cluster.h

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/*

Copyright (c) 2003, Cornell University
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

   - Redistributions of source code must retain the above copyright notice,
       this list of conditions and the following disclaimer.
   - Redistributions in binary form must reproduce the above copyright
       notice, this list of conditions and the following disclaimer in the
       documentation and/or other materials provided with the distribution.
   - Neither the name of Cornell University nor the names of its
       contributors may be used to endorse or promote products derived from
       this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
THE POSSIBILITY OF SUCH DAMAGE.

*/

#if !defined _CLUSTER_H
#define _CLUSTER_H

#include "general.h"
#include <stdio.h>
#include "vec.h"
#include <string>
#include <iostream>

using namespace TNT;
using namespace std;

namespace CLUS
{

/**  Cluster is the abstract base class for cluster hierarchy.
     This class can both identify and run a cluster.
     The H in front of distance functions means that that distance is used
     for the hierarchical version of the algorithm
 */
class Cluster
{
public:
    enum State {Final,Splited,Splitable,Dead};

protected:
    int inDim, outDim;
    const double* dataCache;
    double distanceInfer,distanceClus;
    double weight;
    double dimension;
    State state;

public:
    inline Cluster(int InDim=0, int OutDim=0):
            inDim(InDim), outDim(OutDim), dataCache(NULL), distanceInfer(0.0),
            distanceClus(0.0),weight(1.0), state(Splitable)
    {}
    
    State GetState(void)
    {
        return state;
    }

    /// Initializes the cluster with random values
    void    RandomCluster(void)
    {}
    
    void    InitalizeOnPoint(const double*)
    {}
    
    double InferDistance(const double* DataCache)
    {
        return 0.0;
    }
    
    double ClusDistance(const double* DataCache)
    {
        return 0.0;
    }
    
    inline double LastInferDistance(void)
    {
        return distanceInfer;
    }
    
    inline double LastClusDistance(void)
    {
        return distanceClus;
    }

    /// Called after all distances of the
    /// clusters have been computed so the ponder of
    /// each cluster in final result can be computed
    void CorrectAppartGrade(double Coef)
    {}
    
    void HCorrectAppartGrade(double Coef)
    {}

    /** Finalizes one big step of the identification
                 process.  Computes the new prototype of the cluster
                 and returns the distance between the old and the
                 new prototype so a decision if the proces should
                 continue can be taken
    */
    double AdjustPrototypes(void)
    {
        return 0.0;
    }
    
    double HAdjustPrototypes(void)
    {
        return 0.0;
    }
    
    void AdjustYwithYoverD(Vector<double>& Y)
    {}

    void AdjustYwithYoverD(Vector<double>& Y, double)
    {}

    /// Returns the consequent value of the cluster for the
    /// last input introduced as a parameter of Distance
    double ComputeLastY(void)
    {
        return 0.0;
    }
    
    static string TypeName(void)
    {
        return string("Cluster");
    }
    
    void SaveToStream(ostream& )
    {}
    
    void LoadFromStream(ostream& )
    {}

    bool Split(Cluster& right, Cluster& save)
    {
        return false;
    }
    
    bool SetOptionDbl(char* optName,double value)
    {
        if ( strcmp(optName,"weight") )
        {
            weight=value;
            return true;
        }
        return false;
    }
};

} // namespace CLUS


#endif    /* _CLUSTER_H */

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