text: /*
   * Copyright (c) The Shogun Machine Learning Toolbox
   * Written (w) 2014 Wu Lin
   * Written (W) 2013 Roman Votyakov
   * All rights reserved.
   *
   * Redistribution and use in source and binary forms, with or without
   * modification, are permitted provided that the following conditions are met:
   *
   * 1. Redistributions of source code must retain the above copyright notice, this
   *    list of conditions and the following disclaimer.
   * 2. Redistributions in binary form must reproduce the above copyright notice,
   *    this list of conditions and the following disclaimer in the documentation
   *    and/or other materials provided with the distribution.
   *
   * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
   * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
   * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
   * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
   * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
   * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
   * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
   * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
   * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
   * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
   *
   * The views and conclusions contained in the software and documentation are those
   * of the authors and should not be interpreted as representing official policies,
   * either expressed or implied, of the Shogun Development Team.
   *
   * Code adapted from
   * Gaussian Process Machine Learning Toolbox
   * http://www.gaussianprocess.org/gpml/code/matlab/doc/
   * and
   * https://gist.github.com/yorkerlin/8a36e8f9b298aa0246a4
   */
  
  #ifndef _GAUSSIANPROCESSMACHINE_H_
  #define _GAUSSIANPROCESSMACHINE_H_
  
  #include <shogun/lib/config.h>
  #include <shogun/machine/Machine.h>
  #include <shogun/machine/gp/Inference.h>
  
  
  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(CInference* 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
  	 */
  	CInference* get_inference_method() const
  	{
  		SG_REF(m_method);
  		return m_method;
  	}
  
  	/** set inference method
  	 *
  	 * @param method inference method
  	 */
  	void set_inference_method(CInference* 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 */
  	CInference* m_method;
  };
  }
  #endif /* _GAUSSIANPROCESSMACHINE_H_ */
  