SVMSGD class added
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@ -415,16 +415,6 @@
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pages = {2548--2555},
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organization = {IEEE}
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}
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@ARTICLE{Louhichi07,
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author = {Louhichi, H. and Fournel, T. and Lavest, J. M. and Ben Aissia, H.},
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title = {Self-calibration of Scheimpflug cameras: an easy protocol},
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year = {2007},
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pages = {2616–2622},
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journal = {Meas. Sci. Technol.},
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volume = {18},
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number = {8},
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publisher = {IOP Publishing Ltd}
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}
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@ARTICLE{LibSVM,
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author = {Chang, Chih-Chung and Lin, Chih-Jen},
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title = {LIBSVM: a library for support vector machines},
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@ -874,3 +864,11 @@
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year={2007},
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organization={IEEE}
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}
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@incollection{bottou2010large,
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title={Large-scale machine learning with stochastic gradient descent},
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author={Bottou, L{\'e}on},
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booktitle={Proceedings of COMPSTAT'2010},
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pages={177--186},
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year={2010},
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publisher={Springer}
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}
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@ -1513,6 +1513,127 @@ CV_EXPORTS void randMVNormal( InputArray mean, InputArray cov, int nsamples, Out
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CV_EXPORTS void createConcentricSpheresTestSet( int nsamples, int nfeatures, int nclasses,
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OutputArray samples, OutputArray responses);
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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 {
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public:
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/** @brief SGDSVM constructor.
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@param lambda regularization
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@param learnRate learning rate
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@param nIterations number of training iterations
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*/
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SVMSGD(float lambda = 0.000001, float learnRate = 2, uint nIterations = 100000);
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/** @brief SGDSVM constructor.
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@param updateFrequency online update frequency
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@param learnRateDecay learn rate decay over time: learnRate = learnRate * learnDecay
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@param lambda regularization
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@param learnRate learning rate
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@param nIterations number of training iterations
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*/
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SVMSGD(uint updateFrequency, float learnRateDecay = 1, float lambda = 0.000001, float learnRate = 2, uint nIterations = 100000);
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virtual ~SVMSGD();
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virtual SVMSGD* clone() const;
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/** @brief Train the SGDSVM classifier.
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The function trains the SGDSVM classifier using the train features and the correspondent labels (-1 or 1).
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@param trainFeatures features used for training. Each row is a new sample.
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@param labels mat (size Nx1 with N = number of features) with the label of each training feature.
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*/
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virtual void train(cv::Mat trainFeatures, cv::Mat labels);
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/** @brief Predict the label of a new feature vector.
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The function predicts and returns the label of a new feature vector, using the previously trained SVM model.
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@param newFeature new feature vector used for prediction
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*/
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virtual float predict(cv::Mat newFeature);
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/** @brief Returns the weights of the trained model.
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*/
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virtual std::vector<float> getWeights(){ return _weights; };
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/** @brief Sets the weights of the trained model.
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@param weights weights used to predict the label of a new feature vector.
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*/
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virtual void setWeights(std::vector<float> weights){ _weights = weights; };
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private:
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void updateWeights();
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void generateRandomIndex();
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float calcInnerProduct(float *rowDataPointer);
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void updateWeights(float innerProduct, float *rowDataPointer, int label);
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// Vector with SVM weights
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std::vector<float> _weights;
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// Random index generation
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long long int _randomNumber;
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unsigned int _randomIndex;
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// Number of features and samples
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unsigned int _nFeatures;
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unsigned int _nTrainSamples;
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// Parameters for learning
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float _lambda; //regularization
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float _learnRate; //learning rate
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unsigned int _nIterations; //number of training iterations
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// Vars to control the features slider matrix
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bool _onlineUpdate;
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bool _initPredict;
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uint _slidingWindowSize;
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uint _predictSlidingWindowSize;
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float* _labelSlider;
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float _learnRateDecay;
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// Mat with features slider and correspondent counter
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unsigned int _sliderCounter;
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cv::Mat _featuresSlider;
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};
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//! @} ml
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}
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201
modules/ml/src/svmsgd.cpp
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201
modules/ml/src/svmsgd.cpp
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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) 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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#include "precomp.hpp"
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/****************************************************************************************\
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* Stochastic Gradient Descent SVM Classifier *
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\****************************************************************************************/
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namespace cv {
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namespace ml {
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SVMSGD::SVMSGD(float lambda, float learnRate, uint nIterations){
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// Initialize with random seed
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_randomNumber = 1;
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// Initialize constants
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_slidingWindowSize = 0;
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_nFeatures = 0;
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_predictSlidingWindowSize = 1;
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// Initialize sliderCounter at index 0
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_sliderCounter = 0;
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// Parameters for learning
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_lambda = lambda; // regularization
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_learnRate = learnRate; // learning rate (ideally should be large at beginning and decay each iteration)
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_nIterations = nIterations; // number of training iterations
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// True only in the first predict iteration
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_initPredict = true;
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// Online update flag
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_onlineUpdate = false;
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}
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SVMSGD::SVMSGD(uint updateFrequency, float learnRateDecay, float lambda, float learnRate, uint nIterations){
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// Initialize with random seed
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_randomNumber = 1;
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// Initialize constants
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_slidingWindowSize = 0;
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_nFeatures = 0;
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_predictSlidingWindowSize = updateFrequency;
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// Initialize sliderCounter at index 0
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_sliderCounter = 0;
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// Parameters for learning
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_lambda = lambda; // regularization
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_learnRate = learnRate; // learning rate (ideally should be large at beginning and decay each iteration)
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_nIterations = nIterations; // number of training iterations
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// True only in the first predict iteration
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_initPredict = true;
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// Online update flag
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_onlineUpdate = true;
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// Learn rate decay: _learnRate = _learnRate * _learnDecay
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_learnRateDecay = learnRateDecay;
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}
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SVMSGD::~SVMSGD(){
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}
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SVMSGD* SVMSGD::clone() const{
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return new SVMSGD(*this);
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}
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void SVMSGD::train(cv::Mat trainFeatures, cv::Mat labels){
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// Initialize _nFeatures
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_slidingWindowSize = trainFeatures.rows;
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_nFeatures = trainFeatures.cols;
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float innerProduct;
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// Initialize weights vector with zeros
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if (_weights.size()==0){
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_weights.reserve(_nFeatures);
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for (uint feat = 0; feat < _nFeatures; ++feat){
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_weights.push_back(0.0);
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}
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}
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// Stochastic gradient descent SVM
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for (uint iter = 0; iter < _nIterations; ++iter){
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generateRandomIndex();
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innerProduct = calcInnerProduct(trainFeatures.ptr<float>(_randomIndex));
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int label = (labels.at<int>(_randomIndex,0) > 0) ? 1 : -1; // ensure that labels are -1 or 1
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updateWeights(innerProduct, trainFeatures.ptr<float>(_randomIndex), label );
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}
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}
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float SVMSGD::predict(cv::Mat newFeature){
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float innerProduct;
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if (_initPredict){
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_nFeatures = newFeature.cols;
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_slidingWindowSize = _predictSlidingWindowSize;
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_featuresSlider = cv::Mat::zeros(_slidingWindowSize, _nFeatures, CV_32F);
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_initPredict = false;
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_labelSlider = new float[_predictSlidingWindowSize]();
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_learnRate = _learnRate * _learnRateDecay;
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}
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innerProduct = calcInnerProduct(newFeature.ptr<float>(0));
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// Resultant label (-1 or 1)
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int label = (innerProduct>=0) ? 1 : -1;
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if (_onlineUpdate){
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// Update the featuresSlider with newFeature and _labelSlider with label
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newFeature.row(0).copyTo(_featuresSlider.row(_sliderCounter));
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_labelSlider[_sliderCounter] = float(label);
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// Update weights with a random index
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if (_sliderCounter == _slidingWindowSize-1){
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generateRandomIndex();
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updateWeights(innerProduct, _featuresSlider.ptr<float>(_randomIndex), int(_labelSlider[_randomIndex]) );
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}
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// _sliderCounter++ if < _slidingWindowSize
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_sliderCounter = (_sliderCounter == _slidingWindowSize-1) ? 0 : (_sliderCounter+1);
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}
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return float(label);
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}
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void SVMSGD::generateRandomIndex(){
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// Choose random sample, using Mikolov's fast almost-uniform random number
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_randomNumber = _randomNumber * (unsigned long long) 25214903917 + 11;
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_randomIndex = uint(_randomNumber % (unsigned long long) _slidingWindowSize);
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}
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float SVMSGD::calcInnerProduct(float *rowDataPointer){
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float innerProduct = 0;
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for (uint feat = 0; feat < _nFeatures; ++feat){
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innerProduct += _weights[feat] * rowDataPointer[feat];
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}
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return innerProduct;
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}
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void SVMSGD::updateWeights(float innerProduct, float *rowDataPointer, int label){
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if (label * innerProduct > 1) {
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// Not a support vector, only apply weight decay
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for (uint feat = 0; feat < _nFeatures; feat++) {
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_weights[feat] -= _learnRate * _lambda * _weights[feat];
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}
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} else {
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// It's a support vector, add it to the weights
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for (uint feat = 0; feat < _nFeatures; feat++) {
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_weights[feat] -= _learnRate * (_lambda * _weights[feat] - label * rowDataPointer[feat]);
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}
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}
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}
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}
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}
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