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InferenceMethod.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) 2013 Roman Votyakov
8  * Written (W) 2013 Heiko Strathmann
9  * Copyright (C) 2012 Jacob Walker
10  * Copyright (C) 2013 Roman Votyakov
11  */
12 
13 #ifndef CINFERENCEMETHOD_H_
14 #define CINFERENCEMETHOD_H_
15 
16 #include <shogun/lib/config.h>
17 
18 #ifdef HAVE_EIGEN3
19 
20 #include <shogun/base/SGObject.h>
21 #include <shogun/kernel/Kernel.h>
23 #include <shogun/labels/Labels.h>
27 
28 namespace shogun
29 {
30 
33 {
38  INF_EP=40
39 };
40 
50 {
51 public:
54 
63  CInferenceMethod(CKernel* kernel, CFeatures* features,
64  CMeanFunction* mean, CLabels* labels, CLikelihoodModel* model);
65 
66  virtual ~CInferenceMethod();
67 
72  virtual EInferenceType get_inference_type() const { return INF_NONE; }
73 
86 
122  float64_t get_marginal_likelihood_estimate(int32_t num_importance_samples=1,
123  float64_t ridge_size=1e-15);
124 
139  CMap<TParameter*, CSGObject*>* parameters);
140 
151  virtual SGVector<float64_t> get_alpha()=0;
152 
164  virtual SGMatrix<float64_t> get_cholesky()=0;
165 
178 
195 
212 
221  CMap<TParameter*, CSGObject*>* parameters)
222  {
224  }
225 
231  {
232  SGVector<float64_t> result(1);
234  return result;
235  }
236 
242 
247  virtual void set_features(CFeatures* feat)
248  {
249  SG_REF(feat);
251  m_features=feat;
252  }
253 
258  virtual CKernel* get_kernel() { SG_REF(m_kernel); return m_kernel; }
259 
264  virtual void set_kernel(CKernel* kern)
265  {
266  SG_REF(kern);
268  m_kernel=kern;
269  }
270 
275  virtual CMeanFunction* get_mean() { SG_REF(m_mean); return m_mean; }
276 
281  virtual void set_mean(CMeanFunction* m)
282  {
283  SG_REF(m);
284  SG_UNREF(m_mean);
285  m_mean=m;
286  }
287 
292  virtual CLabels* get_labels() { SG_REF(m_labels); return m_labels; }
293 
298  virtual void set_labels(CLabels* lab)
299  {
300  SG_REF(lab);
302  m_labels=lab;
303  }
304 
310 
315  virtual void set_model(CLikelihoodModel* mod)
316  {
317  SG_REF(mod);
318  SG_UNREF(m_model);
319  m_model=mod;
320  }
321 
326  virtual float64_t get_scale() const { return m_scale; }
327 
332  virtual void set_scale(float64_t scale) { m_scale=scale; }
333 
339  virtual bool supports_regression() const { return false; }
340 
346  virtual bool supports_binary() const { return false; }
347 
353  virtual bool supports_multiclass() const { return false; }
354 
356  virtual void update();
357 
358 protected:
360  virtual void check_members() const;
361 
363  virtual void update_alpha()=0;
364 
366  virtual void update_chol()=0;
367 
371  virtual void update_deriv()=0;
372 
374  virtual void update_train_kernel();
375 
384  const TParameter* param)=0;
385 
394  const TParameter* param)=0;
395 
404  const TParameter* param)=0;
405 
414  const TParameter* param)=0;
415 
419  static void* get_derivative_helper(void* p);
420 
421 private:
422  void init();
423 
424 protected:
427 
430 
433 
436 
439 
442 
445 
448 
451 };
452 }
453 #endif /* HAVE_EIGEN3 */
454 #endif /* CINFERENCEMETHOD_H_ */
virtual void set_labels(CLabels *lab)
virtual void set_model(CLikelihoodModel *mod)
virtual float64_t get_negative_log_marginal_likelihood()=0
virtual CFeatures * get_features()
virtual void update_alpha()=0
SGVector< float64_t > m_alpha
The Inference Method base class.
virtual void set_features(CFeatures *feat)
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:35
parameter struct
Definition: Parameter.h:26
virtual CMap< TParameter *, SGVector< float64_t > > * get_gradient(CMap< TParameter *, CSGObject * > *parameters)
An abstract class of the mean function.
Definition: MeanFunction.h:26
virtual SGVector< float64_t > get_derivative_wrt_likelihood_model(const TParameter *param)=0
#define SG_REF(x)
Definition: SGObject.h:53
virtual void set_scale(float64_t scale)
SGMatrix< float64_t > m_L
virtual bool supports_regression() const
double float64_t
Definition: common.h:48
An abstract class that describes a differentiable function used for GradientEvaluation.
virtual CLabels * get_labels()
the class CMap, a map based on the hash-table. w: http://en.wikipedia.org/wiki/Hash_table ...
Definition: SGObject.h:41
virtual SGVector< float64_t > get_derivative_wrt_inference_method(const TParameter *param)=0
virtual void update_train_kernel()
virtual SGVector< float64_t > get_derivative_wrt_kernel(const TParameter *param)=0
virtual CMeanFunction * get_mean()
virtual void set_kernel(CKernel *kern)
virtual float64_t get_scale() const
float64_t get_marginal_likelihood_estimate(int32_t num_importance_samples=1, float64_t ridge_size=1e-15)
#define SG_UNREF(x)
Definition: SGObject.h:54
virtual SGVector< float64_t > get_derivative_wrt_mean(const TParameter *param)=0
virtual void set_mean(CMeanFunction *m)
virtual SGMatrix< float64_t > get_posterior_covariance()=0
virtual CKernel * get_kernel()
The class Features is the base class of all feature objects.
Definition: Features.h:62
virtual void update_chol()=0
virtual bool supports_multiclass() const
virtual void check_members() const
virtual SGVector< float64_t > get_posterior_mean()=0
virtual EInferenceType get_inference_type() const
The Kernel base class.
Definition: Kernel.h:150
virtual bool supports_binary() const
virtual SGVector< float64_t > get_value()
virtual SGVector< float64_t > get_alpha()=0
virtual SGVector< float64_t > get_diagonal_vector()=0
virtual void update_deriv()=0
virtual SGMatrix< float64_t > get_cholesky()=0
The Likelihood model base class.
SGMatrix< float64_t > m_ktrtr
virtual CMap< TParameter *, SGVector< float64_t > > * get_negative_log_marginal_likelihood_derivatives(CMap< TParameter *, CSGObject * > *parameters)
CLikelihoodModel * get_model()
CLikelihoodModel * m_model
static void * get_derivative_helper(void *p)

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