/*

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license. If you do not agree to this license, do not download, install,
copy or use the software.


                          License Agreement
               For Open Source Computer Vision Library
                       (3-clause BSD License)

Copyright (C) 2013, OpenCV Foundation, all rights reserved.
Third party copyrights are property of their respective owners.

Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:

  * Redistributions of source code must retain the above copyright notice,
    this list of conditions and the following disclaimer.

  * 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.

  * Neither the names of the copyright holders nor the names of the contributors
    may be used to endorse or promote products derived from this software
    without specific prior written permission.

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
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*/

#ifndef __OPENCV_ADAS_WALDBOOST_HPP__
#define __OPENCV_ADAS_WALDBOOST_HPP__

#include <opencv2/core.hpp>

#include "acffeature.hpp"

namespace cv
{
namespace adas
{

class Stump
{
public:

    /* Initialize zero stump */
    Stump(): threshold_(0), polarity_(1), pos_value_(1), neg_value_(-1) {}

    /* Initialize stump with given threshold, polarity
        and classification values */
    Stump(int threshold, int polarity, float pos_value, float neg_value):
        threshold_(threshold), polarity_(polarity),
        pos_value_(pos_value), neg_value_(neg_value) {}

    /* Train stump for given data

        data — matrix of feature values, size M x N, one feature per row

        labels — matrix of sample class labels, size 1 x N. Labels can be from
            {-1, +1}

        weights — matrix of sample weights, size 1 x N

    Returns chosen feature index. Feature enumeration starts from 0
    */
    int train(const Mat& data, const Mat& labels, const Mat& weights);

    /* Predict object class given

        value — feature value. Feature must be the same as was chosen
        during training stump

    Returns real value, sign(value) means class
    */
    float predict(int value);

private:
    /* Stump decision threshold */
    int threshold_;
    /* Stump polarity, can be from {-1, +1} */
    int polarity_;
    /* Classification values for positive and negative classes  */
    float pos_value_, neg_value_;
};

struct WaldBoostParams
{
    int weak_count;
    float alpha;
};

class WaldBoost
{
public:
    /* Initialize WaldBoost cascade with default of specified parameters */
    WaldBoost(const WaldBoostParams& params);

    /* Train WaldBoost cascade for given data

        data — matrix of feature values, size M x N, one feature per row

        labels — matrix of sample class labels, size 1 x N. Labels can be from
            {-1, +1}

    Returns feature indices chosen for cascade.
    Feature enumeration starts from 0
    */
    std::vector<int> train(const Mat& data,
                           const Mat& labels);

    /* Predict object class given object that can compute object features

       feature_evaluator — object that can compute features by demand

    Returns confidence_value — measure of confidense that object
    is from class +1
    */
    float predict(const Ptr<ACFFeatureEvaluator>& feature_evaluator);

private:
    /* Parameters for cascade training */
    WaldBoostParams params_;
    /* Stumps in cascade */
    std::vector<Stump> stumps_;
    /* Rejection thresholds for linear combination at every stump evaluation */
    std::vector<float> thresholds_;
};

} /* namespace adas */
} /* namespace cv */

#endif /* __OPENCV_ADAS_WALDBOOST_HPP__ */
