/usr/include/roboptim/core/derivable-parametrized-function.hh is in libroboptim-core-dev 2.0-6.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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//
// 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/>.
#ifndef ROBOPTIM_TRAJECTORY_DERIVABLE_PARAMETRIZABLE_HH
# define ROBOPTIM_TRAJECTORY_DERIVABLE_PARAMETRIZABLE_HH
# include <utility>
# include <roboptim/core/fwd.hh>
# include <roboptim/core/parametrized-function.hh>
# include <roboptim/core/portability.hh>
namespace roboptim
{
/// \addtogroup roboptim_meta_function
/// @{
/// \brief Parametrized function with parameter derivative available.
///
/// Depending on inner function type, this class allows computation
/// of parameter derivative or combined parameter/function derivative.
///
/// \tparam F inner function type.
template <typename F>
class DerivableParametrizedFunction : public ParametrizedFunction<F>
{
public:
/// \brief Import value type.
typedef typename F::value_type value_type;
/// \brief Import size type.
typedef typename F::size_type size_type;
/// \brief Import vector type.
typedef typename F::vector_t vector_t;
/// \brief Import matrix type.
typedef typename F::matrix_t matrix_t;
/// \brief Import result type.
typedef F result_t;
/// \brief Import argument type.
typedef typename F::argument_t argument_t;
/// \brief Import gradient type.
typedef typename F::vector_t gradient_t;
/// \brief Import jacobian type.
typedef typename F::matrix_t jacobian_t;
/// \brief Import jacobian size type (pair of values).
typedef typename F::jacobianSize_t jacobianSize_t;
/// \brief Return the gradient size.
///
/// Gradient size is equals to the input size.
size_type gradientSize () const throw ()
{
return this->inputSize ();
}
/// \brief Return the jacobian size as a pair.
///
/// Gradient size is equals to (output size, input size).
jacobianSize_t jacobianSize () const throw ()
{
return std::make_pair (this->inputSize (),
this->functionOutputSize ());
}
/// \brief Check if the gradient is valid (check size).
/// \param gradient checked gradient
/// \return true if valid, false if not
bool isValidGradient (const gradient_t& gradient) const throw ()
{
return gradient.size () == this->gradientSize ();
}
/// \brief Check if the jacobian is valid (check sizes).
///
/// \param jacobian checked jacobian
/// \return true if valid, false if not
bool isValidJacobian (const jacobian_t& jacobian) const throw ()
{
return jacobian.rows () == this->jacobianSize ().first
&& jacobian.cols () == this->jacobianSize ().second;
}
/// \brief Computes the jacobian.
///
/// \param argument point at which the jacobian will be computed
/// \param order derivation order
/// \return jacobian matrix
jacobian_t jacobian (const argument_t& argument, size_type order = 0)
const throw ()
{
jacobian_t jacobian (jacobianSize ().first, jacobianSize ().second);
jacobian.setZero ();
this->jacobian (jacobian, argument, order);
return jacobian;
}
/// \brief Computes the jacobian.
///
/// Program will abort if the jacobian size is wrong before
/// or after the jacobian computation.
/// \param jacobian jacobian will be stored in this argument
/// \param order derivation order
/// \param argument inner function point argument value
void jacobian (jacobian_t& jacobian, const argument_t& argument,
size_type order = 0) const throw ()
{
assert (argument.size () == this->inputSize ());
assert (this->isValidJacobian (jacobian));
this->impl_jacobian (jacobian, argument, order);
assert (this->isValidJacobian (jacobian));
}
/// \brief Computes the gradient.
///
/// \param argument inner function argument value
/// \param functionId function id in split representation
/// \param order derivation order
/// \return gradient vector
gradient_t gradient (const argument_t& argument,
size_type functionId = 0,
size_type order = 0) const throw ()
{
gradient_t gradient (gradientSize ());
gradient.setZero ();
this->gradient (gradient, argument, functionId, order);
return gradient;
}
/// \brief Computes the gradient.
///
/// Program will abort if the gradient size is wrong before
/// or after the gradient computation.
/// \param gradient gradient will be stored in this argument
/// \param argument inner function point argument value
/// \param functionId function id in split representation
/// \param order derivation order
/// \return gradient vector
void gradient (gradient_t& gradient,
const argument_t& argument,
size_type functionId = 0,
size_type order = 0) const throw ()
{
assert (argument.size () == this->inputSize ());
assert (this->isValidGradient (gradient));
this->impl_gradient (gradient, argument, functionId, order);
assert (this->isValidGradient (gradient));
}
/// \brief Display the function on the specified output stream.
///
/// \param o output stream used for display
/// \return output stream
virtual std::ostream& print (std::ostream& o) const throw ()
{
return o << "Derivable parametrized function";
}
protected:
/// \brief Concrete class constructor should call this constructor.
///
/// \param inputSize parameter size
/// \param functionInputSize inner function argument size
/// \param functionOutputSize inner function result size
DerivableParametrizedFunction (size_type inputSize,
size_type functionInputSize,
size_type functionOutputSize) throw ()
: ParametrizedFunction<F> (inputSize,
functionInputSize,
functionOutputSize)
{
}
/// \brief Jacobian evaluation.
///
/// Computes the jacobian, can be overridden by concrete classes.
/// The default behavior is to compute the jacobian from the gradient.
/// \warning Do not call this function directly, call #jacobian instead.
/// \param jacobian jacobian will be store in this argument
/// \param arg point where the jacobian will be computed
virtual void impl_jacobian (jacobian_t& jacobian, const argument_t& arg)
const throw ()
{
for (size_type i = 0; i < this->functionOutputSize (); ++i)
{
gradient_t grad = this->gradient (arg, i);
for (size_type j = 0; j < this->inputSize (); ++j)
jacobian (i, j) = grad[j];
}
}
/// \brief Gradient evaluation.
///
/// Compute the gradient, has to be implemented in concrete classes.
/// The gradient is computed for a specific sub-function which id
/// is passed through the functionId argument.
/// \warning Do not call this function directly, call #gradient instead.
/// \param gradient gradient will be store in this argument
/// \param argument inner function point argument value
/// \param functionId evaluated function id in the split representation
/// \param order derivation order
virtual void impl_gradient (gradient_t& gradient,
const argument_t& argument,
size_type functionId = 0,
size_type order = 0)
const throw () = 0;
};
/// @}
} // end of namespace roboptim.
#endif //! ROBOPTIM_TRAJECTORY_N_TIMES_DERIVABLE_HH
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