SHOGUN  4.1.0
GaussianProcessRegression.h
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1 /*
2  * This program is free software; you can redistribute it and/or modify
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  * Copyright (C) 2012 Jacob Walker
9  * Copyright (C) 2013 Roman Votyakov
10  */
11
12 #ifndef _GAUSSIANPROCESSREGRESSION_H_
13 #define _GAUSSIANPROCESSREGRESSION_H_
14
15 #include <shogun/lib/config.h>
16
17 #ifdef HAVE_EIGEN3
18
22 #include <shogun/labels/Labels.h>
23
24 namespace shogun
25 {
26
27 class CInferenceMethod;
28 class CFeatures;
29 class CLabels;
30
35 {
36 public:
39
42
48
50
56  virtual CRegressionLabels* apply_regression(CFeatures* data=NULL);
57
63
69
75  {
77  }
78
83  virtual const char* get_name() const { return "GaussianProcessRegression"; }
84
85 protected:
92  virtual bool train_machine(CFeatures* data=NULL);
93
100  virtual bool is_label_valid(CLabels *lab) const
101  {
102  return lab->get_label_type()==LT_REGRESSION;
103  }
104 };
105 }
106 #endif /* HAVE_EIGEN3 */
107 #endif /* _GAUSSIANPROCESSREGRESSION_H_ */
EMachineType
Definition: Machine.h:33
virtual ELabelType get_label_type() const =0
Real Labels are real-valued labels.
The Inference Method base class.
Class GaussianProcessRegression implements regression based on Gaussian Processes.
A base class for Gaussian Processes.
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
real valued labels (e.g. for regression, classifier outputs)
Definition: LabelTypes.h:22
virtual bool train_machine(CFeatures *data=NULL)
virtual bool is_label_valid(CLabels *lab) const
SGVector< float64_t > get_variance_vector(CFeatures *data)
virtual const char * get_name() const
SGVector< float64_t > get_mean_vector(CFeatures *data)
all of classes and functions are contained in the shogun namespace
Definition: class_list.h:18
virtual CRegressionLabels * apply_regression(CFeatures *data=NULL)
The class Features is the base class of all feature objects.
Definition: Features.h:68

SHOGUN Machine Learning Toolbox - Documentation