnURb0[R"5nURUR5 [R"S5n[R "SUR UR4X!S9n[R"U[R"S[R"SURUS955Ul UR URUR4n[R "SXBUS9R5n[R"URXPR 5 g)Nr r)r generatorr)r )rr Generator manual_seedr randintrrmvpowarangertril_sobol_engine_scramble_r)r$gr shift_intsltm_dimsltms r%rSobolEngine._scrambles'+ 99 !A MM$)) $ll5!]]  ,S XX  !U\\!T[[%MN  NNDKK=mmAxqAFFH %%doosNNKr(cSUR3/nUR(aUS/- nURbUSUR3/- nURRS-SR U5-S-$)Nz dimension=z scramble=Truezseed=(z, ))rrr __class____name__join)r$ fmt_strings r%__repr__SobolEngine.__repr__st"4>>"234 == ?+ +J 99 U499+./ /J~~&&,tyy/DDsJJr()r"rr#r rrrr)FN)r NN)NN)rX __module__ __qualname____firstlineno____doc__rrr&intrrTensorr r6r<r?rCrr[__static_attributes__r(r%rrs&PF F:&*'+ 3 3ell #3 $ 3  3p'+'+ 4 4ell #4 $ 4  4<(L,Kr(r)typingrrrrdr(r%rfs RKRKr(