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 numpy as np
import matplotlib.pyplot as plt

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_test_random.png

Problem class API

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

Methods

__call__(nt)

Compute the requested number of sampling points.
__init__(**kwargs)[source]

Constructor where values of options can be passed in.

For the list of options, see the documentation for the problem 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) → ndarray[source]

Compute the requested number of sampling points.

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

Returns:
ndarray[nt, nx]

The sampling locations in the input space.