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random_sampler.py
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Thu, May 16, 15:37
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text/x-python
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Sat, May 18, 15:37 (2 d)
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R6746 RationalROMPy
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.numerical
import
haltonGenerate
,
sobolGenerate
from
rrompy.utilities.base.types
import
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"
,
"HALTON"
,
"SOBOL"
]
def
__init__
(
self
,
lims
:
paramList
,
kind
:
str
=
"UNIFORM"
,
scalingExp
:
List
[
float
]
=
None
,
seed
:
int
=
42
):
super
()
.
__init__
(
lims
=
lims
,
scalingExp
=
scalingExp
)
self
.
kind
=
kind
self
.
seed
=
seed
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
)
->
paramList
:
"""Array of quadrature points."""
if
hasattr
(
n
,
"__len__"
):
n
=
n
[
0
]
if
self
.
kind
==
"UNIFORM"
:
np
.
random
.
seed
(
self
.
seed
)
xmat
=
np
.
random
.
uniform
(
size
=
(
n
,
self
.
npar
))
elif
self
.
kind
==
"HALTON"
:
xmat
=
haltonGenerate
(
self
.
npar
,
n
,
self
.
seed
)
else
:
xmat
=
sobolGenerate
(
self
.
npar
,
n
,
self
.
seed
)
for
d
in
range
(
self
.
npar
):
a
=
self
.
lims
(
0
,
d
)
**
self
.
scalingExp
[
d
]
b
=
self
.
lims
(
1
,
d
)
**
self
.
scalingExp
[
d
]
xmat
[:,
d
]
=
a
+
(
b
-
a
)
*
xmat
[:,
d
]
xmat
[:,
d
]
**=
1.
/
self
.
scalingExp
[
d
]
x
=
checkParameterList
(
xmat
,
self
.
npar
)[
0
]
return
x
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