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.

• Random sampling
• Latin Hypercube sampling
• Full-factorial sampling
• pyDOE sampling methods

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)

Sampling method class API

class smt.sampling_methods.sampling_method.SamplingMethod(**kwargs)

Methods

__call__([nt])

Compute the samples.

__init__(**kwargs)

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

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.