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GaussianProcessClassification.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
20  * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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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28  * of the authors and should not be interpreted as representing official policies,
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 _GAUSSIANPROCESSCLASSIFICATION_H_
39 #define _GAUSSIANPROCESSCLASSIFICATION_H_
40 
41 #include <shogun/lib/config.h>
42 
43 #ifdef HAVE_EIGEN3
44 
46 #include <shogun/machine/Machine.h>
47 
48 namespace shogun
49 {
50 
55 {
56 public:
59 
62 
68 
70 
77  virtual CBinaryLabels* apply_binary(CFeatures* data=NULL);
78 
86 
94 
102 
108  {
110  }
111 
116  virtual const char* get_name() const
117  {
118  return "GaussianProcessClassification";
119  }
126  virtual CMulticlassLabels* apply_multiclass(CFeatures* data=NULL);
127 
128 protected:
135  virtual bool train_machine(CFeatures* data=NULL);
136 
137 };
138 }
139 #endif /* HAVE_EIGEN3 */
140 #endif /* _GAUSSIANPROCESSCLASSIFICATION_H_ */
EMachineType
Definition: Machine.h:33
SGVector< float64_t > get_variance_vector(CFeatures *data)
The Inference Method base class.
A base class for Gaussian Processes.
Class GaussianProcessClassification implements binary and multiclass classification based on Gaussian...
SGVector< float64_t > get_mean_vector(CFeatures *data)
Multiclass Labels for multi-class classification.
SGVector< float64_t > get_probabilities(CFeatures *data)
virtual CBinaryLabels * apply_binary(CFeatures *data=NULL)
virtual bool train_machine(CFeatures *data=NULL)
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
Binary Labels for binary classification.
Definition: BinaryLabels.h:37
virtual CMulticlassLabels * apply_multiclass(CFeatures *data=NULL)

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