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sampling.py
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Tue, May 28, 02:21
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text/x-python
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Thu, May 30, 02:21 (2 d)
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R6746 RationalROMPy
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
LinearAffineEngine
,
NumpyEngineBase
from
rrompy.sampling
import
SamplingEngineStandard
,
SamplingEngineStandardPOD
from
rrompy.parameter
import
parameterList
class
matrixEngine
(
LinearAffineEngine
,
NumpyEngineBase
):
def
__init__
(
self
):
super
()
.
__init__
(
verbosity
=
0
)
self
.
_affinePoly
=
True
N
=
100
self
.
npar
=
1
self
.
nAs
,
self
.
nbs
=
2
,
1
self
.
setAs
([
sp
.
spdiags
([
np
.
arange
(
1
,
1
+
N
)],
[
0
],
N
,
N
),
-
sp
.
eye
(
N
)])
self
.
setbs
([
np
.
exp
(
1.j
*
np
.
linspace
(
0
,
-
np
.
pi
,
N
))])
self
.
setthAs
(
self
.
getMonomialWeights
(
self
.
nAs
))
self
.
setthbs
(
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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