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RROMPy/rrompy/utilities/parameter_sampling/random_sampler.py
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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 rrompy.utilities.base.sobol import sobolGenerate
from rrompy.utilities.parameter_sampling.generic_sampler import GenericSampler
from rrompy.utilities.base.types import Np1D, Tuple, List
from rrompy.utilities.exception_manager import RROMPyException
__all__ = ['RandomSampler']
class RandomSampler(GenericSampler):
"""Generator of quadrature sample points."""
allowedKinds = ["UNIFORM", "SOBOL"]
def __init__(self, lims:Np1D, kind : str = "UNIFORM",
scaling : callable = None, scalingInv : 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[Np1D, Np1D]:
"""Array of quadrature points and array of weights."""
a, b = self.lims[0], self.lims[1]
if self.scaling is not None:
a, b = self.scaling(a), self.scaling(b)
if self.kind == "UNIFORM":
np.random.seed(seed)
x = np.random.uniform(size = n)
else:
x = sobolGenerate(1, n, seed).flatten()
x = a + (b - a) * x
w = np.abs(a - b) / n * np.ones(n)
if self.scalingInv is not None:
x = self.scalingInv(x)
return x, w
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