Join GitHub today
GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.Sign up
Quasi Monte Carlo Rd Sampling #153
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).
Some plots from the new method (to be in the documentation upon release):
Versus the old method (Sobol):
@@ Coverage Diff @@ ## master #153 +/- ## ===================================== Coverage 100% 100% ===================================== Files 29 29 Lines 1374 1398 +24 Branches 157 162 +5 ===================================== + Hits 1374 1398 +24
@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):