[docs]def gfun_55(x):
"""Performance function for reliability problem 55.
Parameters
----------
x : numpy.array of float(s)
Values of independent variables: columns are the different parameters/random variables (x1, x2,...xn) and rows are different parameter/random variables sets for different calls.
Returns
-------
g_val_sys : numpy.array of float(s)
Performance function value for the system.
g_val_comp : numpy.array of float(s)
Performance function value for each component.
msg : str
Accompanying diagnostic message, e.g. warning.
"""
import numpy as np
# expected number of random variables/columns
nrv_e = 2
g, g1, g2, g3, g4 = float('nan'), float('nan'), float('nan'), float('nan'), float('nan')
msg = 'Ok'
x = np.array(x, dtype='f')
n_dim = len(x.shape)
if n_dim == 1:
x = np.array(x)[np.newaxis]
elif n_dim > 2:
msg = 'Only available for 1D and 2D arrays.'
return float('nan'), float('nan'), msg
nrv_p = x.shape[1]
if nrv_p != nrv_e:
msg = f'The number of random variables (x, columns) is expected to be {nrv_e} but {nrv_p} is provided!'
else:
g1 = 0.2 + 0.6 * (x[:, 0] - x[:, 1]) ** 4 - (x[:, 0] - x[:, 1]) / np.sqrt(2)
g2 = 0.2 + 0.6 * (x[:, 0] - x[:, 1]) ** 4 + (x[:, 0] - x[:, 1]) / np.sqrt(2)
g3 = (x[:, 0] - x[:, 1]) + 5 / np.sqrt(2) - 2.2
g4 = (x[:, 1] - x[:, 0]) + 5 / np.sqrt(2) - 2.2
g = np.amin(np.stack((g1, g2, g3, g4)), 0)
g_val_sys = g
g_val_comp = np.stack((g1, g2, g3, g4))
return g_val_sys, g_val_comp, msg