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random_box_sampler.py
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Wed, May 1, 14:05

random_box_sampler.py
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# Copyright (C) 2018 by the RROMPy authors
#
# This file is part of RROMPy.
#
# RROMPy is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# RROMPy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
#
from numbers import Number
import numpy as np
from .generic_shape_random_sampler import GenericShapeRandomSampler
from rrompy.utilities.numerical import haltonGenerate, sobolGenerate
from rrompy.utilities.base.types import Tuple, List
__all__ = ['RandomBoxSampler']
class RandomBoxSampler(GenericShapeRandomSampler):
"""Generator of (quasi-)random sample points on boxes."""
def refine(self, active : List[int] = None) -> Tuple[List[int], List[int]]:
if active is None:
n = self.npoints
elif isinstance(active, (Number,)):
n = int(active)
else:
n = len(active)
n = int(n * self.refinementFactor)
nEff = int(np.ceil(n * np.prod(
[max(x, 1. / x) for x in self.axisRatios])))
xmat2 = []
while len(xmat2) < n:
if self.kind == "UNIFORM":
xmat2 = np.random.uniform(size = (nEff, 2 * self.npar))
elif self.kind == "HALTON":
xmat2, self.seedLoc = haltonGenerate(2 * self.npar, nEff,
self.seedLoc,
return_seed = True)
else:
xmat2, self.seedLoc = sobolGenerate(2 * self.npar, nEff,
self.seed,
return_seed = True)
for d in range(self.npar):
ax = self.axisRatios[d]
if ax <= 1.:
xmat2 = xmat2[xmat2[:, 2 * d + 1] <= ax]
else:
xmat2 = xmat2[xmat2[:, 2 * d] <= 1. / ax]
xmat2[:, 2 * d : 2 * d + 2] *= ax
nEff += 1
xmat = np.empty((n, self.npar), dtype = np.complex)
limsE = self.mapParameterList(self.lims)
for d in range(self.npar):
a, b = limsE(d)
xmat[:, d] = a + (b - a) * (xmat2[: n, 2 * d]
+ 1.j * self.axisRatios[d] * (xmat2[: n, 2 * d + 1] - .5))
pts = self.mapParameterList(xmat, "B")
idx = np.arange(n, dtype = int) + len(self.points)
for pj in pts: self.points.append(pj)
return list(idx), []

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