/usr/include/shark/Models/MeanModel.h is in libshark-dev 3.0.1+ds1-2ubuntu1.
This file is owned by root:root, with mode 0o644.
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/*!
*
*
* \brief Implements the Mean Model that can be used for ensemble classifiers
*
*
*
* \author Kang Li, O. Krause
* \date 2014
*
*
* \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_MODELS_MEANMODEL_H
#define SHARK_MODELS_MEANMODEL_H
namespace shark {
///
/// \brief Calculates the weighted mean of a set of models
///
template<class ModelType>
class MeanModel : public AbstractModel<typename ModelType::InputType, typename ModelType::OutputType>
{
private:
typedef AbstractModel<typename ModelType::InputType, typename ModelType::OutputType> base_type;
public:
/// Constructor
MeanModel():m_weightSum(0){}
std::string name() const
{ return "MeanModel"; }
using base_type::eval;
void eval(typename base_type::BatchInputType const& patterns, typename base_type::BatchOutputType& outputs)const{
m_models[0].eval(patterns,outputs);
outputs *=m_weight[0];
for(std::size_t i = 1; i != m_models.size(); i++)
noalias(outputs) += m_weight[i] * m_models[i](patterns);
outputs /= m_weightSum;
}
void eval(typename base_type::BatchInputType const& patterns, typename base_type::BatchOutputType& outputs, State& state)const{
eval(patterns,outputs);
}
/// This model does not have any parameters.
RealVector parameterVector() const {
return RealVector();
}
/// This model does not have any parameters
void setParameterVector(const RealVector& param) {
SHARK_ASSERT(param.size() == 0);
}
void read(InArchive& archive){
archive >> m_models;
archive >> m_weight;
archive >> m_weightSum;
}
void write(OutArchive& archive)const{
archive << m_models;
archive << m_weight;
archive << m_weightSum;
}
/// \brief Removes all models from the ensemble
void clearModels(){
m_models.clear();
m_weight.clear();
m_weightSum = 0.0;
}
/// \brief Adds a new model to the ensemble.
///
/// \param model the new model
/// \param weight weight of the model. must be > 0
void addModel(ModelType const& model, double weight = 1.0){
SHARK_CHECK(weight > 0, "Weights must be positive");
m_models.push_back(model);
m_weight.push_back(weight);
m_weightSum+=weight;
}
/// \brief Returns the weight of the i-th model
double const& weight(std::size_t i)const{
return m_weight[i];
}
/// \brief sets the weight of the i-th model
void setWeight(std::size_t i, double newWeight){
m_weightSum=newWeight - m_weight[i];
m_weight[i] = newWeight;
}
/// \brief Returns the number of models.
std::size_t numberOfModels()const{
return m_models.size();
}
protected:
/// collection of models.
std::vector<ModelType> m_models;
/// Weight of the mean.
std::vector<double> m_weight;
/// Total sum of weights.
double m_weightSum;
};
}
#endif // SHARK_MODELS_MEANMODEL_H
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