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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/common.h>
16 #include <shogun/io/SGIO.h>
17 #include <shogun/kernel/Kernel.h>
19 #include <shogun/labels/Labels.h>
20 #include <shogun/machine/Machine.h>
21 
22 #include <stdio.h>
23 
24 namespace shogun
25 {
26 class CMachine;
27 class CLabels;
28 class CKernel;
29 
50 class CKernelMachine : public CMachine
51 {
52  public:
55 
65 
70 
72  virtual ~CKernelMachine();
73 
79  virtual const char* get_name() const { return "KernelMachine"; }
80 
85  void set_kernel(CKernel* k);
86 
92 
97  void set_batch_computation_enabled(bool enable);
98 
104 
109  void set_linadd_enabled(bool enable);
110 
115  bool get_linadd_enabled();
116 
121  void set_bias_enabled(bool enable_bias);
122 
127  bool get_bias_enabled();
128 
134 
139  void set_bias(float64_t bias);
140 
146  int32_t get_support_vector(int32_t idx);
147 
153  float64_t get_alpha(int32_t idx);
154 
161  bool set_support_vector(int32_t idx, int32_t val);
162 
169  bool set_alpha(int32_t idx, float64_t val);
170 
175  int32_t get_num_support_vectors();
176 
181  void set_alphas(SGVector<float64_t> alphas);
182 
188 
191 
194 
199  bool create_new_model(int32_t num);
200 
206 
213  virtual CRegressionLabels* apply_regression(CFeatures* data=NULL);
214 
221  virtual CBinaryLabels* apply_binary(CFeatures* data=NULL);
222 
228  virtual float64_t apply_one(int32_t num);
229 
235  static void* apply_helper(void* p);
236 
243  virtual bool train_locked(SGVector<index_t> indices);
244 
252 
260  SGVector<index_t> indices);
261 
269  SGVector<index_t> indices);
270 
279  virtual void data_lock(CLabels* labs, CFeatures* features=NULL);
280 
282  virtual void data_unlock();
283 
285  virtual bool supports_locking() const;
286 
287  protected:
288 
295 
302  virtual void store_model_features();
303 
304  private:
306  void init();
307 
308  protected:
311 
314 
317 
320 
323 
325  bool use_bias;
326 
329 
332 
335 };
336 }
337 #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:35
The Custom Kernel allows for custom user provided kernel matrices.
Definition: CustomKernel.h:33
virtual const char * get_name() const
Definition: KernelMachine.h:79
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:50
A generic learning machine interface.
Definition: Machine.h:138
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:48
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:16
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:62
void set_linadd_enabled(bool enable)
The Kernel base class.
Definition: Kernel.h:150
Binary Labels for binary classification.
Definition: BinaryLabels.h:36
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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