get_oakley_function_data

pyapprox.benchmarks.sensitivity_benchmarks.get_oakley_function_data()

Get the data \(a_1,a_2,a_3\) and \(M\) of the Oakley function

\[f(z) = a_1^Tz + a_2^T\sin(z) + a_3^T\cos(z) + z^TMz\]

Returns

a1np.ndarray (15)

The vector \(a_1\) of the Oakley function

a2np.ndarray (15)

The vector \(a_2\) of the Oakley function

a3np.ndarray (15)

The vector \(a_3\) of the Oakley function

Mnp.ndarray (15,15)

The non-symmetric matrix \(M\) of the Oakley function

Examples

>>> from pyapprox.benchmarks.sensitivity_benchmarks import get_oakley_function_data
>>> a1,a2,a3,M=get_oakley_function_data()
>>> print(a1)
[0.0118 0.0456 0.2297 0.0393 0.1177 0.3865 0.3897 0.6061 0.6159 0.4005
 1.0741 1.1474 0.788  1.1242 1.1982]
>>> print(a2)
[0.4341 0.0887 0.0512 0.3233 0.1489 1.036  0.9892 0.9672 0.8977 0.8083
 1.8426 2.4712 2.3946 2.0045 2.2621]
>>> print(a3)
[0.1044 0.2057 0.0774 0.273  0.1253 0.7526 0.857  1.0331 0.8388 0.797
 2.2145 2.0382 2.4004 2.0541 1.9845]
>>> print(M)
[[-0.02248289 -0.18501666  0.13418263  0.36867264  0.17172785  0.13651143
  -0.44034404 -0.08142285  0.71321025 -0.44361072  0.50383394 -0.02410146
  -0.04593968  0.21666181  0.05588742]
 [ 0.2565963   0.05379229  0.25800381  0.23795905 -0.59125756 -0.08162708
  -0.28749073  0.41581639  0.49752241  0.08389317 -0.11056683  0.03322235
  -0.13979497 -0.03102056 -0.22318721]
 [-0.05599981  0.19542252  0.09552901 -0.2862653  -0.14441303  0.22369356
   0.14527412  0.28998481  0.2310501  -0.31929879 -0.29039128 -0.20956898
   0.43139047  0.02442915  0.04490441]
 [ 0.66448103  0.43069872  0.29924645 -0.16202441 -0.31479544 -0.39026802
   0.17679822  0.05795266  0.17230342  0.13466011 -0.3527524   0.25146896
  -0.01881053  0.36482392 -0.32504618]
 [-0.121278    0.12463327  0.10656519  0.0465623  -0.21678617  0.19492172
  -0.06552113  0.02440467 -0.09682886  0.19366196  0.33354757  0.31295994
  -0.08361546 -0.25342082  0.37325717]
 [-0.2837623  -0.32820154 -0.10496068 -0.22073452 -0.13708154 -0.14426375
  -0.11503319  0.22424151 -0.03039502 -0.51505615  0.01725498  0.03895712
   0.36069184  0.30902452  0.05003019]
 [-0.07787589  0.00374566  0.88685604 -0.26590028 -0.07932536 -0.04273492
  -0.18653782 -0.35604718 -0.17497421  0.08869996  0.40025886 -0.05597969
   0.13724479  0.21485613 -0.0112658 ]
 [-0.09229473  0.59209563  0.03133829 -0.03308086 -0.24308858 -0.09979855
   0.03446019  0.09511981 -0.3380162   0.006386   -0.61207299  0.08132542
   0.88683114  0.14254905  0.14776204]
 [-0.13189434  0.52878496  0.12652391  0.04511362  0.58373514  0.37291503
   0.11395325 -0.29479222 -0.57014085  0.46291592 -0.09405018  0.13959097
  -0.38607402 -0.4489706  -0.14602419]
 [ 0.05810766 -0.32289338  0.09313916  0.07242723 -0.56919401  0.52554237
   0.23656926 -0.01178202  0.0718206   0.07827729 -0.13355752  0.22722721
   0.14369455 -0.45198935 -0.55574794]
 [ 0.66145875  0.34633299  0.14098019  0.51882591 -0.28019898 -0.1603226
  -0.06841334 -0.20428242  0.06967217  0.23112577 -0.04436858 -0.16455425
   0.21620977  0.00427021 -0.08739901]
 [ 0.31599556 -0.02755186  0.13434254  0.13497371  0.05400568 -0.17374789
   0.17525393  0.06025893 -0.17914162 -0.31056619 -0.25358691  0.02584754
  -0.43006001 -0.62266361 -0.03399688]
 [-0.29038151  0.03410127  0.03490341 -0.12121764  0.02603071 -0.33546274
  -0.41424111  0.05324838 -0.27099455 -0.0262513   0.41024137  0.26636349
   0.15582891 -0.18666254  0.01989583]
 [-0.24388652 -0.44098852  0.01261883  0.24945112  0.07110189  0.24623792
   0.17484502  0.00852868  0.2514707  -0.14659862 -0.08462515  0.36931333
  -0.29955293  0.1104436  -0.75690139]
 [ 0.04149432 -0.25980564  0.46402128 -0.36112127 -0.94980789 -0.16504063
   0.00309433  0.05279294  0.22523648  0.38390366  0.45562427 -0.18631744
   0.0082334   0.16670803  0.16045688]]