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src
shogun
clustering
KMeans.h
浏览该文件的文档.
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/*
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* This program is free software; you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation; either version 3 of the License, or
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* (at your option) any later version.
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*
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* Written (W) 1999-2008 Gunnar Raetsch
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* Written (W) 2007-2009 Soeren Sonnenburg
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* Copyright (C) 1999-2009 Fraunhofer Institute FIRST and Max-Planck-Society
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*/
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#ifndef _KMEANS_H__
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#define _KMEANS_H__
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#include <stdio.h>
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#include <
shogun/lib/common.h
>
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#include <
shogun/io/SGIO.h
>
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#include <
shogun/features/DenseFeatures.h
>
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#include <
shogun/distance/Distance.h
>
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#include <
shogun/machine/DistanceMachine.h
>
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namespace
shogun
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{
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class
CDistanceMachine;
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enum
EKMeansMethod
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{
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KMM_MINI_BATCH
,
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KMM_LLOYD
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};
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class
CKMeans
:
public
CDistanceMachine
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{
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public
:
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CKMeans
();
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CKMeans
(int32_t k,
CDistance
* d,
EKMeansMethod
f);
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CKMeans
(int32_t k,
CDistance
* d,
bool
kmeanspp=
false
,
EKMeansMethod
f=
KMM_LLOYD
);
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CKMeans
(int32_t k_i,
CDistance
* d_i,
SGMatrix<float64_t>
centers_i,
EKMeansMethod
f=
KMM_LLOYD
);
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virtual
~CKMeans
();
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MACHINE_PROBLEM_TYPE
(
PT_MULTICLASS
)
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virtual
EMachineType
get_classifier_type
() {
return
CT_KMEANS
; }
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virtual
bool
load
(FILE* srcfile);
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virtual
bool
save
(FILE* dstfile);
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void
set_k
(int32_t p_k);
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int32_t
get_k
();
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void
set_use_kmeanspp
(
bool
kmpp);
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bool
get_use_kmeanspp
()
const
;
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void
set_fixed_centers
(
bool
fixed);
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bool
get_fixed_centers
();
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void
set_max_iter
(int32_t iter);
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float64_t
get_max_iter
();
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SGVector<float64_t>
get_radiuses
();
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SGMatrix<float64_t>
get_cluster_centers
();
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int32_t
get_dimensions
();
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virtual
const
char
*
get_name
()
const
{
return
"KMeans"
; }
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virtual
void
set_initial_centers
(
SGMatrix<float64_t>
centers);
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void
set_train_method
(
EKMeansMethod
f);
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EKMeansMethod
get_train_method
()
const
;
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void
set_mbKMeans_batch_size
(int32_t b);
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int32_t
get_mbKMeans_batch_size
()
const
;
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void
set_mbKMeans_iter
(int32_t t);
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int32_t
get_mbKMeans_iter
()
const
;
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void
set_mbKMeans_params
(int32_t b, int32_t t);
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private
:
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virtual
bool
train_machine(
CFeatures
* data=NULL);
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virtual
void
store_model_features();
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virtual
bool
train_require_labels()
const
{
return
false
; }
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SGMatrix<float64_t> kmeanspp();
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void
init();
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void
set_random_centers(SGVector<float64_t> weights_set, SGVector<int32_t> ClList, int32_t XSize);
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void
set_initial_centers
(SGVector<float64_t> weights_set,
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SGVector<int32_t> ClList, int32_t XSize);
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void
compute_cluster_variances();
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private
:
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int32_t max_iter;
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bool
fixed_centers;
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int32_t k;
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int32_t dimensions;
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SGVector<float64_t> R;
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SGMatrix<float64_t> mus_initial;
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bool
use_kmeanspp;
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int32_t batch_size;
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int32_t minib_iter;
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SGMatrix<float64_t> mus;
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EKMeansMethod
train_method;
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};
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}
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#endif
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