30 lines
1.0 KiB
Python
30 lines
1.0 KiB
Python
import numpy as np
|
|
from .chebyshev import chebyshev_roots
|
|
|
|
class Cube():
|
|
def __init__(self, bound):
|
|
self.bound = np.asarray(bound)
|
|
|
|
def discretize(self, n, mode='random'):
|
|
|
|
if mode == 'random':
|
|
pts = np.random.uniform(size=(n, self.bound.shape[0]))
|
|
elif mode == 'chebyshev':
|
|
pts = np.array([chebyshev_roots(n) *.5 + .5 for _ in range(self.bound.shape[0])])
|
|
grids = np.meshgrid(*pts)
|
|
pts = np.hstack([grid.reshape(-1, 1) for grid in grids])
|
|
elif mode == 'grid':
|
|
pts = np.array([np.linspace(0, 1, n) for _ in range(self.bound.shape[0])])
|
|
grids = np.meshgrid(*pts)
|
|
pts = np.hstack([grid.reshape(-1, 1) for grid in grids])
|
|
elif mode == 'lh' or mode == 'latin':
|
|
from scipy.stats import qmc
|
|
sampler = qmc.LatinHypercube(d=self.bound.shape[0])
|
|
pts = sampler.random(n)
|
|
|
|
# Scale pts
|
|
pts *= self.bound[:, 1] - self.bound[:, 0]
|
|
pts += self.bound[:, 0]
|
|
|
|
return pts
|