SparseInferenceBase.h

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/*
 * Copyright (c) The Shogun Machine Learning Toolbox
 * Written (W) 2015 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
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 */

#ifndef CSPARSEINFERENCEBASE_H
#define CSPARSEINFERENCEBASE_H

#include <shogun/lib/config.h>

#ifdef HAVE_EIGEN3

#include <shogun/machine/gp/InferenceMethod.h>
#include <shogun/features/DenseFeatures.h>

namespace shogun
{

class CSparseInferenceBase: public CInferenceMethod
{
public:
    CSparseInferenceBase();

    CSparseInferenceBase(CKernel* kernel, CFeatures* features,
            CMeanFunction* mean, CLabels* labels, CLikelihoodModel* model,
            CFeatures* inducing_features);

    virtual ~CSparseInferenceBase();

    virtual EInferenceType get_inference_type() const { return INF_SPARSE; }

    virtual const char* get_name() const { return "SparseBaseInferenceMethod"; }

    virtual void set_inducing_features(CFeatures* feat)
    {
        REQUIRE(feat,"Input inducing features must be not empty\n");
        CDotFeatures *lat_type=dynamic_cast<CDotFeatures *>(feat);
        REQUIRE(lat_type, "Inducing features (%s) must be"
            " DotFeatures or one of its subclasses\n", feat->get_name());
        m_inducing_features=lat_type->get_computed_dot_feature_matrix();
    }

    virtual CFeatures* get_inducing_features()
    {
        SGMatrix<float64_t> out(m_inducing_features.matrix,
            m_inducing_features.num_rows,m_inducing_features.num_cols,false);
        CFeatures* inducing_features=new CDenseFeatures<float64_t>(out);
        SG_REF(inducing_features);
        return inducing_features;
    }

    virtual SGVector<float64_t> get_alpha();

    virtual SGMatrix<float64_t> get_cholesky();

    virtual void update()=0;

    virtual void set_inducing_noise(float64_t noise);

    virtual float64_t get_inducing_noise();

    virtual SGVector<float64_t> get_derivative_wrt_inducing_features(const TParameter* param)=0;

    virtual SGVector<float64_t> get_posterior_mean()=0;

    virtual SGMatrix<float64_t> get_posterior_covariance()=0;

protected:
    virtual void convert_features();

    virtual void check_features();

    virtual void check_members() const;

    virtual void update_train_kernel();

    virtual SGVector<float64_t> get_derivative_wrt_inference_method(
            const TParameter* param)=0;

    virtual SGVector<float64_t> get_derivative_wrt_likelihood_model(
            const TParameter* param)=0;

    virtual SGVector<float64_t> get_derivative_wrt_kernel(
            const TParameter* param)=0;

    virtual SGVector<float64_t> get_derivative_wrt_mean(
            const TParameter* param)=0;

    virtual SGVector<float64_t> get_derivative_wrt_inducing_noise(
            const TParameter* param)=0;

    SGMatrix<float64_t> m_inducing_features;

    float64_t m_log_ind_noise;

    SGMatrix<float64_t> m_kuu;

    SGMatrix<float64_t> m_ktru;

    SGMatrix<float64_t> m_Sigma;

    SGVector<float64_t> m_mu;

    SGVector<float64_t> m_ktrtr_diag;
private:
    void init();
};
}
#endif /* HAVE_EIGEN3 */
#endif /* CSPARSEINFERENCEBASE_H */