using Plots
using Distributed
using LinearAlgebra
addprocs(2)
@everywhere using DPMMSubClusters
@everywhere using Random
function plot_dp_2d(pts, labels)
plt=Plots.plot()
Plots.plot!(pts[1,:],pts[2,:], seriestype=:scatter, color = Int64.(labels), markersize = 3, markerstrokewidth = 0.5)
return plt
end
10^5 Points, 2D, Generated from a 6 components Gaussian mixture model with components mean sampled from a normal distribution with isotropic variance scaled by 80.
Random.seed!(12345)
x,labels,clusters = generate_gaussian_data(10^4,2,6,80.0)
plot_dp_2d(x, labels)