Note
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Use of Second-order polynomial approximationΒΆ
from smt.sampling_methods import LHS
from smt.problems import Sphere
from smt.surrogate_models import QP
import numpy as np
import otsmt
import openturns as ot
Definition of Initial data
Training of smt model for Second-order polynomial approximation
sm_qp = QP()
sm_qp.set_training_values(xt, yt[:,0])
sm_qp.train()
Out:
___________________________________________________________________________
QP
___________________________________________________________________________
Problem size
# training points. : 40
___________________________________________________________________________
Training
Training ...
Training - done. Time (sec): 0.0003443
Creation of OpenTurns PythonFunction for prediction
otqp = otsmt.smt2ot(sm_qp)
otqpprediction = otqp.getPredictionFunction()
print('Predicted values by QP:',otqpprediction(xv))
Out:
___________________________________________________________________________
Evaluation
# eval points. : 2
Predicting ...
Predicting - done. Time (sec): 0.0000496
Prediction time/pt. (sec) : 0.0000248
Predicted values by QP: [ y0 ]
0 : [ 1.01 ]
1 : [ 5 ]
Total running time of the script: ( 0 minutes 0.323 seconds)