Adaptive

Here I show how one can use Adaptive.jl wrapper for python adaptive package to make adaptively sampled figures. Before we start, let's set up our environment by loading packages:

using Distributed
addprocs(2)
using TaskMaster
using Adaptive
using PyPlot

where Adaptive.jl at the moment needs to be added directly from the GitHub repository.

AdaptiveLearner1D

@everywhere f(x) = exp(-x^2)

fig = figure()

x = collect(range(-2,stop=2,length=200))
plot(x,f.(x),label=L"e^{-x^2}")

xx = collect(range(-2,stop=2,length=20))
plot(xx,f.(xx),".-",label="even sampling")

master = WorkMaster(f)
learner1d = AdaptiveLearner1D((-2,+2))
loop = Loop(master,learner1d)
evaluate!(loop,1:20)

plot(learner1d.x,learner1d.y,".-",label="AdaptiveLearner1D")

legend()
savefig("learner1d.svg")

AdaptiveLearner2D

@everywhere f(p) = exp(-p[1]^2 - p[2]^2)

master = WorkMaster(f)
learner2d = AdaptiveLearner2D([(-3,+3),(-3,+3)])
loop = Loop(master,learner2d)
evaluate!(loop,learner->learner.loss()<0.05)

fig = figure()

p,tri,v = learner2d.points, learner2d.vertices, learner2d.values

tricontourf(p[:,1],p[:,2],tri.-1,v)
triplot(p[:,1],p[:,2],tri.-1,"k.-")

savefig("learner2d.svg")