/usr/include/tesseract/cluster.h is in libtesseract-dev 3.02.01-6.
This file is owned by root:root, with mode 0o644.
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** 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"
struct BUCKETS;
#define MINBUCKETS 5
#define MAXBUCKETS 39
/*----------------------------------------------------------------------
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
int MagicSamples; // Ideal number of samples in a cluster.
} CLUSTERCONFIG;
typedef enum {
normal, uniform, D_random, DISTRIBUTION_COUNT
} DISTRIBUTION;
typedef union {
FLOAT32 Spherical;
FLOAT32 *Elliptical;
} FLOATUNION;
typedef struct {
unsigned Significant:1; // TRUE if prototype is significant
unsigned Merged:1; // Merged after clustering so do not output
// but kept for display purposes. If it has no
// samples then it was actually merged.
// Otherwise it matched an already significant
// cluster.
unsigned Style:2; // spherical, elliptical, or mixed
unsigned NumSamples:28; // 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
// cache of reusable histograms by distribution type and number of buckets.
BUCKETS* bucket_cache[DISTRIBUTION_COUNT][MAXBUCKETS + 1 - MINBUCKETS];
} 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_LIST):(S=push(NIL_LIST,(C))))
/*--------------------------------------------------------------------------
Public Function Prototypes
--------------------------------------------------------------------------*/
CLUSTERER *MakeClusterer (inT16 SampleSize, const PARAM_DESC ParamDesc[]);
SAMPLE *MakeSample(CLUSTERER * Clusterer, const 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);
inT32 MergeClusters(inT16 N, PARAM_DESC ParamDesc[], inT32 n1, inT32 n2,
FLOAT32 m[], FLOAT32 m1[], FLOAT32 m2[]);
//--------------Global Data Definitions and Declarations---------------------------
// define errors that can be trapped
#define ALREADYCLUSTERED 4000
#endif
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