tesseract/classify/cluster.h

/******************************************************************************
 **	Filename:	cluster.h
 **	Purpose:	Definition of feature space clustering routines
 **	Author:		Dan Johnson
 **	History:	5/29/89, DSJ, Created.
 **
 **	(c) Copyright Hewlett-Packard Company, 1988.
 ** Licensed under the Apache License, Version 2.0 (the "License");
 ** you may not use this file except in compliance with the License.
 ** You may obtain a copy of the License at
 ** http://www.apache.org/licenses/LICENSE-2.0
 ** Unless required by applicable law or agreed to in writing, software
 ** distributed under the License is distributed on an "AS IS" BASIS,
 ** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 ** See the License for the specific language governing permissions and
 ** limitations under the License.
 ******************************************************************************/
#ifndef   CLUSTER_H
#define   CLUSTER_H

#include "kdtree.h"
#include "oldlist.h"

/*----------------------------------------------------------------------
          Types
----------------------------------------------------------------------*/
typedef struct sample
{
  unsigned Clustered:1;          // TRUE if included in a higher cluster
  unsigned Prototype:1;          // TRUE if cluster represented by a proto
  unsigned SampleCount:30;       // number of samples in this cluster
  struct sample *Left;           // ptr to left sub-cluster
  struct sample *Right;          // ptr to right sub-cluster
  INT32 CharID;                  // identifier of char sample came from
  FLOAT32 Mean[1];               // mean of cluster - SampleSize floats
}


CLUSTER;

typedef CLUSTER SAMPLE;          // can refer to as either sample or cluster

typedef enum {
  spherical, elliptical, mixed, automatic
}


PROTOSTYLE;

typedef struct                   // parameters to control clustering
{
  PROTOSTYLE ProtoStyle;         // specifies types of protos to be made
  FLOAT32 MinSamples;            // min # of samples per proto - % of total
  FLOAT32 MaxIllegal;            // max percentage of samples in a cluster which have
  // more than 1 feature in that cluster
  FLOAT32 Independence;          // desired independence between dimensions
  FLOAT64 Confidence;            // desired confidence in prototypes created
}


CLUSTERCONFIG;

typedef enum {
  normal, uniform, D_random
}


DISTRIBUTION;

typedef union
{
  FLOAT32 Spherical;
  FLOAT32 *Elliptical;

}


FLOATUNION;

typedef struct proto
{
  unsigned Significant:1;        // TRUE if prototype is significant
  unsigned Style:2;              // spherical, elliptical, or mixed
  unsigned NumSamples:29;        // number of samples in the cluster
  CLUSTER *Cluster;              // ptr to cluster which made prototype
  DISTRIBUTION *Distrib;         // different distribution for each dimension
  FLOAT32 *Mean;                 // prototype mean
  FLOAT32 TotalMagnitude;        // total magnitude over all dimensions
  FLOAT32 LogMagnitude;          // log base e of TotalMagnitude
  FLOATUNION Variance;           // prototype variance
  FLOATUNION Magnitude;          // magnitude of density function
  FLOATUNION Weight;             // weight of density function
}


PROTOTYPE;

typedef struct
{
  INT16 SampleSize;              // number of parameters per sample
  PARAM_DESC *ParamDesc;         // description of each parameter
  INT32 NumberOfSamples;         // total number of samples being clustered
  KDTREE *KDTree;                // for optimal nearest neighbor searching
  CLUSTER *Root;                 // ptr to root cluster of cluster tree
  LIST ProtoList;                // list of prototypes
  INT32 NumChar;                 // # of characters represented by samples
}


CLUSTERER;

typedef struct
{
  INT32 NumSamples;              // number of samples in list
  INT32 MaxNumSamples;           // maximum size of list
  SAMPLE *Sample[1];             // array of ptrs to sample data structures
}


SAMPLELIST;

// low level cluster tree analysis routines.
#define InitSampleSearch(S,C) (((C)==NULL)?(S=NIL):(S=push(NIL,(C))))

/*--------------------------------------------------------------------------
        Public Function Prototypes
--------------------------------------------------------------------------*/
CLUSTERER *MakeClusterer (INT16 SampleSize, PARAM_DESC ParamDesc[]);

SAMPLE *MakeSample (CLUSTERER * Clusterer, FLOAT32 Feature[], INT32 CharID);

LIST ClusterSamples(CLUSTERER *Clusterer, CLUSTERCONFIG *Config); 

void FreeClusterer(CLUSTERER *Clusterer); 

void FreeProtoList(LIST *ProtoList); 

void FreePrototype(void *arg);  //PROTOTYPE     *Prototype);

CLUSTER *NextSample(LIST *SearchState); 

FLOAT32 Mean(PROTOTYPE *Proto, UINT16 Dimension); 

FLOAT32 StandardDeviation(PROTOTYPE *Proto, UINT16 Dimension); 

//--------------Global Data Definitions and Declarations---------------------------
// define errors that can be trapped
#define ALREADYCLUSTERED  4000
#endif