/usr/include/shogun/clustering/Hierarchical.h is in libshogun-dev 3.2.0-7.5.
This file is owned by root:root, with mode 0o644.
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* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2007-2009 Soeren Sonnenburg
* Copyright (C) 2007-2009 Fraunhofer Institute FIRST and Max-Planck-Society
*/
#ifndef _HIERARCHICAL_H__
#define _HIERARCHICAL_H__
#include <stdio.h>
#include <shogun/lib/common.h>
#include <shogun/io/SGIO.h>
#include <shogun/distance/Distance.h>
#include <shogun/machine/DistanceMachine.h>
namespace shogun
{
class CDistanceMachine;
/** @brief Agglomerative hierarchical single linkage clustering.
*
* Starting with each object being assigned to its own cluster clusters are
* iteratively merged. Here the clusters are merged whose elements have
* minimum distance, i.e. the clusters A and B that obtain
*
* \f[
* \min\{d({\bf x},{\bf x'}): {\bf x}\in {\cal A},{\bf x'}\in {\cal B}\}
* \f]
*
* are merged.
*
* cf e.g. http://en.wikipedia.org/wiki/Data_clustering*/
class CHierarchical : public CDistanceMachine
{
public:
/** default constructor */
CHierarchical();
/** constructor
*
* @param merges the merges
* @param d distance
*/
CHierarchical(int32_t merges, CDistance* d);
virtual ~CHierarchical();
/** problem type */
MACHINE_PROBLEM_TYPE(PT_MULTICLASS);
/** get classifier type
*
* @return classifier type HIERARCHICAL
*/
virtual EMachineType get_classifier_type();
/** load distance machine from file
*
* @param srcfile file to load from
* @return if loading was successful
*/
virtual bool load(FILE* srcfile);
/** save distance machine to file
*
* @param dstfile file to save to
* @return if saving was successful
*/
virtual bool save(FILE* dstfile);
/** set merges
*
* @param m new merges
*/
inline void set_merges(int32_t m)
{
ASSERT(m>0)
merges=m;
}
/** get merges
*
* @return merges
*/
int32_t get_merges();
/** get assignment
*
*/
SGVector<int32_t> get_assignment();
/** get merge distance
*
*/
SGVector<float64_t> get_merge_distances();
/** get cluster pairs
*
*/
SGMatrix<int32_t> get_cluster_pairs();
/** @return object name */
virtual const char* get_name() const { return "Hierarchical"; }
protected:
/** estimate hierarchical clustering
*
* @param data training data (parameter can be avoided if distance or
* kernel-based classifiers are used and distance/kernels are
* initialized with train data)
*
* @return whether training was successful
*/
virtual bool train_machine(CFeatures* data=NULL);
/** TODO: Ensures cluster centers are in lhs of underlying distance
* Currently: does nothing.
* */
virtual void store_model_features();
virtual bool train_require_labels() const { return false; }
protected:
/// the number of merges in hierarchical clustering
int32_t merges;
/// number of dimensions
int32_t dimensions;
/// size of assignment table
int32_t assignment_size;
/// cluster assignment for the num_points
int32_t* assignment;
/// size of the below tables
int32_t table_size;
/// tuples of i/j
int32_t* pairs;
/// distance at which pair i/j was added
float64_t* merge_distance;
};
}
#endif
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