def pred_sensitivity(krigobj, init_samp, nvar, second=False):
lb = (np.min(init_samp, axis=0))
ub = (np.max(init_samp, axis=0))
lb = np.hstack((lb, lb))
ub = np.hstack((ub, ub))
testSA = SobolI(nvar, krigobj, None, ub, lb, nMC=2e5)
result = testSA.analyze(True, True, second)
print("PREDICTED SA")
for key in result.keys():
print(key + ':')
if type(result[key]) is not dict:
print(result[key])
else:
for subkey in result[key].keys():
print(subkey + ':', result[key][subkey])
return result
def real_sensi(krigobj, init_samp, nvar, second=False, prob='hidimenra'):
lb = (np.min(init_samp, axis=0))
ub = (np.max(init_samp, axis=0))
lb = np.hstack((lb, lb))
ub = np.hstack((ub, ub))
testSA = SobolI(nvar, krigobj, prob, ub, lb, nMC=2e5)
result = testSA.analyze(True, True, second)
print("REAL SA")
for key in result.keys():
print(key + ':')
if type(result[key]) is not dict:
print(result[key])
else:
for subkey in result[key].keys():
print(subkey + ':', result[key][subkey])
return result