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efcm.h
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efcm.h
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/***************************************************************************
* Copyright (C) 2007 by vahid mokhtari and Ramin Fathzadeh *
* mokhtari@mrl.ir *
* fathzadeh@mrl.ir *
* *
* This program is free software; you can redistribute it and/or modify *
* it under the terms of the GNU General Public License as published by *
* the Free Software Foundation; either version 2 of the License, or *
* (at your option) any later version. *
* *
* This program 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 General Public License for more details. *
* *
* You should have received a copy of the GNU General Public License *
* along with this program; if not, write to the *
* Free Software Foundation, Inc., *
* 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. *
***************************************************************************/
#include "load.h"
#define U(i,j) U[j][i]
#define Co_AssocN (((N)*(N))-N)/2
#define Co_Assoc(i,j) Co_Assoc[(((i)*(N))+(j)) - (((i)*(i)+((3*(i))+2))/2)]
/* clustering methods */
#define _FCM "fcm"
#define _EFCM "efcm"
/* consensus methods */
#define RELABELING 0
#define COASSOCIATION 1
/* dataset types */
#define STATIC 0
#define STREAM 1
/* cluster type */
#define JOINED 0 // integrate all data with a cluster label in a one file
#define DISJOINED 1 // put data with the same cluster label in separated files
extern int OS; /* specify the operating system. (defined in main.cpp)
0: windows, 1: linux */
extern int ClusterGeneration;
/* specify how to generate cluster number in each run
of fcm, that can be either 0:random or 1:sequential. */
/***************************************************************************/
/* Fuzzy C-Means Class */
/***************************************************************************/
class FuzzyCMeans
{
public:
/* Constructors */
FuzzyCMeans ( );
FuzzyCMeans ( double Epsilon,
double FuzzyExp,
int NrClusters,
int NrDimensions,
long NrData,
int NrTrueClusters = 2,
int ClusterValidation = 0,
int FirstClusterLabel = 0,
double *MaxValues = NULL );
~ FuzzyCMeans ( );
/* Parameters */
void ResetParameters ( double Epsilon,
double FuzzyExp,
int NrClusters,
int NrDimensions,
long NrData,
double *MaxValues = NULL );
void GetParameters ( double &Epsilon,
double &FuzzyExp,
int &NrClusters,
int &NrDimensions,
long &NrData );
void GetParameters ( char *FileName,
double &Epsilon,
double &FuzzyExp,
int &NrClusters,
int &NrDimensions,
long &NrData );
/* Main functions */
int FCM ( int displayinfo = 1 );
int Init ( );
int UpdateCentroids ( );
double UpdateUmatrix ( );
double ClusterValidation ( );
int GetNrFinalCluster ( );
int GetFinalClusters ( int * f );
int GetClusters ( double ** v );
/* Utilities */
int IsExampleCentroid ( int k );
double Distance ( double * v1, double * v2 );
/* Public Output Utilities */
double ** GetCentroids ( );
double ** GetUmatrix ( );
int * GetMembers ( int * m );
int WriteCentroids ( char * filename );
int WriteUmatrix ( char * filename );
int WriteMembers ( char * filename );
int WriteClusters ( char * filename );
int WriteMRIClusters ( int col = 256,
char * filename = NULL );
/* Referral **X to external **x data */
void SetData ( double ** x );
void SetTrueClusters ( int * tc );
void SetMaxValue ( double * maxvalue );
/* Print info into console. */
void PrintParameters ( int display = 1 );
void PrintData ( int display = 1 );
protected:
double Epsilon; // square error threshold
double m; // fuzzification exponent
int C; // number of clusters
int D; // dimension of input data
long N; // number of input data
int NumberOfIterations;
double ** V; // cluster prototypes (centers) V[C][D]
double ** U; // membership matrix U[N][C]
double ** X; // input data X[N][D]
double * MaxValue; // maximum value of attributes (used in first random init) MaxValue[D]
/* cluster validation attributes */
int K; // number of true clusters (labels) ,used in validity
int S; // first cluster label ,used in validity
int * TC; // true clusters (labels) TC[N] ,used in validity
int Validation; // validate cclustering result
long ** ConfusionMatrix; /* Virtually all criteria for comparing clusterings can be computed given
the so-called confusion matrix. Assume that we have two clusterings
C ={C1,C2, . . . ,CK} and C' = {C'1,C'2 , . . . ,C'K'}. The confusion
matrix M =(mij) is a K x K' matrix whose ijth element is the number
of points in the intersection of clusters Ci and C'j ,
i.e., mij = |Ci \ C'j |. */
};
/***************************************************************************/
/* Ensemble Fuzzy C-Means Class */
/***************************************************************************/
class EnssembleFuzzyCMeans
{
public:
/* Constructors */
EnssembleFuzzyCMeans ( );
EnssembleFuzzyCMeans ( double MinEpsilon = 0.0001,
double MaxEpsilon = 0.0001,
double MinFuzzyexp = 2.0,
double MaxFuzzyexp = 3.0,
int MinCluster = 2,
int MaxCluster = 2,
long NrData = 2,
int NrDimension = 2,
int NrTrueClusters = 2,
int FirstClusterLabel = 0,
int NrClusterings = 2,
int ClusterValidation = 0,
int NrForceClusters = 0,
int ConsensusMethod = COASSOCIATION );
~ EnssembleFuzzyCMeans ( );
/* Parameters */
void ResetParameters ( double MinEpsilon = 0.0001,
double MaxEpsilon = 0.0001,
double MinFuzzyexp = 2.0,
double MaxFuzzyexp = 3.0,
int MinCluster = 2,
int MaxCluster = 2,
long NrData = 2,
int NrDimension = 2,
int NrTrueClusters = 2,
int FirstClusterLabel = 0,
int NrClusterings = 2,
int ClusterValidation = 0,
int NrForceClusters = 0,
int ConsensusMethod = COASSOCIATION );
void GetParameters ( double &min_epsilon,
double &max_epsilon,
double &min_fuzzyexp,
double &max_fuzzyexp,
int &min_k, int &max_k,
int &d, long &n, int &c );
/* Main functions */
int EFCM ( char * logfile = NULL );
int Init ( );
int MakeClusters ( char * logfile = NULL );
int * UpdateCoAssocUmatrix( int c = 0, char * logfile = NULL );
double ClusterValidation ( );
int GetNrFinalCluster ( );
int GetFinalClusters ( int * f );
int GetClusters ( double ** v );
/* Referral **X to external **x data */
void SetData ( double **x );
void SetTrueClusters ( int * tc );
void SetMaxValue ( double * maxvalue );
double * GetMaxValue ( );
/* Print info into console. */
void PrintParameters ( int display = 1 );
int WriteMembers ( char * filename );
int WriteCentroids ( char * filename );
int WriteClusters ( char * filename,
int integration = JOINED );
int WriteMRIClusters ( int col = 256,
char * filename = NULL );
int WritePartitions ( char * filename );
int WriteCoAssocMatrix ( char * filename );
int WriteSLTree ( char * filename );
protected:
/* public attributes */
double Min_Epsilon; // minimum square error threshold
double Max_Epsilon; // maximum square error threshold
double Min_m; // maximum fuzzification exponent
double Max_m; // maximum fuzzification exponent
int Min_K; // maximum number of clusters
int Max_K; // maximum number of clusters
int D; // dimension of input data
long N; // length of input data
int C; // number of clusterings (ensembles)
int FC; // number of final clusters
double ** X; // input data X[N][D]
int * F; // final partition (cluster name of each element) F[N]
double ** V; // cluster prototypes (centers) V[FC][D]
int Consensus; // integration strategy (consensus)
double * MaxValue; // maximum value of attributes MaxValue[D]
FuzzyCMeans * fcm;
/* relabeling & voting attributes */
int ** P; // partitions P[C][N] (used in relabeling & voting matrix)
/* evidence accumulation attributes */
int ** FinalClusters; // FinalClusters[C][N]
int ForceC; // number of final clusters which efcm forced to produce them
int * Co_Assoc; // co-association matrix C[N][N] in fact: (C[(N*N-N)/2])
long ** SL[2]; /* single-linkage tree SL[2][N][C]
first dimension is either '0' (left) or '1' (right) which means '0'
contains left neighbor and '1' contains right neighbor in its cluster.
second dimension is the index of data in **X for left and right neighbor.
third dimension is the level of single-linkage tree.
every SL[][i][j] contains the left and right co-clusters for i'th data
in j'th level of single-linkage tree. */
long ** Clusters; /* store number of clusters and level of each partition.
first dimension contains two values involve
cluster-number ([0]) and level of partition ([1]).
second dimension is the maximum partition number ([C+1]),
actually is the number of ensembles */
long Clustersidx; /* index of Clusters[2][C+1] (used in second dimension) */
/* cluster validation attributes */
int K; // number of true clusters (labels) ,used in validity
int S; // first cluster label ,used in validity
int * TC; // true clusters (labels) TC[N] ,used in validity
int Validation; // validate cclustering result
long ** ConfusionMatrix; /* Virtually all criteria for comparing clusterings can be computed given
the so-called confusion matrix. Assume that we have two clusterings
C ={C1,C2, . . . ,CK} and C' = {C'1,C'2 , . . . ,C'K'}. The confusion
matrix M =(mij) is a K x K' matrix whose ijth element is the number
of points in the intersection of clusters Ci and C'j ,
i.e., mij = |Ci \ C'j |. */
};
/***************************************************************************/
/* Stream Ensemble Fuzzy C-Means Class */
/***************************************************************************/
class StreamEnssembleFuzzyCMeans
{
public:
/* Constructors */
StreamEnssembleFuzzyCMeans ( );
StreamEnssembleFuzzyCMeans ( double MinEpsilon = 0.0001,
double MaxEpsilon = 0.0001,
double MinFuzzyexp = 2.0,
double MaxFuzzyexp = 3.0,
int MinCluster = 2,
int MaxCluster = 2,
long NrData = 2,
int NrDimension = 2,
long BlockSize = 2,
int NrTrueClusters = 2,
int FirstClusterLabel = 0,
int NrClusterings = 2,
int ClusterValidation = 0,
int NrForceClusters = 0,
int ConsensusMethod = COASSOCIATION,
char * BaseClusterMethod = _EFCM );
~ StreamEnssembleFuzzyCMeans ( );
/* Parameters */
void ResetParameters ( double MinEpsilon = 0.0001,
double MaxEpsilon = 0.0001,
double MinFuzzyexp = 2.0,
double MaxFuzzyexp = 3.0,
int MinCluster = 2,
int MaxCluster = 2,
long NrData = 2,
int NrDimension = 2,
long BlockSize = 1000,
int NrTrueClusters = 2,
int FirstClusterLabel = 0,
int NrClusterings = 2,
int ClusterValidation = 0,
int NrForceClusters = 0,
int ConsensusMethod = COASSOCIATION,
char * BaseClusterMethod = _EFCM );
/* Main functions */
int Init ( char * logfile = NULL );
int SEFCM ( long block = 1000,
char * logfile = NULL );
int ReCluster ( char * logfile = NULL );
double ClusterValidation ( );
/* Referral **X to external **x data */
void SetData ( double **x );
double ** GetData ( );
void SetTrueClusters ( int * tc );
int * GetTrueClusters ( );
int * GetTrueClusters ( int * tc );
void SetMaxValue ( double * maxvalue );
double * GetMaxValue ( );
/* Print info into console. */
void PrintParameters ( int display = 1 );
int WriteMembers ( char * filename );
int WriteMRIMembers ( char * filename );
int WriteCentroids ( char * filename );
int WriteClusters ( char * filename );
protected:
/* public attributes */
double Min_Epsilon; // minimum square error threshold
double Max_Epsilon; // maximum square error threshold
double Min_m; // maximum fuzzification exponent
double Max_m; // maximum fuzzification exponent
int Min_K; // maximum number of clusters
int Max_K; // maximum number of clusters
int D; // dimension of input data
long N; // length of input data
long B; // size of blocks should be loaded from input data iteratively
int C; // number of clusterings (ensembles)
int FC; // number of final clusters
double ** X; // input data X[N][D]
int * F; // final partition (cluster name of each element) F[N]
double ** V; // cluster prototypes (centers) V[FC][D]
int Consensus; // integration strategy (consensus)
double * MaxValue; // maximum value of attributes MaxValue[D]
char * BaseMethod; // base clustering method which can be either "efcm" or "fcm"
/* evidence accumulation attributes */
int ForceC; // number of final clusters which efcm forced to produce them
/* stream attributes at every block */
int BIdx; // blocks indexes that proceed to 'Blocks'
int NrBlocks; // block numbers that equales to (N / B)
int * BFC; // number of final clusters at every block FC[NrBlocks]
int ** BF; // final partition at every block (cluster name of each element) F[NrBlocks][B]
double *** BV; // cluster prototypes (centers) at every block V[NrBlocks][BFC][D]
long * BL; // data length for each efcm clustering at every block L[NrBlocks]
int ** BTC; // true clusters (labels) at every block TC[NrBlocks][B]
/* cluster validation attributes */
int K; // number of true clusters (labels) ,used in validity
int S; // first cluster label ,used in validity
int * TC; // true clusters (labels) TC[N] ,used in validity
int Validation; // validate cclustering result
long ** ConfusionMatrix; /* Virtually all criteria for comparing clusterings can be computed given
the so-called confusion matrix. Assume that we have two clusterings
C ={C1,C2, . . . ,CK} and C' = {C'1,C'2 , . . . ,C'K'}. The confusion
matrix M =(mij) is a K x K' matrix whose ijth element is the number
of points in the intersection of clusters Ci and C'j ,
i.e., mij = |Ci ^ C'j|. */
};