/usr/share/openturns/examples/t_MergeRandomAndConstantInput.cxx is in openturns-examples 1.3-3.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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/**
* @file t_MergeRandomAndConstantInput.cxx
* @brief The test file of class NumericalMathFunction for standard methods
*
* Copyright (C) 2005-2014 Airbus-EDF-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
* along with this library. If not, see <http://www.gnu.org/licenses/>.
*
* @author schueller
* @date 2012-02-17 19:35:43 +0100 (Fri, 17 Feb 2012)
*/
#include "OT.hxx"
#include "OTtestcode.hxx"
using namespace OT;
using namespace OT::Test;
int main(int argc, char *argv[])
{
TESTPREAMBLE;
OStream fullprint(std::cout);
try
{
/** External code. This code has an input vector of dimension 4, namely (p0, p1, p2, p3)'. */
Description input(4);
input[0] = "E";
input[1] = "F";
input[2] = "L";
input[3] = "I";
NumericalMathFunction externalCode(input, Description(1, "d"), Description(1, "F*L^3/(3*E*I)"));
UnsignedLong dim(externalCode.getInputDimension());
/** The external code will be connected to 2 independent random variables X0 and X1 and one deterministic variable X2 with the following scheme:
X2->p0
X0->p1
X1->p2
X0->p3
It means that (p0, p1, p2, p3)' = A.(X0, X1)' + b with:
A = [0 0] b = [X2]
[1 0] [ 0]
[0 1] [ 0]
[1 0] [ 0]
Here we build the linear function x -> A.x + b
*/
UnsignedLong stochasticDimension(2);
// UnsignedLong deterministicDimension(1);
Matrix A(dim, stochasticDimension);
A(1, 0) = 1;
A(2, 1) = 1;
A(3, 0) = 1;
NumericalPoint b(dim, 0);
NumericalScalar X2(50.0);
b[0] = X2;
NumericalMathFunction connect;
NumericalPoint zero(stochasticDimension, 0);
/** A LinearNumericalMathFunction will arrive soon... */
connect.setEvaluationImplementation(new LinearNumericalMathEvaluationImplementation(zero, b, A.transpose()));
connect.setGradientImplementation(new ConstantNumericalMathGradientImplementation(A.transpose()));
connect.setHessianImplementation(new ConstantNumericalMathHessianImplementation(SymmetricTensor(stochasticDimension, dim)));
/** We are now ready to build the resulting code externalCode(connect()) */
NumericalMathFunction finalCode(externalCode, connect);
/** Check if it worked */
NumericalPoint x(connect.getInputDimension());
x[0] = 5;
x[1] = 10;
fullprint << "finalCode(x)=" << finalCode(x) << std::endl;
NumericalPoint xRef(dim);
xRef[0] = X2;
xRef[1] = x[0];
xRef[2] = x[1];
xRef[3] = x[0];
fullprint << "ref=" << externalCode(xRef) << std::endl;
}
catch (TestFailed & ex)
{
std::cerr << ex << std::endl;
return ExitCode::Error;
}
return ExitCode::Success;
}
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