AbstractNearestNeighbors.h

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//===========================================================================
/*!
 * 
 *
 * \brief       Interface for nearest Neighbor queries
 * 
 * 
 *
 * \author      O.Krause
 * \date        2012-2014
 *
 *
 * \par Copyright 1995-2017 Shark Development Team
 * 
 * <BR><HR>
 * This file is part of Shark.
 * <https://shark-ml.github.io/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_ALGORITHMS_NEARESTNEIGHBORS_ABSTRACTNEARESTNEIGHBORS_H
#define SHARK_ALGORITHMS_NEARESTNEIGHBORS_ABSTRACTNEARESTNEIGHBORS_H
 
#include <shark/Core/utility/KeyValuePair.h>
#include <shark/Data/Dataset.h>
namespace shark{
 
    
/// \brief Interface for Nearest Neighbor queries.
///
/// Defines the abstract interface for query of nearest neighbors. This is used to generalize over the different algorithms
/// to query for nearest neighbors.
template<class InputType,class LabelType>
class AbstractNearestNeighbors{
public:
    typedef KeyValuePair<double,LabelType> DistancePair;
    typedef typename Batch<InputType>::type BatchInputType;
 
    ///\brief Returns the k-nearest neighbors of a batch of points and returns them as linearized array.
    ///
    ///Given a batch of size n, a array with nxk values is returned where each entry is a key-value pair of distance and label.
    ///the first k entries are the neighbors of point 1, the next k of point 2 and so on.
    virtual std::vector<DistancePair> getNeighbors(BatchInputType const& batch, std::size_t k) const = 0;
 
    /// \brief Returns the expected shape of the inputs
    Shape const& inputShape() const{
        return m_inputShape;
    }
    
    ///\brief returns a const reference to the dataset used by the algorithm
    virtual LabeledData<InputType,LabelType>const& dataset()const = 0;
 
    virtual ~AbstractNearestNeighbors() {}
protected:
    Shape m_inputShape;
};
 
    
}
 
#endif