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KNN.h
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1 /*
2  * This program is free software; you can redistribute it and/or modify
3  * it under the terms of the GNU General Public License as published by
4  * the Free Software Foundation; either version 3 of the License, or
5  * (at your option) any later version.
6  *
7  * Written (W) 2006 Christian Gehl
8  * Written (W) 1999-2009 Soeren Sonnenburg
9  * Written (W) 2011 Sergey Lisitsyn
10  * Written (W) 2012 Fernando José Iglesias García, cover tree support
11  * Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
12  */
13 
14 #ifndef _KNN_H__
15 #define _KNN_H__
16 
17 #include <shogun/lib/config.h>
18 
19 #include <stdio.h>
20 #include <shogun/lib/common.h>
21 #include <shogun/io/SGIO.h>
25 
26 namespace shogun
27 {
28 
29 class CDistanceMachine;
30 
57 class CKNN : public CDistanceMachine
58 {
59  public:
61 
62 
63  CKNN();
64 
71  CKNN(int32_t k, CDistance* d, CLabels* trainlab);
72  virtual ~CKNN();
73 
78  virtual EMachineType get_classifier_type() { return CT_KNN; }
79 
90 
96  virtual CMulticlassLabels* apply_multiclass(CFeatures* data=NULL);
97 
99  virtual float64_t apply_one(int32_t vec_idx)
100  {
101  SG_ERROR("for performance reasons use apply() instead of apply(int32_t vec_idx)\n")
102  return 0;
103  }
104 
109 
115  virtual bool load(FILE* srcfile);
116 
122  virtual bool save(FILE* dstfile);
123 
128  inline void set_k(int32_t k)
129  {
130  ASSERT(k>0)
131  m_k=k;
132  }
133 
138  inline int32_t get_k()
139  {
140  return m_k;
141  }
142 
146  inline void set_q(float64_t q)
147  {
148  ASSERT(q<=1.0 && q>0.0)
149  m_q = q;
150  }
151 
155  inline float64_t get_q() { return m_q; }
156 
160  inline void set_use_covertree(bool use_covertree)
161  {
162  m_use_covertree = use_covertree;
163  }
164 
168  inline bool get_use_covertree() const { return m_use_covertree; }
169 
171  virtual const char* get_name() const { return "KNN"; }
172 
173  protected:
178  virtual void store_model_features();
179 
183  virtual CMulticlassLabels* classify_NN();
184 
188  void init_distance(CFeatures* data);
189 
198  virtual bool train_machine(CFeatures* data=NULL);
199 
200  private:
201  void init();
202 
215  int32_t choose_class(float64_t* classes, int32_t* train_lab);
216 
229  void choose_class_for_multiple_k(int32_t* output, int32_t* classes, int32_t* train_lab, int32_t step);
230 
231  protected:
233  int32_t m_k;
234 
237 
240 
242  int32_t m_num_classes;
243 
245  int32_t m_min_label;
246 
249 };
250 
251 }
252 #endif
EMachineType
Definition: Machine.h:33
virtual void store_model_features()
Definition: KNN.cpp:450
virtual bool save(FILE *dstfile)
Definition: KNN.cpp:443
virtual EMachineType get_classifier_type()
Definition: KNN.h:78
Class Distance, a base class for all the distances used in the Shogun toolbox.
Definition: Distance.h:83
void init_distance(CFeatures *data)
Definition: KNN.cpp:422
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
float64_t get_q()
Definition: KNN.h:155
SGMatrix< int32_t > classify_for_multiple_k()
Definition: KNN.cpp:333
#define SG_ERROR(...)
Definition: SGIO.h:130
int32_t get_k()
Definition: KNN.h:138
int32_t m_min_label
smallest label, i.e. -1
Definition: KNN.h:245
virtual bool train_machine(CFeatures *data=NULL)
Definition: KNN.cpp:72
void set_q(float64_t q)
Definition: KNN.h:146
SGMatrix< index_t > nearest_neighbors()
Definition: KNN.cpp:109
A generic DistanceMachine interface.
virtual bool load(FILE *srcfile)
Definition: KNN.cpp:436
int32_t m_num_classes
number of classes (i.e. number of values labels can take)
Definition: KNN.h:242
Multiclass Labels for multi-class classification.
int32_t m_k
the k parameter in KNN
Definition: KNN.h:233
#define ASSERT(x)
Definition: SGIO.h:202
void set_use_covertree(bool use_covertree)
Definition: KNN.h:160
#define MACHINE_PROBLEM_TYPE(PT)
Definition: Machine.h:116
double float64_t
Definition: common.h:50
Class KNN, an implementation of the standard k-nearest neigbor classifier.
Definition: KNN.h:57
float64_t m_q
parameter q of rank weighting
Definition: KNN.h:236
SGVector< int32_t > m_train_labels
Definition: KNN.h:248
bool get_use_covertree() const
Definition: KNN.h:168
void set_k(int32_t k)
Definition: KNN.h:128
virtual const char * get_name() const
Definition: KNN.h:171
virtual ~CKNN()
Definition: KNN.cpp:68
The class Features is the base class of all feature objects.
Definition: Features.h:68
virtual CMulticlassLabels * classify_NN()
Definition: KNN.cpp:288
virtual CMulticlassLabels * apply_multiclass(CFeatures *data=NULL)
Definition: KNN.cpp:153
virtual float64_t apply_one(int32_t vec_idx)
get output for example "vec_idx"
Definition: KNN.h:99
bool m_use_covertree
parameter to enable cover tree support
Definition: KNN.h:239

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