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// Copyright (C) 2009 by Thomas Moulard, AIST, CNRS, INRIA.
//
// This file is part of the roboptim.
//
// roboptim 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.
//
// roboptim 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 roboptim. If not, see <http://www.gnu.org/licenses/>.
#include "shared-tests/fixture.hh"
#include <iostream>
#include <roboptim/core/io.hh>
#include <roboptim/core/twice-differentiable-function.hh>
#include <roboptim/core/util.hh>
using namespace roboptim;
struct Null : public TwiceDifferentiableFunction
{
Null () : TwiceDifferentiableFunction (1, 1, "null function")
{}
void impl_compute (result_t& res, const argument_t&) const throw ()
{
res.setZero ();
}
void impl_gradient (gradient_t& grad, const argument_t&,
size_type) const throw ()
{
grad.setZero ();
}
void impl_hessian (hessian_t& h, const argument_t&,
size_type) const throw ()
{
h.setZero ();
}
};
struct NoTitle : public TwiceDifferentiableFunction
{
NoTitle () : TwiceDifferentiableFunction (1, 1)
{}
void impl_compute (result_t& res, const argument_t&) const throw ()
{
res.setZero ();
}
void impl_gradient (gradient_t& grad, const argument_t&,
size_type) const throw ()
{
grad.setZero ();
}
void impl_hessian (hessian_t& h, const argument_t&, size_type)
const throw ()
{
h.setZero ();
}
};
BOOST_FIXTURE_TEST_SUITE (core, TestSuiteConfiguration)
BOOST_AUTO_TEST_CASE (twice_derivable_function)
{
boost::shared_ptr<boost::test_tools::output_test_stream>
output = retrievePattern ("twice-derivable-function");
Null null;
NoTitle notitle;
Null::vector_t x (1);
x[0] = 42.;
Null::hessian_t h (null.hessianSize ().first,
null.hessianSize ().second);
Null::gradient_t grad (null.gradientSize ());
(*output) << null << std::endl
<< notitle << std::endl;
(*output) << null.inputSize () << std::endl
<< notitle.inputSize () << std::endl;
(*output) << null.outputSize () << std::endl
<< notitle.outputSize () << std::endl;
(*output) << null.getName () << std::endl
<< notitle.getName () << std::endl;
(*output) << null.isValidResult (null (x)) << std::endl
<< notitle.isValidResult (notitle (x)) << std::endl;
(*output) << null (x) << std::endl
<< notitle (x) << std::endl;
(*output) << null.gradient (x) << std::endl
<< notitle.gradient (x) << std::endl;
null.gradient (grad, x);
(*output) << grad << std::endl;
notitle.gradient (grad, x);
(*output) << grad << std::endl;
(*output) << null.gradientSize () << std::endl
<< notitle.gradientSize () << std::endl;
(*output) << null.jacobianSize () << std::endl
<< notitle.jacobianSize () << std::endl;
(*output) << null.isValidGradient (null.gradient (x)) << std::endl
<< notitle.isValidGradient (notitle.gradient (x)) << std::endl;
(*output) << null.isValidJacobian (null.jacobian (x)) << std::endl
<< notitle.isValidJacobian (notitle.jacobian (x)) << std::endl;
(*output) << null.hessian (x) << std::endl
<< notitle.hessian (x) << std::endl;
null.hessian (h, x);
(*output) << h << std::endl;
notitle.hessian (h, x);
(*output) << h << std::endl;
(*output) << null.hessianSize () << std::endl
<< notitle.hessianSize () << std::endl;
(*output) << null.isValidHessian (null.hessian (x)) << std::endl
<< notitle.isValidHessian (notitle.hessian (x)) << std::endl;
std::cout << output->str () << std::endl;
BOOST_CHECK (output->match_pattern ());
}
BOOST_AUTO_TEST_SUITE_END ()
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