Boundary check¶

Check probability at boundaries.

In this case we define the probability density function (PDF) directly in an n-dimensional uniform box.

Ideally, the correlation plots and variable distributions will be uniform.

from bumps.names import *

Adjust scale from 1e-150 to 1e+150 and you will see that DREAM is equally adept at filling the box.

scale = 1

Uniform cost function.

def box(x):
    return 0 if np.all(np.abs(x)<=scale) else np.inf

def diamond(x):
    return 0 if np.sum(np.abs(x))<=scale else np.inf

Wrap it in a PDF object which turns an arbitrary probability density into a fitting function. Give it a valid initial value, and set the bounds to a unit cube with one corner at the origin.

M = PDF(lambda a,b: box([a,b]))
#M = PDF(lambda a,b: diamond([a,b]))
M.a.range(-2*scale,2*scale)
M.b.range(-2*scale,2*scale)

Make the PDF a fit problem that bumps can process.

problem = FitProblem(M)

Download: bounded.py.