AbstractClustering.h

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//===========================================================================
/*!
 * 
 *
 * \brief       Super class for clustering definitions.
 * \file
 * 
 *
 * \author      T. Glasmachers
 * \date        2011
 *
 *
 * \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_MODELS_CLUSTERING_ABSTRACTCLUSTERING_H
#define SHARK_MODELS_CLUSTERING_ABSTRACTCLUSTERING_H
 
 
#include <shark/Data/BatchInterface.h>
#include <shark/Core/Flags.h>
#include <shark/Core/INameable.h>
#include <shark/Core/IParameterizable.h>
#include <shark/Core/ISerializable.h>
#include <shark/Core/Shape.h>
 
namespace shark {
    
///\defgroup clustering Clustering Algorithms
/// 
/// A variety of models and algorithms for clustering
/// \ingroup models
 
///
/// \brief Base class for clustering.
///
/// \par
/// Clustering algorithms vary widely in the data structures
/// on which they operate. For example, simple centroid-based
/// approaches such as k-means are mutually incompatible with
/// tree-based hierarchical clustering. This interface class
/// is the attempt to cast different cluster descriptions into
/// a minimal common interface that is useful for prediction.
///
/// \par
/// There are at least two common types of predictions made
/// with clusterings. The first one is the assignment of the
/// best matching cluster to a patters, such as in vector
/// quantization or unsupervised clustering. This is often
/// referred to as "hard clustering". The second one is the
/// computation of a membership function ("soft clustering").
/// The membership of a pattern to a cluster is non-negative,
/// and the total membership should add to one.
///
/// \par
/// This interface makes minimal assumptions to allow for
/// these types of predictions. It assumes that clusters can
/// be enumbered (identified by an index), and that at least
/// a membership test can be made (hard clustering). It is
/// optional to provide a membership function. Only one of
/// the two interfaces for best matching cluster or membership
/// function need to be implemented, since the best matching
/// cluster can be deduced from the membership function.
/// However, often the best matching cluster can be computed
/// more efficiently than the membership function. In these
/// cases both interface functions should be implemented.
///
/// \par
/// The purpose of this interface is to act as a common
/// super class to different data structures describing the
/// outcome of a clustering operation. The interface allows
/// all of these data structures to be used in the two
/// clustering model classes: HardClusteringModel and
/// SoftClusteringModel.
///
/// \ingroup clustering
template <class InputT>
class AbstractClustering : public INameable, public IParameterizable<>, public ISerializable
{
public:
    typedef InputT InputType;
    typedef unsigned int OutputType;
    typedef typename Batch<InputType>::type BatchInputType;
    typedef Batch<OutputType>::type BatchOutputType;
 
    enum Feature {
        HAS_SOFT_MEMBERSHIP = 1,
    };
    SHARK_FEATURE_INTERFACE;
 
    /// Tests whether the clustering can compute a (soft)
    /// member ship function, describing the membership
    /// of a sample to the different clusters.
    bool hasSoftMembershipFunction()const{
        return m_features & HAS_SOFT_MEMBERSHIP;
    }
 
    /// return the number of clusters
    virtual std::size_t numberOfClusters() const = 0;
    
    virtual Shape inputShape() const = 0;
 
    /// \brief Compute best matching cluster.
    ///
    /// \par
    /// This function should be overriden by sub-classes to
    /// compute the cluster best matching the input pattern.
    /// The (typically slow) default implementation is to
    /// create a batch of size 1 and return the result of the batch call to hardMembership
    virtual unsigned int hardMembership(InputType const& pattern) const{
        typename Batch<InputType>::type b = Batch<InputType>::createBatch(pattern);
        getBatchElement(b,0) = pattern;
        return hardMembership(b)(0);
    }
    
    /// \brief Compute best matching cluster for a batch of inputs.
    ///
    /// \par
    /// This function should be overriden by sub-classes to
    /// compute the cluster best matching the input pattern.
    /// The (typically slow) default implementation is to
    /// return the arg max of the soft membership function for every pattern.
    virtual BatchOutputType hardMembership(BatchInputType const& patterns) const{
        std::size_t numPatterns = batchSize(patterns);
        RealMatrix f = softMembership(patterns);
        SHARK_ASSERT(f.size2() > 0);
        SHARK_ASSERT(f.size1() == numPatterns);
        BatchOutputType outputs(numPatterns);
        for(std::size_t i = 0; i != numPatterns;++i){
            auto membership = row(f,i);
            outputs(i) = (unsigned int)(std::max_element(membership.begin(),membership.end())-membership.begin());
        }
        return outputs;
    }
 
    /// \brief Compute cluster membership function.
    ///
    /// \par
    /// This function should be overriden by sub-classes to
    /// compute the membership of a pattern to the clusters.
    /// The default implementation creates a batch of size 1 
    /// and calls the batched version. If this is not overriden, an xception is thrown.
    virtual RealVector softMembership(InputType const& pattern) const{
        auto output = softMembership(Batch<InputType>::createBatch(pattern));
        return row(output,0);
    }
    
    /// \brief Compute cluster membership function.
    ///
    /// \par
    /// This function should be overriden by sub-classes to
    /// compute the membership of a pattern to the clusters.
    /// This default implementation throws an exception.
    virtual RealMatrix softMembership(BatchInputType const& patterns) const{
        SHARK_FEATURE_EXCEPTION(HAS_SOFT_MEMBERSHIP);
    }
 
    /// empty default implementation of ISerializable::read
    void read(InArchive& archive) {}
 
    /// empty default implementation of ISerializable::write
    void write(OutArchive& archive) const {}
};
 
 
}
#endif