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Machine.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 _MACHINE_H__
13 #define _MACHINE_H__
14 
15 #include <shogun/lib/config.h>
16 
17 #include <shogun/lib/common.h>
18 #include <shogun/base/SGObject.h>
25 
26 namespace shogun
27 {
28 
29 class CFeatures;
30 class CLabels;
31 
34 {
35  CT_NONE = 0,
36  CT_LIGHT = 10,
38  CT_LIBSVM = 20,
41  CT_MPD = 50,
42  CT_GPBT = 60,
46  CT_LDA = 100,
47  CT_LPM = 110,
48  CT_LPBOOST = 120,
49  CT_KNN = 130,
50  CT_SVMLIN=140,
52  CT_GNPPSVM = 160,
53  CT_GMNPSVM = 170,
54  CT_SVMPERF = 200,
55  CT_LIBSVR = 210,
56  CT_SVRLIGHT = 220,
57  CT_LIBLINEAR = 230,
58  CT_KMEANS = 240,
60  CT_SVMOCAS = 260,
61  CT_WDSVMOCAS = 270,
62  CT_SVMSGD = 280,
68  CT_DASVM = 340,
69  CT_LARANK = 350,
73  CT_SGDQN = 390,
77  CT_QDA = 430,
78  CT_NEWTONSVM = 440,
80  CT_LARS = 460,
86  CT_CCSOSVM = 520,
92 };
93 
96 {
104 };
105 
108 {
114 };
115 
116 #define MACHINE_PROBLEM_TYPE(PT) \
117  \
120  virtual EProblemType get_machine_problem_type() const { return PT; }
121 
139 class CMachine : public CSGObject
140 {
141  public:
143  CMachine();
144 
146  virtual ~CMachine();
147 
157  virtual bool train(CFeatures* data=NULL);
158 
165  virtual CLabels* apply(CFeatures* data=NULL);
166 
168  virtual CBinaryLabels* apply_binary(CFeatures* data=NULL);
170  virtual CRegressionLabels* apply_regression(CFeatures* data=NULL);
172  virtual CMulticlassLabels* apply_multiclass(CFeatures* data=NULL);
174  virtual CStructuredLabels* apply_structured(CFeatures* data=NULL);
176  virtual CLatentLabels* apply_latent(CFeatures* data=NULL);
177 
182  virtual void set_labels(CLabels* lab);
183 
188  virtual CLabels* get_labels();
189 
195 
201 
207 
212  void set_solver_type(ESolverType st);
213 
219 
225  virtual void set_store_model_features(bool store_model);
226 
235  virtual bool train_locked(SGVector<index_t> indices)
236  {
237  SG_ERROR("train_locked(SGVector<index_t>) is not yet implemented "
238  "for %s\n", get_name());
239  return false;
240  }
241 
243  virtual float64_t apply_one(int32_t i)
244  {
246  return 0.0;
247  }
248 
254  virtual CLabels* apply_locked(SGVector<index_t> indices);
255 
258  SGVector<index_t> indices);
261  SGVector<index_t> indices);
264  SGVector<index_t> indices);
267  SGVector<index_t> indices);
270  SGVector<index_t> indices);
271 
280  virtual void data_lock(CLabels* labs, CFeatures* features);
281 
283  virtual void post_lock(CLabels* labs, CFeatures* features) { };
284 
286  virtual void data_unlock();
287 
289  virtual bool supports_locking() const { return false; }
290 
292  bool is_data_locked() const { return m_data_locked; }
293 
296  {
298  return PT_BINARY;
299  }
300 
301  virtual const char* get_name() const { return "Machine"; }
302 
303  protected:
314  virtual bool train_machine(CFeatures* data=NULL)
315  {
316  SG_ERROR("train_machine is not yet implemented for %s!\n",
317  get_name());
318  return false;
319  }
320 
331  virtual void store_model_features()
332  {
333  SG_ERROR("Model storage and therefore unlocked Cross-Validation and"
334  " Model-Selection is not supported for %s. Locked may"
335  " work though.\n", get_name());
336  }
337 
344  virtual bool is_label_valid(CLabels *lab) const
345  {
346  return true;
347  }
348 
350  virtual bool train_require_labels() const { return true; }
351 
352  protected:
355 
358 
361 
364 
367 };
368 }
369 #endif // _MACHINE_H__
virtual float64_t apply_one(int32_t i)
Definition: Machine.h:243
EMachineType
Definition: Machine.h:33
void set_max_train_time(float64_t t)
Definition: Machine.cpp:90
Base class of the labels used in Structured Output (SO) problems.
Real Labels are real-valued labels.
virtual CLabels * apply_locked(SGVector< index_t > indices)
Definition: Machine.cpp:195
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
ESolverType
Definition: Machine.h:95
float64_t m_max_train_time
Definition: Machine.h:354
CLabels * m_labels
Definition: Machine.h:357
#define SG_ERROR(...)
Definition: SGIO.h:130
#define SG_NOTIMPLEMENTED
Definition: SGIO.h:140
ESolverType m_solver_type
Definition: Machine.h:360
bool m_data_locked
Definition: Machine.h:366
virtual CStructuredLabels * apply_locked_structured(SGVector< index_t > indices)
Definition: Machine.cpp:267
virtual bool train_machine(CFeatures *data=NULL)
Definition: Machine.h:314
bool m_store_model_features
Definition: Machine.h:363
virtual const char * get_name() const
Definition: Machine.h:301
virtual bool train_locked(SGVector< index_t > indices)
Definition: Machine.h:235
A generic learning machine interface.
Definition: Machine.h:139
Multiclass Labels for multi-class classification.
virtual CBinaryLabels * apply_binary(CFeatures *data=NULL)
Definition: Machine.cpp:216
virtual void set_store_model_features(bool store_model)
Definition: Machine.cpp:115
EProblemType
Definition: Machine.h:107
virtual ~CMachine()
Definition: Machine.cpp:42
Class SGObject is the base class of all shogun objects.
Definition: SGObject.h:98
double float64_t
Definition: common.h:50
virtual CRegressionLabels * apply_regression(CFeatures *data=NULL)
Definition: Machine.cpp:222
virtual void data_unlock()
Definition: Machine.cpp:151
virtual void data_lock(CLabels *labs, CFeatures *features)
Definition: Machine.cpp:120
virtual CLabels * get_labels()
Definition: Machine.cpp:84
float64_t get_max_train_time()
Definition: Machine.cpp:95
ESolverType get_solver_type()
Definition: Machine.cpp:110
virtual CLatentLabels * apply_latent(CFeatures *data=NULL)
Definition: Machine.cpp:240
virtual EMachineType get_classifier_type()
Definition: Machine.cpp:100
virtual EProblemType get_machine_problem_type() const
Definition: Machine.h:295
virtual CRegressionLabels * apply_locked_regression(SGVector< index_t > indices)
Definition: Machine.cpp:253
virtual void store_model_features()
Definition: Machine.h:331
virtual bool supports_locking() const
Definition: Machine.h:289
virtual CMulticlassLabels * apply_locked_multiclass(SGVector< index_t > indices)
Definition: Machine.cpp:260
virtual CStructuredLabels * apply_structured(CFeatures *data=NULL)
Definition: Machine.cpp:234
virtual void post_lock(CLabels *labs, CFeatures *features)
Definition: Machine.h:283
virtual bool is_label_valid(CLabels *lab) const
Definition: Machine.h:344
The class Features is the base class of all feature objects.
Definition: Features.h:68
virtual CBinaryLabels * apply_locked_binary(SGVector< index_t > indices)
Definition: Machine.cpp:246
virtual bool train(CFeatures *data=NULL)
Definition: Machine.cpp:47
Binary Labels for binary classification.
Definition: BinaryLabels.h:37
virtual CMulticlassLabels * apply_multiclass(CFeatures *data=NULL)
Definition: Machine.cpp:228
virtual bool train_require_labels() const
Definition: Machine.h:350
virtual CLatentLabels * apply_locked_latent(SGVector< index_t > indices)
Definition: Machine.cpp:274
virtual void set_labels(CLabels *lab)
Definition: Machine.cpp:73
abstract class for latent labels As latent labels always depends on the given application, this class only defines the API that the user has to implement for latent labels.
Definition: LatentLabels.h:26
bool is_data_locked() const
Definition: Machine.h:292
void set_solver_type(ESolverType st)
Definition: Machine.cpp:105
virtual CLabels * apply(CFeatures *data=NULL)
Definition: Machine.cpp:160

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