Quasi Monte Carlo Rd Sampling

[arvoelke]

Resolves #152. Code is modified from http://extremelearning.com.au/unreasonable-effectiveness-of-quasirandom-sequences/

This is a new method for sampling points from the sphere and ball (encoders and evaluation points).
It is still possible to use the old method by passing base=Sobol() to the constructor of all of the distributions, instead of the new default of base=Rd(). The biggest benefit is the new method works for arbitrary dimension, whereas the old method regressed to Nengo's independent distribution for d > 40.

Some plots from the new method (to be in the documentation upon release):

Versus the old method (Sobol):

[codecov-io]

Codecov Report

Merging #153 into master will not change coverage.
The diff coverage is 100%.

@@          Coverage Diff          @@
##           master   #153   +/-   ##
=====================================
  Coverage     100%   100%           
=====================================
  Files          29     29           
  Lines        1374   1398   +24     
  Branches      157    162    +5     
=====================================
+ Hits         1374   1398   +24

Impacted Files	Coverage Δ
nengolib/stats/ntmdists.py	100% <100%> (ø)	⬆️

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[arvoelke]

@astoeckel Since you were using the Halton sequence, this might be of interest to you. This is a new quasi-random sequence, that's been implemented as a Nengo distribution. You can use it to generate scattered points on the cube, sphere, or ball.

Links to staging documentation (with inline code examples):

• https://arvoelke.github.io/nengolib-docs/master/nengolib.stats.Rd.html#nengolib.stats.Rd
• https://arvoelke.github.io/nengolib-docs/master/nengolib.stats.ScatteredCube.html#nengolib.stats.ScatteredCube
• https://arvoelke.github.io/nengolib-docs/master/nengolib.stats.ScatteredHypersphere.html