1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
|
// -*- C++ -*-
/**
* @brief The test file of KrigingRandomVector class
*
* Copyright 2005-2025 Airbus-EDF-IMACS-ONERA-Phimeca
*
* This library is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This library is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this library. If not, see <http://www.gnu.org/licenses/>.
*
*/
#include "openturns/OT.hxx"
#include "openturns/OTtestcode.hxx"
using namespace OT;
using namespace OT::Test;
int main(int, char *[])
{
TESTPREAMBLE;
OStream fullprint(std::cout);
try
{
PlatformInfo::SetNumericalPrecision(2);
// Learning data
Point levels(2);
levels[0] = 8;
levels[1] = 5;
// Define the Box
Box box(levels);
// Get the input sample
Sample inputSample( box.generate() );
// Scale each direction
inputSample *= 10;
// Define model
Description inputDescription(2);
inputDescription[0] = "x";
inputDescription[1] = "y";
Description formula(1);
formula[0] = "cos(0.5*x) + sin(y)" ;
const SymbolicFunction model(inputDescription, formula);
// Build the output sample
const Sample outputSample( model(inputSample) );
// 2) Definition of exponential model
Point scale(2);
scale[0] = 5.33532;
scale[1] = 2.61534;
Point amplitude(1, 1.61536);
SquaredExponential covarianceModel(scale, amplitude);
// 3) Basis definition
Basis basis(ConstantBasisFactory(2).build());
// Kriring algorithm
KrigingAlgorithm algo(inputSample, outputSample, covarianceModel, basis);
algo.setOptimizeParameters(false);
algo.run();
// Get result
KrigingResult result(algo.getResult());
// Get meta model
Function metaModel(result.getMetaModel());
// Interpolation error
assert_almost_equal(outputSample, metaModel(inputSample), 3.0e-5, 3.0e-5);
// Kriging variance is 0 on learning points
CovarianceMatrix var(result.getConditionalCovariance(inputSample));
// assert_almost_equal could not be applied to matrices
Point covariancePoint(*var.getImplementation());
assert_almost_equal(covariancePoint, Point(covariancePoint.getSize()), 1e-6, 1e-6);
// Random vector evaluation
Sample unifRealization(Uniform(0.0, 10.0).getSample(2));
Point validationPoint(unifRealization.getImplementation()->getData());
KrigingRandomVector rvector(result, validationPoint);
// Realization of the random vector
Point realization (rvector.getRealization());
std::cout << "Realization of the KRV=" << realization << std::endl;
// Get a sample of size 10
Sample realizations(rvector.getSample(10));
std::cout << "Sample of realizations of the KRV=" << realizations << std::endl;
}
catch (TestFailed & ex)
{
std::cerr << ex << std::endl;
return ExitCode::Error;
}
return ExitCode::Success;
}
|
