xmmHierarchicalHmm.hpp

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/*
 * hierarchical_hmm.h
 *
 * Hierarchical Hidden Markov Model for Continuous Recognition and Regression
 * (Multi-class)
 *
 * Contact:
 * - Jules Francoise <jules.francoise@ircam.fr>
 *
 * This code has been initially authored by Jules Francoise
 * <http://julesfrancoise.com> during his PhD thesis, supervised by Frederic
 * Bevilacqua <href="http://frederic-bevilacqua.net>, in the Sound Music
 * Movement Interaction team <http://ismm.ircam.fr> of the
 * STMS Lab - IRCAM, CNRS, UPMC (2011-2015).
 *
 * Copyright (C) 2015 UPMC, Ircam-Centre Pompidou.
 *
 * This File is part of XMM.
 *
 * XMM 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.
 *
 * XMM is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with XMM.  If not, see <http://www.gnu.org/licenses/>.
 */

#ifndef xmm_lib_hierarchical_hmm_h
#define xmm_lib_hierarchical_hmm_h

#include "../../core/model/xmmModel.hpp"
#include "../gmm/xmmGmm.hpp"
#include "xmmHmmSingleClass.hpp"

namespace xmm {
class HierarchicalHMM : public Model<SingleClassHMM, HMM> {
  public:
    static const double DEFAULT_EXITTRANSITION() { return 0.1; }

    HierarchicalHMM(bool bimodal = false);

    HierarchicalHMM(HierarchicalHMM const& src);

    explicit HierarchicalHMM(Json::Value const& root);

    HierarchicalHMM& operator=(HierarchicalHMM const& src);


    virtual void removeClass(std::string const& label);

    virtual void clear();



    void addExitPoint(int state, float proba);



    virtual void reset();

    virtual void filter(std::vector<float> const& observation);



    virtual Json::Value toJson() const;

    virtual void fromJson(Json::Value const& root);


    //        /**
    //         @brief Convert to bimodal HierarchicalHMM in place
    //         @param dimension_input dimension of the input modality
    //         @throws runtime_error if the model is already bimodal
    //         @throws out_of_range if the requested input dimension is too
    //         large
    //         */
    //        void makeBimodal(unsigned int dimension_input);
    //
    //        /**
    //         @brief Convert to unimodal HierarchicalHMM in place
    //         @throws runtime_error if the model is already unimodal
    //         */
    //        void makeUnimodal();
    //
    //        /**
    //         @brief extract a submodel with the given columns
    //         @param columns columns indices in the target order
    //         @throws runtime_error if the model is training
    //         @throws out_of_range if the number or indices of the requested
    //         columns exceeds the current dimension
    //         @return a HierarchicalHMM from the current model considering only
    //         the target columns
    //         */
    //        HierarchicalHMM extractSubmodel(std::vector<unsigned int>&
    //        columns)
    //        const;
    //
    //        /**
    //         @brief extract the submodel of the input modality
    //         @throws runtime_error if the model is training or if it is not
    //         bimodal
    //         @return a unimodal GMM of the input modality from the current
    //         bimodal model
    //         */
    //        HierarchicalHMM extractSubmodel_input() const;
    //
    //        /**
    //         @brief extract the submodel of the output modality
    //         @throws runtime_error if the model is training or if it is not
    //         bimodal
    //         @return a unimodal HierarchicalHMM of the output modality from
    //         the current bimodal model
    //         */
    //        HierarchicalHMM extractSubmodel_output() const;
    //
    //        /**
    //         @brief extract the model with reversed input and output
    //         modalities
    //         @throws runtime_error if the model is training or if it is not
    //         bimodal
    //         @return a bimodal HierarchicalHMM  that swaps the input and
    //         output modalities
    //         */
    //        HierarchicalHMM extract_inverse_model() const;

    Results<HMM> results;

    std::vector<double> prior;

    std::vector<double> exit_transition;

    std::vector<std::vector<double>> transition;

  protected:
    virtual void joinTraining();

    void updateTransitionParameters();

    void updatePrior();

    void updateTransition();

    void updateExitProbabilities();

    virtual void addModelForClass(std::string const& label);

    void normalizeTransitions();

    void forward_init(std::vector<float> const& observation);

    void forward_update(std::vector<float> const& observation);

    void likelihoodAlpha(int exitNum,
                         std::vector<double>& likelihoodVector) const;

    void updateResults();

    bool forward_initialized_;

    std::vector<double> frontier_v1_;

    std::vector<double> frontier_v2_;
};
}

#endif