/usr/include/shark/ObjectiveFunctions/AbstractCost.h is in libshark-dev 3.1.3+ds1-2.
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/*!
*
*
* \brief cost function for quantitative judgement of deviations of predictions from target values
*
*
*
* \author T. Glasmachers
* \date 2011
*
*
* \par Copyright 1995-2015 Shark Development Team
*
* <BR><HR>
* This file is part of Shark.
* <http://image.diku.dk/shark/>
*
* Shark 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.
*
* Shark 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 Shark. If not, see <http://www.gnu.org/licenses/>.
*
*/
#ifndef SHARK_OBJECTIVEFUNCTIONS_ABSTRACTCOST_H
#define SHARK_OBJECTIVEFUNCTIONS_ABSTRACTCOST_H
#include <shark/LinAlg/Base.h>
#include <shark/Core/INameable.h>
#include <shark/Core/Flags.h>
#include <shark/Data/Dataset.h>
namespace shark {
/// \brief Cost function interface
///
/// \par
/// In Shark a cost function encodes the severity of a deviation
/// of predictions from targets. This concept is more general than
/// that or a loss function, because it does not necessarily amount
/// to (uniformly) averaging a loss function over samples.
/// In general, the loss depends on the true (training) label and
/// the prediction in a not necessarily symmetric way. Also, in
/// the most general case predictions can be in a different format
/// than labels. E.g., the model prediction could be a probability
/// distribution, while the label is a single value.
///
/// \par
/// The concept of an AbstractCost function is different from that
/// encoded by the ErrorFunction class. A cost function compares
/// model predictions to labels. It does not know about the model
/// making the predictions, and thus it can not handle LabeledData
/// directly. However, it is one of the components necessary to
/// process LabeledData in an ErrorFunction.
///
template<class LabelT, class OutputT = LabelT>
class AbstractCost : public INameable
{
public:
typedef OutputT OutputType;
typedef LabelT LabelType;
typedef typename Batch<OutputType>::type BatchOutputType;
typedef typename Batch<LabelType>::type BatchLabelType;
virtual ~AbstractCost()
{ }
/// list of features a cost function can have
enum Feature {
HAS_FIRST_DERIVATIVE = 1,
HAS_SECOND_DERIVATIVE = 2,
IS_LOSS_FUNCTION = 4,
};
SHARK_FEATURE_INTERFACE;
/// returns true when the first parameter derivative is implemented
bool hasFirstDerivative() const{
return m_features & HAS_FIRST_DERIVATIVE;
}
//~ /// returns true when the second parameter derivative is implemented
//~ bool hasSecondDerivative() const{
//~ return m_features & HAS_SECOND_DERIVATIVE;
//~ }
/// returns true when the cost function is in fact a loss function
bool isLossFunction() const{
return m_features & IS_LOSS_FUNCTION;
}
/// Evaluates the cost of predictions, given targets.
/// \param targets target values
/// \param predictions predictions, typically made by a model
virtual double eval(Data<LabelType> const& targets, Data<OutputType> const& predictions) const = 0;
double operator () (Data<LabelType> const& targets, Data<OutputType> const& predictions) const
{ return eval(targets, predictions); }
};
}
#endif
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