/*
 *  Linear Emotion Emotions1
 *
 *  Author: Andrew Salamon
 *
 *  Copyright (c) 2008 Machine Perception Laboratory
 *  University of California San Diego.
 *
 * 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.
 *    3. The name of the author may not be used to endorse or promote products derived from this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``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 AUTHOR 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.
 *
 */

#ifndef __MPT_EMOTIONS1_H__
#define __MPT_EMOTIONS1_H__

#include <string>
#include <vector>
#include <map>
#include <exception>

#ifdef XCODE
#include <CERTFramework/simplematrix.hpp>
#include <CERTFramework/simplematrix_ser.hpp>
#else
#include "simplematrix.hpp"
#include "simplematrix_ser.hpp"
#endif

#include <boost/multi_array.hpp>
#include <boost/array.hpp>

#include <boost/serialization/nvp.hpp>

namespace mpt
{

/** Gaussian Kernel Regression model class.
 *  This class generates a model that can be used to predict AU baselines based on pose data.
 *  It's a two step process: generate the model based on pose data and 'neutral' AU data,
 *  then use the model to adjust runtime AU data based on the runtime pose data.
 */
class Emotions1
{
public:
    typedef std::map< std::string, Emotions1 > Container;
    static const int auCount = 19;
    static const int emotionCount = 7;

public:
    Emotions1();

	~Emotions1() { }

	void clear(); ///< Clear the weights

    bool readFromFile( const std::string &file );

    std::vector<float> calc( const std::vector<float> &aus );

private:
    Matrix2d weights;

private:
    // for serialization
    friend class boost::serialization::access;
    template<class Archive>
    void serialize(Archive &ar, const unsigned int version)
    {
        using boost::serialization::make_nvp;
        ar & make_nvp( "Gmean", weights );
    }
};

void printMatrix( const Matrix2d &matrix, const std::string &title = std::string("") );

} // end namespace mpt
#endif