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Quasi Monte Carlo Rd Sampling #153

merged 6 commits into from Sep 5, 2018


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arvoelke commented Aug 30, 2018

Resolves #152. Code is modified from

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):




@arvoelke arvoelke force-pushed the quasi_rd branch from 5396850 to 527c2a4 Aug 30, 2018
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codecov-io commented Aug 30, 2018

Codecov Report

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

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@@          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/ 100% <100%> (ø) ⬆️

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@arvoelke arvoelke force-pushed the quasi_rd branch from 527c2a4 to 319783d Aug 30, 2018
@arvoelke arvoelke force-pushed the quasi_rd branch from 319783d to c144d88 Aug 30, 2018
@arvoelke arvoelke merged commit 293492c into master Sep 5, 2018
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@arvoelke arvoelke deleted the quasi_rd branch Sep 5, 2018

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arvoelke commented Sep 5, 2018

@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):

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