--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/mupdf-source/thirdparty/tesseract/src/classify/cluster.h	Mon Sep 15 11:43:07 2025 +0200
@@ -0,0 +1,144 @@
+/******************************************************************************
+ ** 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
+----------------------------------------------------------------------*/
+struct CLUSTER {
+  CLUSTER(size_t n) : Mean(n) {
+  }
+
+  ~CLUSTER() {
+    delete Left;
+    delete Right;
+  }
+
+  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
+  CLUSTER *Left;       // ptr to left sub-cluster
+  CLUSTER *Right;      // ptr to right sub-cluster
+  int32_t CharID;            // identifier of char sample came from
+  std::vector<float> Mean;   // mean of cluster - SampleSize floats
+};
+using SAMPLE = CLUSTER; // can refer to as either sample or cluster
+
+typedef enum { spherical, elliptical, mixed, automatic } PROTOSTYLE;
+
+struct CLUSTERCONFIG {   // 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.
+};
+
+typedef enum { normal, uniform, D_random, DISTRIBUTION_COUNT } DISTRIBUTION;
+
+union FLOATUNION {
+  float Spherical;
+  float *Elliptical;
+};
+
+struct PROTOTYPE {
+  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
+  std::vector<DISTRIBUTION> Distrib; // different distribution for each dimension
+  std::vector<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
+};
+
+struct CLUSTERER {
+  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
+  uint32_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];
+};
+
+struct SAMPLELIST {
+  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
+};
+
+// 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, uint32_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