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git-svn-id: https://tesseract-ocr.googlecode.com/svn/trunk@650 d0cd1f9f-072b-0410-8dd7-cf729c803f20
135 lines
5.7 KiB
C
135 lines
5.7 KiB
C
/******************************************************************************
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** Filename: cluster.h
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** Purpose: Definition of feature space clustering routines
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** Author: Dan Johnson
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** History: 5/29/89, DSJ, Created.
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**
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** (c) Copyright Hewlett-Packard Company, 1988.
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** Licensed under the Apache License, Version 2.0 (the "License");
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** you may not use this file except in compliance with the License.
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** You may obtain a copy of the License at
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** http://www.apache.org/licenses/LICENSE-2.0
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** Unless required by applicable law or agreed to in writing, software
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** distributed under the License is distributed on an "AS IS" BASIS,
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** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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** See the License for the specific language governing permissions and
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** limitations under the License.
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******************************************************************************/
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#ifndef CLUSTER_H
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#define CLUSTER_H
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#include "kdtree.h"
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#include "oldlist.h"
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struct BUCKETS;
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#define MINBUCKETS 5
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#define MAXBUCKETS 39
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/*----------------------------------------------------------------------
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Types
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----------------------------------------------------------------------*/
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typedef struct sample {
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unsigned Clustered:1; // TRUE if included in a higher cluster
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unsigned Prototype:1; // TRUE if cluster represented by a proto
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unsigned SampleCount:30; // number of samples in this cluster
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struct sample *Left; // ptr to left sub-cluster
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struct sample *Right; // ptr to right sub-cluster
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inT32 CharID; // identifier of char sample came from
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FLOAT32 Mean[1]; // mean of cluster - SampleSize floats
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} CLUSTER;
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typedef CLUSTER SAMPLE; // can refer to as either sample or cluster
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typedef enum {
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spherical, elliptical, mixed, automatic
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} PROTOSTYLE;
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typedef struct { // parameters to control clustering
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PROTOSTYLE ProtoStyle; // specifies types of protos to be made
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FLOAT32 MinSamples; // min # of samples per proto - % of total
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FLOAT32 MaxIllegal; // max percentage of samples in a cluster which have
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// more than 1 feature in that cluster
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FLOAT32 Independence; // desired independence between dimensions
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FLOAT64 Confidence; // desired confidence in prototypes created
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int MagicSamples; // Ideal number of samples in a cluster.
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} CLUSTERCONFIG;
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typedef enum {
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normal, uniform, D_random, DISTRIBUTION_COUNT
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} DISTRIBUTION;
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typedef union {
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FLOAT32 Spherical;
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FLOAT32 *Elliptical;
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} FLOATUNION;
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typedef struct {
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unsigned Significant:1; // TRUE if prototype is significant
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unsigned Merged:1; // Merged after clustering so do not output
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// but kept for display purposes. If it has no
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// samples then it was actually merged.
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// Otherwise it matched an already significant
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// cluster.
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unsigned Style:2; // spherical, elliptical, or mixed
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unsigned NumSamples:28; // number of samples in the cluster
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CLUSTER *Cluster; // ptr to cluster which made prototype
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DISTRIBUTION *Distrib; // different distribution for each dimension
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FLOAT32 *Mean; // prototype mean
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FLOAT32 TotalMagnitude; // total magnitude over all dimensions
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FLOAT32 LogMagnitude; // log base e of TotalMagnitude
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FLOATUNION Variance; // prototype variance
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FLOATUNION Magnitude; // magnitude of density function
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FLOATUNION Weight; // weight of density function
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} PROTOTYPE;
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typedef struct {
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inT16 SampleSize; // number of parameters per sample
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PARAM_DESC *ParamDesc; // description of each parameter
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inT32 NumberOfSamples; // total number of samples being clustered
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KDTREE *KDTree; // for optimal nearest neighbor searching
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CLUSTER *Root; // ptr to root cluster of cluster tree
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LIST ProtoList; // list of prototypes
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inT32 NumChar; // # of characters represented by samples
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// cache of reusable histograms by distribution type and number of buckets.
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BUCKETS* bucket_cache[DISTRIBUTION_COUNT][MAXBUCKETS + 1 - MINBUCKETS];
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} CLUSTERER;
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typedef struct {
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inT32 NumSamples; // number of samples in list
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inT32 MaxNumSamples; // maximum size of list
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SAMPLE *Sample[1]; // array of ptrs to sample data structures
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} SAMPLELIST;
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// low level cluster tree analysis routines.
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#define InitSampleSearch(S,C) (((C)==NULL)?(S=NIL_LIST):(S=push(NIL_LIST,(C))))
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/*--------------------------------------------------------------------------
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Public Function Prototypes
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--------------------------------------------------------------------------*/
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CLUSTERER *MakeClusterer (inT16 SampleSize, const PARAM_DESC ParamDesc[]);
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SAMPLE *MakeSample(CLUSTERER * Clusterer, const FLOAT32* Feature, inT32 CharID);
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LIST ClusterSamples(CLUSTERER *Clusterer, CLUSTERCONFIG *Config);
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void FreeClusterer(CLUSTERER *Clusterer);
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void FreeProtoList(LIST *ProtoList);
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void FreePrototype(void *arg); // PROTOTYPE *Prototype);
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CLUSTER *NextSample(LIST *SearchState);
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FLOAT32 Mean(PROTOTYPE *Proto, uinT16 Dimension);
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FLOAT32 StandardDeviation(PROTOTYPE *Proto, uinT16 Dimension);
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inT32 MergeClusters(inT16 N, PARAM_DESC ParamDesc[], inT32 n1, inT32 n2,
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FLOAT32 m[], FLOAT32 m1[], FLOAT32 m2[]);
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//--------------Global Data Definitions and Declarations---------------------------
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// define errors that can be trapped
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#define ALREADYCLUSTERED 4000
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#endif
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