Source code for torch.quasirandom

import torch
from typing import Optional

[docs]class SobolEngine(object): r""" 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 1111. It uses direction numbers to generate these sequences, and these numbers have been adapted from `here <>`_. 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:: >>> soboleng = torch.quasirandom.SobolEngine(dimension=5) >>> soboleng.draw(3) tensor([[0.5000, 0.5000, 0.5000, 0.5000, 0.5000], [0.7500, 0.2500, 0.7500, 0.2500, 0.7500], [0.2500, 0.7500, 0.2500, 0.7500, 0.2500]]) """ MAXBIT = 30 MAXDIM = 1111 def __init__(self, dimension, scramble=False, seed=None): if dimension > self.MAXDIM or dimension < 1: raise ValueError("Supported range of dimensionality " f"for SobolEngine is [1, {self.MAXDIM}]") self.seed = seed self.scramble = scramble self.dimension = dimension cpu = torch.device("cpu") self.sobolstate = torch.zeros(dimension, self.MAXBIT, device=cpu, dtype=torch.long) torch._sobol_engine_initialize_state_(self.sobolstate, self.dimension) if self.scramble: g: Optional[torch.Generator] = None if self.seed is not None: g = torch.Generator() g.manual_seed(self.seed) shift_ints = torch.randint(2, (self.dimension, self.MAXBIT), device=cpu, generator=g) self.shift =, torch.pow(2, torch.arange(0, self.MAXBIT, device=cpu))) ltm_dims = (self.dimension, self.MAXBIT, self.MAXBIT) ltm = torch.randint(2, ltm_dims, device=cpu, generator=g).tril() torch._sobol_engine_scramble_(self.sobolstate, ltm, self.dimension) else: self.shift = torch.zeros(self.dimension, device=cpu, dtype=torch.long) self.quasi = self.shift.clone(memory_format=torch.contiguous_format) self.num_generated = 0
[docs] def draw(self, n=1, out=None, dtype=torch.float32): r""" 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: ``torch.float32`` """ result, self.quasi = torch._sobol_engine_draw(self.quasi, n, self.sobolstate, self.dimension, self.num_generated, dtype=dtype) self.num_generated += n if out is not None: out.resize_as_(result).copy_(result) return out return result
[docs] def reset(self): r""" Function to reset the ``SobolEngine`` to base state. """ self.quasi.copy_(self.shift) self.num_generated = 0 return self
[docs] def fast_forward(self, n): r""" 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. """ torch._sobol_engine_ff_(self.quasi, n, self.sobolstate, self.dimension, self.num_generated) self.num_generated += n return self
def __repr__(self): fmt_string = [f'dimension={self.dimension}'] if self.scramble: fmt_string += ['scramble=True'] if self.seed is not None: fmt_string += [f'seed={self.seed}'] return self.__class__.__name__ + '(' + ', '.join(fmt_string) + ')'


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