McLain S5 Function#

import numpy as np
import matplotlib.pyplot as plt
import uqtestfuns as uqtf

The McLain S5 function is a two-dimensional scalar-valued function. The function was introduced in [McL74] as a test function for procedures to construct contours from a given set of points.

Note

The McLain’s test functions are a set of five two-dimensional functions that mathematically defines surfaces. The functions are:

  • S1: A part of a sphere

  • S2: A steep hill rising from a plain

  • S3: A less steep hill

  • S4: A long narrow hill

  • S5: A plateau and plain separated by a steep cliff (this function)

../_images/mclain-s5_3_0.png

As shown in the plots above, the function features two nearly flat regions of height \(0.0\) and (approximately) \(1.0\). The two regions are joined by a sharp rise that runs diagonally from \((0.0, 10.0)\) to \((10.0, 0.0)\).

Note

The McLain S5 function appeared in a modified form in the report of Franke [Fra79] (specifically the (2nd) Franke function).

In fact, four of the Franke’s test functions (2, 4, 5, and 6) are slight modifications of the McLain’s, including the translation of the input domain from \([1.0, 10.0]^2\) to \([0.0, 1.0]^2\).

Test function instance#

To create a default instance of the McLain S5 function:

my_testfun = uqtf.McLainS5()

Check if it has been correctly instantiated:

print(my_testfun)
Name              : McLainS5
Spatial dimension : 2
Description       : McLain S5 function from McLain (1974)

Description#

The McLain S5 function is defined as follows:

\[ \mathcal{M}(\boldsymbol{x}) = \tanh{\left( x_1 + x_2 - 11 \right)} \]

where \(\boldsymbol{x} = \{ x_1, x_2 \}\) is the two-dimensional vector of input variables further defined below.

Probabilistic input#

Based on [McL74], the probabilistic input model for the function consists of two independent random variables as shown below.

my_testfun.prob_input

Name: McLain-1974

Spatial Dimension: 2

Description: Input specification for the McLain's test functions from McLain (1974).

Marginals:

No. Name Distribution Parameters Description
1 X1 uniform [ 1. 10.] None
2 X2 uniform [ 1. 10.] None

Copulas: None

Reference results#

This section provides several reference results of typical UQ analyses involving the test function.

Sample histogram#

Shown below is the histogram of the output based on \(100'000\) random points:

xx_test = my_testfun.prob_input.get_sample(100000)
yy_test = my_testfun(xx_test)

plt.hist(yy_test, bins="auto", color="#8da0cb");
plt.grid();
plt.ylabel("Counts [-]");
plt.xlabel("$\mathcal{M}(\mathbf{X})$");
plt.gcf().set_dpi(150);
../_images/mclain-s5_11_0.png

References#

Fra79

Richard Franke. A critical comparison of some methods for interpolation of scattered data. techreport NPS53-79-003, Naval Postgraduate School, Monterey, Canada, 1979. URL: https://core.ac.uk/reader/36727660.

McL74(1,2)

D. H. McLain. Drawing contours from arbitrary data points. The Computer Journal, 17(4):318–324, 1974. doi:10.1093/comjnl/17.4.318.