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random_sampler.py
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Sun, May 12, 09:09

random_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/>.
#
import numpy as np
from .generic_sampler import GenericSampler
from rrompy.utilities.base.sobol import sobolGenerate
from rrompy.utilities.base.types import Np1D, Tuple, List, paramList
from rrompy.utilities.exception_manager import RROMPyException
from rrompy.parameter import checkParameterList
__all__ = ['RandomSampler']
class RandomSampler(GenericSampler):
"""Generator of quadrature sample points."""
allowedKinds = ["UNIFORM", "SOBOL"]
def __init__(self, lims:paramList, kind : str = "UNIFORM",
scaling : List[callable] = None,
scalingInv : List[callable] = None):
super().__init__(lims = lims, scaling = scaling,
scalingInv = scalingInv)
self.kind = kind
def __str__(self) -> str:
return "{}_{}".format(super().__str__(), self.kind)
def __repr__(self) -> str:
return self.__str__() + " at " + hex(id(self))
@property
def kind(self):
"""Value of kind."""
return self._kind
@kind.setter
def kind(self, kind):
if kind.upper() not in self.allowedKinds:
raise RROMPyException("Generator kind not recognized.")
self._kind = kind.upper()
def generatePoints(self, n:int,
seed : int = 420) -> Tuple[paramList, Np1D]:
"""Array of quadrature points and array of weights."""
wdMult = 1. / n
if self.kind == "UNIFORM":
np.random.seed(seed)
xmat = np.random.uniform(size = (n, self.npar))
else:
xmat = sobolGenerate(self.npar, n, seed)
for d in range(self.npar):
a, b = self.lims(0, d), self.lims(1, d)
if self.scaling is not None:
a, b = self.scaling[d](a), self.scaling[d](b)
wdMult *= np.abs(a - b)
xmat[:, d] = a + (b - a) * xmat[:, d]
if self.scalingInv is not None:
xmat[:, d] = self.scalingInv[d](xmat[:, d])
x, _ = checkParameterList(xmat, self.npar)
return x, wdMult * np.ones(len(x))

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