[docs]def gfun_110(x):
"""Performance function for reliability problem 25.
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 = 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 = np.zeros((x.shape[0], 1))
g2 = np.zeros((x.shape[0], 1))
for i in range(x.shape[0]):
if x[i, 0] <= 3.5:
g1[i] = 0.85 - 0.1 * x[i, 0]
else:
g1[i] = 4 - x[i, 0]
if x[i, 1] <= 2:
g2[i] = 2.3 - x[i, 1]
else:
g2[i] = 0.5 - 0.1 * x[i, 1]
g = np.amin(np.stack((g1, g2)), 0)
g_val_sys = g
g_val_comp = np.stack((g1, g2))
return g_val_sys, g_val_comp, msg