Increasing the dimension of features space in the SVMSGD::train function.
This commit is contained in:
Marina Noskova
@@ -1496,6 +1496,121 @@ public:
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CV_WRAP static Ptr<LogisticRegression> create();
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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.
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First, create the SVMSGD object. Set parametrs of model (type, lambda, gamma0, c) using the functions setType, setLambda, setGamma0 and setC or the function setOptimalParametrs.
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Recommended model type is ASGD.
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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.
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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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/** SVMSGD type.
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ASGD is often the preferable choice. */
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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 (decision function f(x) = weights * x + shift).
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*/
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CV_WRAP virtual Mat getWeights() = 0;
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/**
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* @return the shift of the trained model (decision function f(x) = weights * x + shift).
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*/
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CV_WRAP virtual float getShift() = 0;
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/** Creates empty model.
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Use StatModel::train to train the model. Since %SVMSGD has several parameters, you may want to
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find the best parameters for your problem or use setOptimalParameters() to set some default parameters.
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*/
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CV_WRAP static Ptr<SVMSGD> create();
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/** Function sets optimal parameters values for chosen SVM SGD model.
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* If chosen type is ASGD, function sets the following values for parameters of model:
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* lambda = 0.00001;
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* gamma0 = 0.05;
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* c = 0.75;
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* termCrit.maxCount = 100000;
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* termCrit.epsilon = 0.00001;
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*
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* If SGD:
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* lambda = 0.0001;
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* gamma0 = 0.05;
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* c = 1;
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* termCrit.maxCount = 100000;
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* termCrit.epsilon = 0.00001;
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* @param type is the type of SVMSGD classifier. Legal values are SvmsgdType::SGD and SvmsgdType::ASGD.
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* Recommended value is SvmsgdType::ASGD (by default).
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*/
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CV_WRAP virtual void setOptimalParameters(int type = ASGD) = 0;
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/** %Algorithm type, one of SVMSGD::SvmsgdType. */
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/** @see setAlgorithmType */
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CV_WRAP virtual int getType() const = 0;
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/** @copybrief getAlgorithmType @see getAlgorithmType */
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CV_WRAP virtual void setType(int type) = 0;
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/** Parameter _Lambda_ of a %SVMSGD optimization problem. Default value is 0. */
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/** @see setLambda */
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CV_WRAP virtual float getLambda() const = 0;
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/** @copybrief getLambda @see getLambda */
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CV_WRAP virtual void setLambda(float lambda) = 0;
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/** Parameter _Gamma0_ of a %SVMSGD optimization problem. Default value is 0. */
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/** @see setGamma0 */
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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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/** Parameter _C_ of a %SVMSGD optimization problem. Default value is 0. */
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/** @see setC */
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CV_WRAP virtual float getC() const = 0;
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/** @copybrief getC @see getC */
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CV_WRAP virtual void setC(float c) = 0;
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/** @brief Termination criteria of the training algorithm.
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You can specify the maximum number of iterations (maxCount) and/or how much the error could
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change between the iterations to make the algorithm continue (epsilon).*/
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/** @see setTermCriteria */
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CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
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/** @copybrief getTermCriteria @see getTermCriteria */
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CV_WRAP virtual void setTermCriteria(const cv::TermCriteria &val) = 0;
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};
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/****************************************************************************************\
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* Auxilary functions declarations *
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\****************************************************************************************/
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@@ -1,134 +0,0 @@
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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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// 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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