Source code for smt.sampling_methods.sampling_method
"""
Author: Dr. John T. Hwang <hwangjt@umich.edu>
This package is distributed under New BSD license.
Base class for sampling algorithms.
"""
from abc import ABCMeta, abstractmethod
import numpy as np
from smt.utils.options_dictionary import OptionsDictionary
[docs]
class SamplingMethod(metaclass=ABCMeta):
[docs]
def __init__(self, **kwargs):
"""
Constructor where values of options can be passed in.
For the list of options, see the documentation for the problem being used.
Parameters
----------
**kwargs : named arguments
Set of options that can be optionally set; each option must have been declared.
Examples
--------
>>> import numpy as np
>>> from smt.sampling_methods import Random
>>> sampling = Random(xlimits=np.arange(2).reshape((1, 2)))
"""
self.options = OptionsDictionary()
self.options.declare(
"xlimits",
types=np.ndarray,
desc="The interval of the domain in each dimension with shape nx x 2 (required)",
)
self._initialize(**kwargs)
self.options.update(kwargs)
[docs]
def _initialize(self, **kwargs) -> None:
"""
Implemented by sampling methods to declare options
and/or use these optional values for initialization (optional)
Parameters
----------
**kwargs : named arguments passed by the user
Set of options that can be optionally set
Examples
--------
self.options.declare('option_name', default_value, types=(bool, int), desc='description')
"""
pass
[docs]
def __call__(self, nt: int) -> np.ndarray:
"""
Compute the requested number of sampling points.
The number of dimensions (nx) is determined based on `xlimits.shape[0]`.
Arguments
---------
nt : int
Number of points requested.
Returns
-------
ndarray[nt, nx]
The sampling locations in the input space.
"""
return self._compute(nt)
[docs]
@abstractmethod
def _compute(self, nt: int) -> np.ndarray:
"""
Implemented by sampling methods to compute the requested number of sampling points.
The number of dimensions (nx) is determined based on `xlimits.shape[0]`.
Arguments
---------
nt : int
Number of points requested.
Returns
-------
ndarray[nt, nx]
The sampling locations in the input space.
"""
raise Exception("This sampling method has not been implemented correctly")
[docs]
class ScaledSamplingMethod(SamplingMethod):
"""This class describes an sample method which generates samples in the unit hypercube.
The __call__ method does scale the generated samples accordingly to the defined xlimits.
"""
def __call__(self, nt: int) -> np.ndarray:
"""
Compute the requested number of sampling points.
The number of dimensions (nx) is determined based on `xlimits.shape[0]`.
Arguments
---------
nt : int
Number of points requested.
Returns
-------
ndarray[nt, nx]
The sampling locations in the input space.
"""
return _scale_to_xlimits(self._compute(nt), self.options["xlimits"])
[docs]
@abstractmethod
def _compute(self, nt: int) -> np.ndarray:
"""
Implemented by sampling methods to compute the requested number of sampling points.
The number of dimensions (nx) is determined based on `xlimits.shape[0]`.
Arguments
---------
nt : int
Number of points requested.
Returns
-------
ndarray[nt, nx]
The sampling locations in the unit hypercube.
"""
raise Exception("This sampling method has not been implemented correctly")
def _scale_to_xlimits(samples: np.ndarray, xlimits: np.ndarray) -> np.ndarray:
"""Scales the samples from the unit hypercube to the specified limits.
Parameters
----------
samples : np.ndarray
The samples with coordinates in [0,1]
xlimits : np.ndarray
The xlimits
Returns
-------
np.ndarray
The scaled samples.
"""
nx = xlimits.shape[0]
for kx in range(nx):
samples[:, kx] = xlimits[kx, 0] + samples[:, kx] * (
xlimits[kx, 1] - xlimits[kx, 0]
)
return samples
