/*

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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.
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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
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  * 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
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*/

#ifndef __OPENCV_ADAS_WALDBOOST_HPP__
#define __OPENCV_ADAS_WALDBOOST_HPP__

namespace cv
{
namespace adas
{

class Stump
{
public:
    /* 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}

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

    /* Predict object class given

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

    Returns object class from {-1, +1}
    */
    int predict(int value);

private:
    /* Stump decision threshold */
    int threshold_;
    /* Stump polarity, can be from {-1, +1} */
    int polarity_;
    /* Stump decision rule:
        h(value) = polarity * sign(value - threshold)
    */
};

/* Save Stump to FileStorage */
FileStorage& operator<< (FileStorage& out, const Stump& classifier);
/* Load Stump from FileStorage */
FileStorage& operator>> (FileStorage& in, Stump& classifier);

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

    /* 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_;
    /* Weight for stumps in cascade linear combination */
    std::vector<float> stump_weights_;
    /* Rejection thresholds for linear combination at every stump evaluation */
    std::vector<float> thresholds_;
};

/* Save WaldBoost to FileStorage */
FileStorage& operator<< (FileStorage& out, const WaldBoost& classifier);
/* Load WaldBoost from FileStorage */
FileStorage& operator>> (FileStorage& in, WaldBoost& classifier);

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

#endif /* __OPENCV_ADAS_WALDBOOST_HPP__ */
