/******************************************************************************
 ** Filename:   cluster.h
 ** Purpose:    Definition of feature space clustering routines
 ** Author:     Dan Johnson
 **
 ** (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"

namespace tesseract {

struct BUCKETS;

#define MINBUCKETS 5
#define MAXBUCKETS 39

/*----------------------------------------------------------------------
          Types
----------------------------------------------------------------------*/
typedef struct sample {
  bool Clustered : 1;         // true if included in a higher cluster
  bool 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_t CharID;             // identifier of char sample came from
  float Mean[1];              // mean of cluster - SampleSize floats
} CLUSTER;

using SAMPLE = CLUSTER;  // 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
  float MinSamples;       // min # of samples per proto - % of total
  float MaxIllegal;       // max percentage of samples in a cluster which
                          // have more than 1 feature in that cluster
  float Independence;     // desired independence between dimensions
  double 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 {
  float Spherical;
  float* Elliptical;
} FLOATUNION;

typedef struct {
  bool Significant : 1;      // true if prototype is significant
  bool 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
  float* Mean;               // prototype mean
  float TotalMagnitude;      // total magnitude over all dimensions
  float 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_t SampleSize;       // number of parameters per sample
  PARAM_DESC* ParamDesc;    // description of each parameter
  int32_t 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_t 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_t NumSamples;     // number of samples in list
  int32_t 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) == nullptr) ? (S = NIL_LIST) : (S = push(NIL_LIST, (C))))

/*--------------------------------------------------------------------------
        Public Function Prototypes
--------------------------------------------------------------------------*/
TESS_API
CLUSTERER* MakeClusterer(int16_t SampleSize, const PARAM_DESC ParamDesc[]);

TESS_API
SAMPLE* MakeSample(CLUSTERER* Clusterer, const float* Feature, int32_t CharID);

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

TESS_API
void FreeClusterer(CLUSTERER* Clusterer);

TESS_API
void FreeProtoList(LIST* ProtoList);

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

CLUSTER* NextSample(LIST* SearchState);

float Mean(PROTOTYPE* Proto, uint16_t Dimension);

float StandardDeviation(PROTOTYPE* Proto, uint16_t Dimension);

TESS_API
int32_t MergeClusters(int16_t N, PARAM_DESC ParamDesc[], int32_t n1, int32_t n2,
                      float m[], float m1[], float m2[]);

} // namespace tesseract

#endif
