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
|
// -*- C++ -*-
/**
* @brief The test file of GeneralLinearModelAlgorithm 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
{
// Set Numerical precision to 4
PlatformInfo::SetNumericalPrecision(4);
UnsignedInteger sampleSize = 40;
UnsignedInteger inputDimension = 1;
// Create the function to estimate
SymbolicFunction model("x0", "x0");
Sample X(sampleSize, inputDimension);
for (UnsignedInteger i = 0; i < sampleSize; ++ i)
X(i, 0) = 3.0 + (8.0 * i) / sampleSize;
Sample Y = model(X);
// Add a small noise to data
Y += GaussianProcess(AbsoluteExponential(Point(1, 0.1), Point(1, 0.2)), Mesh(X)).getRealization().getValues();
Basis basis = LinearBasisFactory(inputDimension).build();
// Case of a misspecified covariance model
DiracCovarianceModel covarianceModel(inputDimension);
GeneralLinearModelAlgorithm algo(X, Y, covarianceModel, basis);
algo.run();
GeneralLinearModelResult result = algo.getResult();
Point ref = {0.1957};
assert_almost_equal(result.getCovarianceModel().getParameter(), ref, 1e-4, 1e-4);
ref = {-0.1109, 1.015};
assert_almost_equal(result.getTrendCoefficients(), ref, 1e-4, 1e-4);
// Now without estimating covariance parameters
basis = LinearBasisFactory(inputDimension).build();
covarianceModel = DiracCovarianceModel(inputDimension);
algo = GeneralLinearModelAlgorithm(X, Y, covarianceModel, basis, true);
algo.setOptimizeParameters(false);
algo.run();
result = algo.getResult();
ref = {1.0};
assert_almost_equal(result.getCovarianceModel().getParameter(), ref, 1e-4, 1e-4);
ref = {-0.1109, 1.015};
assert_almost_equal(result.getTrendCoefficients(), ref, 1e-4, 1e-4);
// Case of a well specified covariance model
// Test the optimization when the amplitude is deduced analytically from the scale
{
AbsoluteExponential covarianceModel2(inputDimension);
algo = GeneralLinearModelAlgorithm(X, Y, covarianceModel2, basis);
algo.run();
result = algo.getResult();
ref = {0.1328, 0.1956};
assert_almost_equal(result.getCovarianceModel().getParameter(), ref, 1e-4, 1e-4);
ref = {-0.1034, 1.014};
assert_almost_equal(result.getTrendCoefficients(), ref, 1e-4, 1e-4);
ResourceMap::SetAsBool("GeneralLinearModelAlgorithm-UnbiasedVariance", false);
algo = GeneralLinearModelAlgorithm(X, Y, covarianceModel2, basis);
algo.run();
result = algo.getResult();
ref = {0.1328, 0.1907};
assert_almost_equal(result.getCovarianceModel().getParameter(), ref, 1e-4, 1e-4);
ref = {-0.1034, 1.014};
assert_almost_equal(result.getTrendCoefficients(), ref, 1e-4, 1e-4);
ResourceMap::SetAsBool("GeneralLinearModelAlgorithm-UseAnalyticalAmplitudeEstimate", false);
algo = GeneralLinearModelAlgorithm(X, Y, covarianceModel2, basis);
algo.run();
result = algo.getResult();
ref = {0.01, 0.1908};
assert_almost_equal(result.getCovarianceModel().getParameter(), ref, 1e-2, 1e-2);
ref = {-0.111, 1.015};
assert_almost_equal(result.getTrendCoefficients(), ref, 1e-4, 1e-4);
}
}
catch (TestFailed & ex)
{
std::cerr << ex << std::endl;
return ExitCode::Error;
}
return ExitCode::Success;
}
|
