xmmGmmSingleClass.hpp

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/*
 * xmmGmmSingleClass.hpp
 *
 * Gaussian Mixture Model Definition for a Single 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 xmmGmmSingleClass_hpp
#define xmmGmmSingleClass_hpp

#include "../../core/model/xmmModelResults.hpp"
#include "../../core/model/xmmModelSingleClass.hpp"
#include "xmmGmmParameters.hpp"
#include <memory>

namespace xmm {
const std::vector<float> null_vector_float;

class SingleClassGMM : public SingleClassProbabilisticModel {
  public:
    template <typename SingleClassModel, typename ModelType>
    friend class Model;
    friend class SingleClassHMM;
    friend class HierarchicalHMM;

    SingleClassGMM(std::shared_ptr<SharedParameters> p = NULL);

    SingleClassGMM(SingleClassGMM const& src);

    explicit SingleClassGMM(std::shared_ptr<SharedParameters> p,
                            Json::Value const& root);

    SingleClassGMM& operator=(SingleClassGMM const& src);


    void reset();

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



    Json::Value toJson() const;

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


    //
    //        /**
    //         @brief Convert to bimodal GMM 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 GMM 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 GMM from the current model considering only the target
    //         columns
    //         */
    //        GMM 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
    //         */
    //        GMM 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 GMM of the output modality from the current
    //         bimodal model
    //         */
    //        GMM 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 GMM  that swaps the input and output modalities
    //         */
    //        GMM extract_inverse_model() const;

    ClassParameters<GMM> parameters;

    ClassResults<GMM> results;

    std::vector<GaussianDistribution> components;

    std::vector<float> mixture_coeffs;

    std::vector<double> beta;

  protected:
    void allocate();

    double obsProb(const float* observation, int mixtureComponent = -1) const;

    double obsProb_input(const float* observation_input,
                         int mixtureComponent = -1) const;

    double obsProb_bimodal(const float* observation_input,
                           const float* observation_output,
                           int mixtureComponent = -1) const;

    void emAlgorithmInit(TrainingSet* trainingSet);

    double emAlgorithmUpdate(TrainingSet* trainingSet);

    void initParametersToDefault(std::vector<float> const& dataStddev);

    void initMeansWithKMeans(TrainingSet* trainingSet);

    void initCovariances_fullyObserved(TrainingSet* trainingSet);

    void normalizeMixtureCoeffs();

    void addCovarianceOffset();

    void updateInverseCovariances();

    double likelihood(
        std::vector<float> const& observation,
        std::vector<float> const& observation_output = null_vector_float);

    void regression(std::vector<float> const& observation_input);

    void updateResults();

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

#endif