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KernelMachine.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) 1999-2009 Soeren Sonnenburg
8  * Written (W) 2011-2012 Heiko Strathmann
9  * Copyright (C) 1999-2009 Fraunhofer Institute FIRST and Max-Planck-Society
10  */
11 
12 #ifndef _KERNEL_MACHINE_H__
13 #define _KERNEL_MACHINE_H__
14 
15 #include <shogun/lib/config.h>
16 
17 #include <shogun/lib/common.h>
18 #include <shogun/machine/Machine.h>
19 #include <shogun/lib/SGVector.h>
20 
21 
22 namespace shogun
23 {
24 class CLabels;
25 class CBinaryLabels;
26 class CRegressionLabels;
27 class CKernel;
28 class CCustomKernel;
29 class CFeatures;
30 
51 class CKernelMachine : public CMachine
52 {
53  public:
56 
66 
71 
73  virtual ~CKernelMachine();
74 
80  virtual const char* get_name() const { return "KernelMachine"; }
81 
86  void set_kernel(CKernel* k);
87 
93 
98  void set_batch_computation_enabled(bool enable);
99 
105 
110  void set_linadd_enabled(bool enable);
111 
116  bool get_linadd_enabled();
117 
122  void set_bias_enabled(bool enable_bias);
123 
128  bool get_bias_enabled();
129 
135 
140  void set_bias(float64_t bias);
141 
147  int32_t get_support_vector(int32_t idx);
148 
154  float64_t get_alpha(int32_t idx);
155 
162  bool set_support_vector(int32_t idx, int32_t val);
163 
170  bool set_alpha(int32_t idx, float64_t val);
171 
176  int32_t get_num_support_vectors();
177 
182  void set_alphas(SGVector<float64_t> alphas);
183 
189 
192 
195 
200  bool create_new_model(int32_t num);
201 
207 
214  virtual CRegressionLabels* apply_regression(CFeatures* data=NULL);
215 
222  virtual CBinaryLabels* apply_binary(CFeatures* data=NULL);
223 
229  virtual float64_t apply_one(int32_t num);
230 
236  static void* apply_helper(void* p);
237 
244  virtual bool train_locked(SGVector<index_t> indices);
245 
253 
261  SGVector<index_t> indices);
262 
270  SGVector<index_t> indices);
271 
280  virtual void data_lock(CLabels* labs, CFeatures* features=NULL);
281 
283  virtual void data_unlock();
284 
286  virtual bool supports_locking() const;
287 
288  protected:
289 
296 
303  virtual void store_model_features();
304 
305  private:
307  void init();
308 
309  protected:
312 
315 
318 
321 
324 
326  bool use_bias;
327 
330 
333 
336 };
337 }
338 #endif /* _KERNEL_MACHINE_H__ */
virtual float64_t apply_one(int32_t num)
SGVector< float64_t > apply_get_outputs(CFeatures *data)
SGVector< int32_t > m_svs
void set_bias_enabled(bool enable_bias)
virtual CBinaryLabels * apply_locked_binary(SGVector< index_t > indices)
Real Labels are real-valued labels.
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
The Custom Kernel allows for custom user provided kernel matrices.
Definition: CustomKernel.h:36
virtual const char * get_name() const
Definition: KernelMachine.h:80
virtual CRegressionLabels * apply_regression(CFeatures *data=NULL)
SGVector< int32_t > get_support_vectors()
CCustomKernel * m_custom_kernel
static void * apply_helper(void *p)
A generic KernelMachine interface.
Definition: KernelMachine.h:51
A generic learning machine interface.
Definition: Machine.h:143
void set_support_vectors(SGVector< int32_t > svs)
virtual bool train_locked(SGVector< index_t > indices)
SGVector< float64_t > m_alpha
virtual void store_model_features()
void set_bias(float64_t bias)
void set_batch_computation_enabled(bool enable)
virtual SGVector< float64_t > apply_locked_get_output(SGVector< index_t > indices)
double float64_t
Definition: common.h:50
bool set_alpha(int32_t idx, float64_t val)
virtual void data_unlock()
float64_t get_alpha(int32_t idx)
virtual bool supports_locking() const
bool set_support_vector(int32_t idx, int32_t val)
int32_t get_support_vector(int32_t idx)
SGVector< float64_t > get_alphas()
all of classes and functions are contained in the shogun namespace
Definition: class_list.h:18
virtual CRegressionLabels * apply_locked_regression(SGVector< index_t > indices)
void set_alphas(SGVector< float64_t > alphas)
The class Features is the base class of all feature objects.
Definition: Features.h:68
void set_linadd_enabled(bool enable)
The Kernel base class.
Definition: Kernel.h:159
Binary Labels for binary classification.
Definition: BinaryLabels.h:37
void set_kernel(CKernel *k)
virtual CBinaryLabels * apply_binary(CFeatures *data=NULL)
bool create_new_model(int32_t num)
virtual void data_lock(CLabels *labs, CFeatures *features=NULL)

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