.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_example/plot_example_KPLSK.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_example_plot_example_KPLSK.py: Use of KPLSK ------------ .. GENERATED FROM PYTHON SOURCE LINES 7-16 .. code-block:: default from smt.sampling_methods import LHS from smt.problems import Sphere from smt.surrogate_models import KPLSK import numpy as np import otsmt import openturns as ot .. GENERATED FROM PYTHON SOURCE LINES 17-18 | Definition of Initial data .. GENERATED FROM PYTHON SOURCE LINES 18-32 .. code-block:: default # Construction of the DOE fun = Sphere(ndim=2) sampling = LHS(xlimits=fun.xlimits, criterion="m") xt = sampling(40) yt = fun(xt) # Compute the gradient for i in range(2): yd = fun(xt, kx=i) yt = np.concatenate((yt, yd), axis=1) xv = ot.Sample([[0.1,1.],[1.,2.]]) .. GENERATED FROM PYTHON SOURCE LINES 33-34 | Training of smt model for KPLSK .. GENERATED FROM PYTHON SOURCE LINES 34-40 .. code-block:: default sm_kplsk = KPLSK(theta0=[1e-2]) sm_kplsk.set_training_values(xt, yt[:,0]) sm_kplsk.train() .. rst-class:: sphx-glr-script-out Out: .. code-block:: none ___________________________________________________________________________ KPLSK ___________________________________________________________________________ Problem size # training points. : 40 ___________________________________________________________________________ Training Training ... Training - done. Time (sec): 0.0953236 .. GENERATED FROM PYTHON SOURCE LINES 41-42 | Creation of OpenTurns PythonFunction for prediction .. GENERATED FROM PYTHON SOURCE LINES 42-51 .. code-block:: default otkplsk = otsmt.smt2ot(sm_kplsk) otkplskprediction = otkplsk.getPredictionFunction() otkplskvariances = otkplsk.getConditionalVarianceFunction() otkplskgradient = otkplsk.getPredictionDerivativesFunction() print('Predicted values by KPLSK:',otkplskprediction(xv)) print('Predicted variances values by KPLSK:',otkplskvariances(xv)) print('Prediction derivatives derivatives by KPLSK:',otkplskgradient(xv)) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none ___________________________________________________________________________ Evaluation # eval points. : 2 Predicting ... Predicting - done. Time (sec): 0.0002224 Prediction time/pt. (sec) : 0.0001112 Predicted values by KPLSK: [ y0 ] 0 : [ 1.01022 ] 1 : [ 5.00008 ] Predicted variances values by KPLSK: [ y0 ] 0 : [ 5.85532e-08 ] 1 : [ 5.94511e-08 ] ___________________________________________________________________________ Evaluation # eval points. : 2 Predicting ... Predicting - done. Time (sec): 0.0001738 Prediction time/pt. (sec) : 0.0000869 ___________________________________________________________________________ Evaluation # eval points. : 2 Predicting ... Predicting - done. Time (sec): 0.0001333 Prediction time/pt. (sec) : 0.0000666 Prediction derivatives derivatives by KPLSK: [ y0 y1 ] 0 : [ 0.19995 1.99993 ] 1 : [ 1.99992 3.9999 ] .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.101 seconds) .. _sphx_glr_download_auto_example_plot_example_KPLSK.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_example_KPLSK.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_example_KPLSK.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_