/usr/include/shogun/regression/GaussianProcessRegression.h is in libshogun-dev 3.2.0-7.3build4.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | /*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2013 Roman Votyakov
* Copyright (C) 2012 Jacob Walker
* Copyright (C) 2013 Roman Votyakov
*/
#ifndef _GAUSSIANPROCESSREGRESSION_H_
#define _GAUSSIANPROCESSREGRESSION_H_
#include <shogun/lib/config.h>
#ifdef HAVE_EIGEN3
#include <shogun/machine/GaussianProcessMachine.h>
#include <shogun/machine/gp/InferenceMethod.h>
#include <shogun/features/Features.h>
#include <shogun/labels/Labels.h>
namespace shogun
{
class CInferenceMethod;
class CFeatures;
class CLabels;
/** @brief Class GaussianProcessRegression implements regression based on
* Gaussian Processes.
*/
class CGaussianProcessRegression : public CGaussianProcessMachine
{
public:
/** problem type */
MACHINE_PROBLEM_TYPE(PT_REGRESSION);
/** default constructor */
CGaussianProcessRegression();
/** constructor
*
* @param method chosen inference method
*/
CGaussianProcessRegression(CInferenceMethod* method);
virtual ~CGaussianProcessRegression();
/** apply regression to data
*
* @param data (test)data to be classified
* @return classified labels
*/
virtual CRegressionLabels* apply_regression(CFeatures* data=NULL);
/** get predicted mean vector
*
* @return predicted mean vector
*/
SGVector<float64_t> get_mean_vector(CFeatures* data);
/** get variance vector
*
* @return variance vector
*/
SGVector<float64_t> get_variance_vector(CFeatures* data);
/** get classifier type
*
* @return classifier type GaussianProcessRegression
*/
virtual EMachineType get_classifier_type()
{
return CT_GAUSSIANPROCESSREGRESSION;
}
/** return name of the regression object
*
* @return name GaussianProcessRegression
*/
virtual const char* get_name() const { return "GaussianProcessRegression"; }
protected:
/** train regression
*
* @param data training data
*
* @return whether training was successful
*/
virtual bool train_machine(CFeatures* data=NULL);
/** check whether training labels are valid for regression
*
* @param lab training labels
*
* @return whether training labels are valid for regression
*/
virtual bool is_label_valid(CLabels *lab) const
{
return lab->get_label_type()==LT_REGRESSION;
}
};
}
#endif /* HAVE_EIGEN3 */
#endif /* _GAUSSIANPROCESSREGRESSION_H_ */
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