Sampling methods

SMT contains a library of sampling methods used to generate sets of points in the input space, either for training or for prediction. These are listed below.

Usage

import matplotlib.pyplot as plt
import numpy as np

from smt.sampling_methods import Random

xlimits = np.array([[0.0, 4.0], [0.0, 3.0]])
sampling = Random(xlimits=xlimits)

num = 50
x = sampling(num)

print(x.shape)

plt.plot(x[:, 0], x[:, 1], "o")
plt.xlabel("x")
plt.ylabel("y")
plt.show()
(50, 2)
../_images/sampling_methods_Test_run_random.png

Sampling method class API

class smt.sampling_methods.sampling_method.SamplingMethod(**kwargs)[source]

Methods

__call__([nt])

Compute the samples.

__init__(**kwargs)[source]

Constructor where values of options can be passed in. xlimits keyword argument is required.

For the list of options, see the documentation for the sampling method being used.

Parameters:
**kwargsnamed 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)))
__call__(nt: int = None) ndarray[source]

Compute the samples. Depending on the concrete sampling method the requested number of samples nt may not be enforced.

The number of dimensions (nx) is determined based on xlimits.shape[0].

Parameters:
ntint

Number of points hint.

Returns:
ndarray[nt, nx]

The sampling locations in the input space.