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sampling.py
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Sun, May 5, 23:25

sampling.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
import scipy.sparse as sp
from rrompy.hfengines.base import MatrixEngineBase
from rrompy.sampling.standard import (SamplingEngineStandard,
SamplingEngineStandardPOD)
from rrompy.parameter import parameterList
class matrixEngine(MatrixEngineBase):
def __init__(self):
super().__init__(verbosity = 0)
N = 100
self.npar = 1
self.nAs, self.nbs = 2, 1
self.As = [sp.spdiags([np.arange(1, 1 + N)], [0], N, N),
- sp.eye(N)]
self.bs = [np.exp(1.j * np.linspace(0, -np.pi, N))]
self.thAs = self.getMonomialWeights(self.nAs)
self.thbs = self.getMonomialWeights(self.nbs)
def test_krylov():
mu = 10. + .5j
solver = matrixEngine()
samplingEngine = SamplingEngineStandard(solver, verbosity = 0)
samples = samplingEngine.iterSample([mu] * 5).data
assert samples.shape == (100, 5)
assert np.isclose(np.linalg.norm(samples), 37.02294804524299, rtol = 1e-5)
def test_distributed():
mus = parameterList(np.linspace(5, 15, 11) + .5j)
solver = matrixEngine()
samplingEngine = SamplingEngineStandard(solver, verbosity = 0)
samples = samplingEngine.iterSample(mus).data
assert samples.shape == (100, 11)
assert np.isclose(np.linalg.norm(samples), 8.59778606421386, rtol = 1e-5)
def test_distributed_pod():
mus = np.linspace(5, 15, 11) + .5j
solver = matrixEngine()
samplingEngine = SamplingEngineStandardPOD(solver, verbosity = 0)
samples = samplingEngine.iterSample(mus).data
assert samples.shape == (100, 11)
assert np.isclose(np.linalg.norm(samples), 3.3166247903553994, rtol = 1e-5)
assert np.isclose(np.linalg.cond(samples.conj().T.dot(samples)), 1.,
rtol = 1e-5)

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