tj(ddlZGddZdS)Nc eZdZdZdZdZddZ dded ej dzd ej dzd ej fd Z dd ed ej dzd ej dzd ej fdZ dZ dZdZdZdS) 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]]) iRFNc||jks|dkrtd|jd||_||_||_t jd}t j||j|t j |_ t j |j |j|js,t j|j|t j |_ n| |j t j|_|jd|jzz dd|_d |_dS) 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 O/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/torch/quasirandom.py__init__zSobolEngine.__init__2s. t{ " "i!mm9*.+999     "l5!!+ t{3ej    -dot~NNN} T^CuzRRRDJJ NN   Z%%E4K%LL !Z!T[.8AA!RHHrnoutr returncF|tj}|jdkr|dkr|j|}ntj|j|dz |j|j|j|\}|_tj |j||fd}n9tj|j||j|j|jdz |\}|_|xj|z c_|*| | ||S|S)ak 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`` Nrr)r )dim) rget_default_dtyper!r to_sobol_engine_drawrrrcat resize_as_copy_)r"r&r'r results r#drawzSobolEngine.drawMs4& =+--E   " "Avv*--e44%*%=JEON& &&&" D$5$8$8$?$?#HbQQQ!&!9 "Q& """ FDJ a ? NN6 " " ( ( 0 0 0J r%mc d|z}|j|z}||dz zdks&td|jd|jd|d|d |||| S) aI Function to draw a sequence of :attr:`2**m` points from a Sobol sequence. Note that the samples are dependent on the previous samples. The size of the result is :math:`(2**m, dimension)`. Args: m (Int): The (base2) exponent of the number of points to draw. out (Tensor, optional): The output tensor dtype (:class:`torch.dtype`, optional): the desired data type of the returned tensor. Default: ``None`` r rrzFThe balance properties of Sobol' points require n to be a power of 2. z0 points have been previously generated, then: n=z+2**=zH. If you still want to do this, please use 'SobolEngine.draw()' instead.)r&r'r )r!rr3)r"r4r'r r&total_ns r# draw_base2zSobolEngine.draw_base2s$ qD$q(7Q;'1,,0)-);00151C00IJ00MT000 yy1#Uy333r%cR|j|jd|_|S)zF Function to reset the ``SobolEngine`` to base state. r)rr1rr!)r"s r#resetzSobolEngine.resets) $$$ r%c|jdkr0tj|j|dz |j|j|jn/tj|j||j|j|jdz |xj|z c_|S)a 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_rrr)r"r&s r# fast_forwardzSobolEngine.fast_forwards   " "  # AE4?DNDDV      # At@RUV@V    a r%c ,d}|j-tj}||jtjd}tjd|j|jf||}tj|tj dtj d|j||_ |j|j|jf}tjd||| }tj |j||jdS)Nr r )r generatorr)r )rr Generator manual_seedr randintrrmvpowarangertril_sobol_engine_scramble_r)r"gr shift_intsltm_dimsltms r#rzSobolEngine._scrambles$( 9 !!A MM$) $ $ $l5!!]  ,SA   X  !U\!T[%M%M%MNN   NDK=mAxqAAAFFHH %dosDNKKKKKr%cd|jg}|jr|dgz }|j|d|jgz }|jjdzd|zdzS)Nz dimension=z scramble=Truezseed=(z, ))rrr __class____name__join)r" fmt_strings r#__repr__zSobolEngine.__repr__sq34>334 = , ?+ +J 9 .49../ /J~&,tyy/D/DDsJJr%)FN)rNN)NN)rP __module__ __qualname____doc__rrr$intrTensorr r3r8r:r=rrSr%r#rrs5&&PF F:#'$( 33 3\D 3{T! 3  3333p$($( 44 4\D 4{T! 4  4444<(LLL,KKKKKr%r)rrrYr%r#rZsV RKRKRKRKRKRKRKRKRKRKr%