/usr/include/shogun/machine/GaussianProcessMachine.h is in libshogun-dev 3.1.1-1.
This file is owned by root:root, with mode 0o644.
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* 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
*/
#ifndef _GAUSSIANPROCESSMACHINE_H_
#define _GAUSSIANPROCESSMACHINE_H_
#include <shogun/lib/config.h>
#include <shogun/machine/Machine.h>
#include <shogun/machine/gp/InferenceMethod.h>
#ifdef HAVE_EIGEN3
namespace shogun
{
/** @brief A base class for Gaussian Processes.
*
* Instead of a distribution over weights, the GP specifies a distribution over
* functions:
*
* \f[
* f(x) \sim \mathcal{GP} (m(x), k(x,x'))
* \f]
*
* where \f$m(x)\f$ - mean function, \f$k(x, x')\f$ - covariance function.
*/
class CGaussianProcessMachine : public CMachine
{
public:
/** default constructor */
CGaussianProcessMachine();
/** constructor
*
* @param method inference method
*/
CGaussianProcessMachine(CInferenceMethod* method);
virtual ~CGaussianProcessMachine();
/** returns name of the machine
*
* @return name GaussianProcessMachine
*/
virtual const char* get_name() const { return "GaussianProcessMachine"; }
/** returns a mean \f$\mu\f$ of a Gaussian distribution
* \f$\mathcal{N}(\mu,\sigma^2)\f$, which is an approximation to the
* posterior marginal \f$p(f_*|X,y,x_*)\f$.
*
* @param data testing features
*
* @return posterior means
*/
SGVector<float64_t> get_posterior_means(CFeatures* data);
/** returns a variance \f$\sigma^2\f$ of a Gaussian distribution
* \f$\mathcal{N}(\mu,\sigma^2)\f$, which is an approximation to the
* posterior marginal \f$p(f_*|X,y,x_*)\f$.
*
* @param data testing features
*
* @return posterior variances
*/
SGVector<float64_t> get_posterior_variances(CFeatures* data);
/** get inference method
*
* @return inference method, which is used by Gaussian process machine
*/
CInferenceMethod* get_inference_method() const
{
SG_REF(m_method);
return m_method;
}
/** set inference method
*
* @param method inference method
*/
void set_inference_method(CInferenceMethod* method)
{
SG_REF(method);
SG_UNREF(m_method);
m_method=method;
}
/** set training labels
*
* @param lab labels to set
*/
virtual void set_labels(CLabels* lab)
{
CMachine::set_labels(lab);
m_method->set_labels(lab);
}
/** Stores feature data of underlying model. After this method has been
* called, it is possible to change the machine's feature data and call
* apply(), which is then performed on the training feature data that is
* part of the machine's model.
*/
virtual void store_model_features() { }
private:
void init();
protected:
/** inference method */
CInferenceMethod* m_method;
};
}
#endif /* HAVE_EIGEN3 */
#endif /* _GAUSSIANPROCESSMACHINE_H_ */
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