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GaussianProcessMachine.h
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1 /*
2  * Copyright (c) The Shogun Machine Learning Toolbox
3  * Written (w) 2014 Wu Lin
4  * Written (W) 2013 Roman Votyakov
5  * All rights reserved.
6  *
7  * Redistribution and use in source and binary forms, with or without
8  * modification, are permitted provided that the following conditions are met:
9  *
10  * 1. Redistributions of source code must retain the above copyright notice, this
11  * list of conditions and the following disclaimer.
12  * 2. Redistributions in binary form must reproduce the above copyright notice,
13  * this list of conditions and the following disclaimer in the documentation
14  * and/or other materials provided with the distribution.
15  *
16  * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
17  * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
18  * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
19  * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
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21  * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
22  * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
23  * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
24  * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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29  * either expressed or implied, of the Shogun Development Team.
30  *
31  * Code adapted from
32  * Gaussian Process Machine Learning Toolbox
33  * http://www.gaussianprocess.org/gpml/code/matlab/doc/
34  * and
35  * https://gist.github.com/yorkerlin/8a36e8f9b298aa0246a4
36  */
37 
38 #ifndef _GAUSSIANPROCESSMACHINE_H_
39 #define _GAUSSIANPROCESSMACHINE_H_
40 
41 #include <shogun/lib/config.h>
42 #include <shogun/machine/Machine.h>
44 
45 #ifdef HAVE_EIGEN3
46 
47 namespace shogun
48 {
49 
62 {
63 public:
66 
72 
73  virtual ~CGaussianProcessMachine();
74 
79  virtual const char* get_name() const { return "GaussianProcessMachine"; }
80 
90 
100 
106  {
107  SG_REF(m_method);
108  return m_method;
109  }
110 
116  {
117  SG_REF(method);
119  m_method=method;
120  }
121 
126  virtual void set_labels(CLabels* lab)
127  {
129  m_method->set_labels(lab);
130  }
131 
137  virtual void store_model_features() { }
138 
139 private:
140  void init();
141 
142 protected:
145 };
146 }
147 #endif /* HAVE_EIGEN3 */
148 #endif /* _GAUSSIANPROCESSMACHINE_H_ */
virtual void set_labels(CLabels *lab)
The Inference Method base class.
A base class for Gaussian Processes.
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
SGVector< float64_t > get_posterior_variances(CFeatures *data)
SGVector< float64_t > get_posterior_means(CFeatures *data)
void set_inference_method(CInferenceMethod *method)
#define SG_REF(x)
Definition: SGObject.h:51
virtual void set_labels(CLabels *lab)
A generic learning machine interface.
Definition: Machine.h:143
CInferenceMethod * get_inference_method() const
#define SG_UNREF(x)
Definition: SGObject.h:52
all of classes and functions are contained in the shogun namespace
Definition: class_list.h:18
The class Features is the base class of all feature objects.
Definition: Features.h:68
virtual void set_labels(CLabels *lab)
Definition: Machine.cpp:65
virtual const char * get_name() const

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