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Marina Noskova
9 years ago
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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// Copyright (C) 2014, Itseez Inc, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#ifndef __OPENCV_ML_SVMSGD_HPP__ |
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#define __OPENCV_ML_SVMSGD_HPP__ |
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#ifdef __cplusplus |
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#include "opencv2/ml.hpp" |
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namespace cv |
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{ |
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namespace ml |
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{ |
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/****************************************************************************************\
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* Stochastic Gradient Descent SVM Classifier * |
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\****************************************************************************************/ |
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/*!
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@brief Stochastic Gradient Descent SVM classifier |
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SVMSGD provides a fast and easy-to-use implementation of the SVM classifier using the Stochastic Gradient Descent approach, as presented in @cite bottou2010large. |
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The gradient descent show amazing performance for large-scale problems, reducing the computing time. This allows a fast and reliable online update of the classifier for each new feature which |
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is fundamental when dealing with variations of data over time (like weather and illumination changes in videosurveillance, for example). |
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First, create the SVMSGD object. To enable the online update, a value for updateFrequency should be defined. |
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Then the SVM model can be trained using the train features and the correspondent labels. |
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After that, the label of a new feature vector can be predicted using the predict function. If the updateFrequency was defined in the constructor, the predict function will update the weights automatically. |
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@code |
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// Initialize object
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SVMSGD SvmSgd; |
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// Train the Stochastic Gradient Descent SVM
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SvmSgd.train(trainFeatures, labels); |
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// Predict label for the new feature vector (1xM)
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predictedLabel = SvmSgd.predict(newFeatureVector); |
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@endcode |
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*/ |
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class CV_EXPORTS_W SVMSGD : public cv::ml::StatModel |
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{ |
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public: |
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enum SvmsgdType |
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{ |
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ILLEGAL_VALUE, |
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SGD, //Stochastic Gradient Descent
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ASGD //Average Stochastic Gradient Descent
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}; |
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/**
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* @return the weights of the trained model. |
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*/ |
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CV_WRAP virtual Mat getWeights() = 0; |
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CV_WRAP virtual float getShift() = 0; |
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CV_WRAP static Ptr<SVMSGD> create();
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CV_WRAP virtual void setOptimalParameters(int type = ASGD) = 0; |
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CV_WRAP virtual int getType() const = 0; |
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CV_WRAP virtual void setType(int type) = 0; |
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CV_WRAP virtual float getLambda() const = 0; |
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CV_WRAP virtual void setLambda(float lambda) = 0; |
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CV_WRAP virtual float getGamma0() const = 0; |
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CV_WRAP virtual void setGamma0(float gamma0) = 0; |
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CV_WRAP virtual float getC() const = 0; |
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CV_WRAP virtual void setC(float c) = 0; |
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CV_WRAP virtual cv::TermCriteria getTermCriteria() const = 0; |
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CV_WRAP virtual void setTermCriteria(const cv::TermCriteria &val) = 0; |
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}; |
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} //ml
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} //cv
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#endif // __clpusplus
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#endif // __OPENCV_ML_SVMSGD_HPP
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