SobolEngine

class torch.quasirandom.SobolEngine(dimension, scramble=False, seed=None)

The 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.

Parameters

• 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]])

draw(n=1, out=None, dtype=torch.float32)

Function to draw a sequence of n points from a Sobol sequence. Note that the samples are dependent on the previous samples. The size of the result is $(n, dimension)$ .

Parameters

• n (Int, optional) – The length of sequence of points to draw. Default: 1

• out (Tensor, optional) – The output tensor

• dtype (torch.dtype, optional) – the desired data type of the returned tensor. Default: torch.float32

draw_base2(m, out=None, dtype=torch.float32)

Function to draw a sequence of 2**m points from a Sobol sequence. Note that the samples are dependent on the previous samples. The size of the result is $(2**m, dimension)$ .

Parameters

• m (Int) – The (base2) exponent of the number of points to draw.

• out (Tensor, optional) – The output tensor

• dtype (torch.dtype, optional) – the desired data type of the returned tensor. Default: torch.float32

fast_forward(n)

Function to fast-forward the state of the SobolEngine by n steps. This is equivalent to drawing n samples without using the samples.

Parameters

n (Int) – The number of steps to fast-forward by.

reset()

Function to reset the SobolEngine to base state.