Refactored SVMSGD class
This commit is contained in:
Marina Noskova
@@ -45,7 +45,7 @@
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#include "opencv2/ml.hpp"
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#include "opencv2/core/core_c.h"
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#include "opencv2/core/utility.hpp"
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#include "opencv2/ml/svmsgd.hpp"
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#include "opencv2/core/private.hpp"
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#include <assert.h>
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@@ -41,161 +41,430 @@
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//M*/
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#include "precomp.hpp"
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#include "limits"
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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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namespace cv
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{
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namespace ml
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{
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SVMSGD::SVMSGD(float lambda, float learnRate, uint nIterations){
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class SVMSGDImpl : public SVMSGD
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{
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// Initialize with random seed
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_randomNumber = 1;
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public:
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SVMSGDImpl();
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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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virtual ~SVMSGDImpl() {}
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// Initialize sliderCounter at index 0
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_sliderCounter = 0;
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virtual bool train(const Ptr<TrainData>& data, int);
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virtual float predict( InputArray samples, OutputArray results=noArray(), int flags = 0 ) const;
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virtual bool isClassifier() const { return params.svmsgdType == SGD || params.svmsgdType == ASGD; }
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virtual bool isTrained() const;
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virtual void clear();
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virtual void write(FileStorage& fs) const;
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virtual void read(const FileNode& fn);
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virtual Mat getWeights(){ return weights_; }
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virtual float getShift(){ return shift_; }
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virtual int getVarCount() const { return weights_.cols; }
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virtual String getDefaultName() const {return "opencv_ml_svmsgd";}
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virtual void setOptimalParameters(int type = ASGD);
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virtual int getType() const;
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virtual void setType(int type);
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CV_IMPL_PROPERTY(float, Lambda, params.lambda)
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CV_IMPL_PROPERTY(float, Gamma0, params.gamma0)
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CV_IMPL_PROPERTY(float, C, params.c)
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CV_IMPL_PROPERTY_S(cv::TermCriteria, TermCriteria, params.termCrit)
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private:
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void updateWeights(InputArray sample, bool is_first_class, float gamma);
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float calcShift(InputArray trainSamples, InputArray trainResponses) const;
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std::pair<bool,bool> areClassesEmpty(Mat responses);
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void writeParams( FileStorage& fs ) const;
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void readParams( const FileNode& fn );
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static inline bool isFirstClass(float val) { return val > 0; }
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// Vector with SVM weights
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Mat weights_;
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float shift_;
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// Random index generation
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RNG rng_;
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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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struct SVMSGDParams
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{
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float lambda; //regularization
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float gamma0; //learning rate
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float c;
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TermCriteria termCrit;
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SvmsgdType svmsgdType;
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};
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// True only in the first predict iteration
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_initPredict = true;
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SVMSGDParams params;
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};
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// Online update flag
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_onlineUpdate = false;
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Ptr<SVMSGD> SVMSGD::create()
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{
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return makePtr<SVMSGDImpl>();
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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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bool SVMSGDImpl::train(const Ptr<TrainData>& data, int)
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{
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clear();
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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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Mat trainSamples = data->getTrainSamples();
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// Initialize sliderCounter at index 0
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_sliderCounter = 0;
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// Initialize varCount
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int trainSamplesCount_ = trainSamples.rows;
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int varCount = trainSamples.cols;
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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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weights_ = Mat::zeros(1, varCount, CV_32F);
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Mat trainResponses = data->getTrainResponses(); // (trainSamplesCount x 1) matrix
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std::pair<bool,bool> are_empty = areClassesEmpty(trainResponses);
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if ( are_empty.first && are_empty.second )
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{
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weights_.release();
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return false;
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}
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if ( are_empty.first || are_empty.second )
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{
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shift_ = are_empty.first ? -1 : 1;
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return true;
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}
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Mat currentSample;
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float gamma = 0;
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Mat lastWeights = Mat::zeros(1, varCount, CV_32F); //weights vector for calculating terminal criterion
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Mat averageWeights; //average weights vector for ASGD model
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double err = DBL_MAX;
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if (params.svmsgdType == ASGD)
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{
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averageWeights = Mat::zeros(1, varCount, CV_32F);
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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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for (int iter = 0; (iter < params.termCrit.maxCount)&&(err > params.termCrit.epsilon); iter++)
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{
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//generate sample number
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int randomNumber = rng_.uniform(0, trainSamplesCount_);
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float SVMSGD::predict(cv::Mat newFeature){
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float innerProduct;
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currentSample = trainSamples.row(randomNumber);
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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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//update gamma
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gamma = params.gamma0 * std::pow((1 + params.lambda * params.gamma0 * (float)iter), (-params.c));
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innerProduct = calcInnerProduct(newFeature.ptr<float>(0));
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bool is_first_class = isFirstClass(trainResponses.at<float>(randomNumber));
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updateWeights( currentSample, is_first_class, gamma );
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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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//average weights (only for ASGD model)
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if (params.svmsgdType == ASGD)
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{
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averageWeights = ((float)iter/ (1 + (float)iter)) * averageWeights + weights_ / (1 + (float) iter);
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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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err = norm(weights_ - lastWeights);
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weights_.copyTo(lastWeights);
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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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if (params.svmsgdType == ASGD)
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{
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weights_ = averageWeights;
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}
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return innerProduct;
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shift_ = calcShift(trainSamples, trainResponses);
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return true;
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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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std::pair<bool,bool> SVMSGDImpl::areClassesEmpty(Mat responses)
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{
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std::pair<bool,bool> are_classes_empty(true, true);
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int limit_index = responses.rows;
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for(int index = 0; index < limit_index; index++)
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{
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if (isFirstClass(responses.at<float>(index,0)))
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are_classes_empty.first = false;
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else
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are_classes_empty.second = false;
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if (!are_classes_empty.first && ! are_classes_empty.second)
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break;
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}
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return are_classes_empty;
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}
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float SVMSGDImpl::calcShift(InputArray _samples, InputArray _responses) const
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{
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float distance_to_classes[2] = { std::numeric_limits<float>::max(), std::numeric_limits<float>::max() };
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Mat trainSamples = _samples.getMat();
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int trainSamplesCount = trainSamples.rows;
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Mat trainResponses = _responses.getMat();
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for (int samplesIndex = 0; samplesIndex < trainSamplesCount; samplesIndex++)
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{
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Mat currentSample = trainSamples.row(samplesIndex);
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float scalar_product = currentSample.dot(weights_);
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bool is_first_class = isFirstClass(trainResponses.at<float>(samplesIndex));
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int index = is_first_class ? 0:1;
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float sign_to_mul = is_first_class ? 1 : -1;
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float cur_distance = scalar_product * sign_to_mul ;
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if (cur_distance < distance_to_classes[index])
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{
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distance_to_classes[index] = cur_distance;
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}
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}
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//todo: areClassesEmpty(); make const;
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return -(distance_to_classes[0] - distance_to_classes[1]) / 2.f;
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}
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float SVMSGDImpl::predict( InputArray _samples, OutputArray _results, int ) const
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{
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float result = 0;
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cv::Mat samples = _samples.getMat();
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int nSamples = samples.rows;
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cv::Mat results;
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CV_Assert( samples.cols == weights_.cols && samples.type() == CV_32F );
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if( _results.needed() )
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{
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_results.create( nSamples, 1, samples.type() );
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results = _results.getMat();
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}
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else
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{
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CV_Assert( nSamples == 1 );
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results = Mat(1, 1, CV_32F, &result);
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}
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Mat currentSample;
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float criterion = 0;
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for (int sampleIndex = 0; sampleIndex < nSamples; sampleIndex++)
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{
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currentSample = samples.row(sampleIndex);
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criterion = currentSample.dot(weights_) + shift_;
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results.at<float>(sampleIndex) = (criterion >= 0) ? 1 : -1;
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}
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return result;
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}
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void SVMSGDImpl::updateWeights(InputArray _sample, bool is_first_class, float gamma)
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{
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Mat sample = _sample.getMat();
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int responce = is_first_class ? 1 : -1; // ensure that trainResponses are -1 or 1
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if ( sample.dot(weights_) * responce > 1)
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{
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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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weights_ *= (1.f - gamma * params.lambda);
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}
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else
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{
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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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weights_ -= (gamma * params.lambda) * weights_ - gamma * responce * sample;
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//std::cout << "sample " << sample << std::endl;
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//std::cout << "weights_ " << weights_ << std::endl;
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}
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}
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bool SVMSGDImpl::isTrained() const
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{
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return !weights_.empty();
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}
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void SVMSGDImpl::write(FileStorage& fs) const
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{
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if( !isTrained() )
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CV_Error( CV_StsParseError, "SVMSGD model data is invalid, it hasn't been trained" );
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writeParams( fs );
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fs << "shift" << shift_;
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fs << "weights" << weights_;
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}
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void SVMSGDImpl::writeParams( FileStorage& fs ) const
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{
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String SvmsgdTypeStr;
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switch (params.svmsgdType)
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{
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case SGD:
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SvmsgdTypeStr = "SGD";
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break;
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case ASGD:
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SvmsgdTypeStr = "ASGD";
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break;
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case ILLEGAL_VALUE:
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SvmsgdTypeStr = format("Uknown_%d", params.svmsgdType);
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default:
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std::cout << "params.svmsgdType isn't initialized" << std::endl;
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}
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fs << "svmsgdType" << SvmsgdTypeStr;
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fs << "lambda" << params.lambda;
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fs << "gamma0" << params.gamma0;
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fs << "c" << params.c;
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fs << "term_criteria" << "{:";
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if( params.termCrit.type & TermCriteria::EPS )
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fs << "epsilon" << params.termCrit.epsilon;
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if( params.termCrit.type & TermCriteria::COUNT )
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fs << "iterations" << params.termCrit.maxCount;
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fs << "}";
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}
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void SVMSGDImpl::read(const FileNode& fn)
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{
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clear();
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readParams(fn);
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shift_ = (float) fn["shift"];
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fn["weights"] >> weights_;
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}
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void SVMSGDImpl::readParams( const FileNode& fn )
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{
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String svmsgdTypeStr = (String)fn["svmsgdType"];
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SvmsgdType svmsgdType =
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svmsgdTypeStr == "SGD" ? SGD :
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svmsgdTypeStr == "ASGD" ? ASGD : ILLEGAL_VALUE;
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if( svmsgdType == ILLEGAL_VALUE )
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CV_Error( CV_StsParseError, "Missing or invalid SVMSGD type" );
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params.svmsgdType = svmsgdType;
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CV_Assert ( fn["lambda"].isReal() );
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params.lambda = (float)fn["lambda"];
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CV_Assert ( fn["gamma0"].isReal() );
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params.gamma0 = (float)fn["gamma0"];
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CV_Assert ( fn["c"].isReal() );
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params.c = (float)fn["c"];
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FileNode tcnode = fn["term_criteria"];
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if( !tcnode.empty() )
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{
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params.termCrit.epsilon = (double)tcnode["epsilon"];
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params.termCrit.maxCount = (int)tcnode["iterations"];
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params.termCrit.type = (params.termCrit.epsilon > 0 ? TermCriteria::EPS : 0) +
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(params.termCrit.maxCount > 0 ? TermCriteria::COUNT : 0);
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}
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else
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params.termCrit = TermCriteria( TermCriteria::EPS + TermCriteria::COUNT, 1000, FLT_EPSILON );
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}
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void SVMSGDImpl::clear()
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{
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weights_.release();
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shift_ = 0;
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}
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SVMSGDImpl::SVMSGDImpl()
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{
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clear();
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rng_(0);
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params.svmsgdType = ILLEGAL_VALUE;
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// Parameters for learning
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params.lambda = 0; // regularization
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params.gamma0 = 0; // learning rate (ideally should be large at beginning and decay each iteration)
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params.c = 0;
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TermCriteria _termCrit(TermCriteria::COUNT + TermCriteria::EPS, 0, 0);
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params.termCrit = _termCrit;
|
||||
}
|
||||
|
||||
void SVMSGDImpl::setOptimalParameters(int type)
|
||||
{
|
||||
switch (type)
|
||||
{
|
||||
case SGD:
|
||||
params.svmsgdType = SGD;
|
||||
params.lambda = 0.00001;
|
||||
params.gamma0 = 0.05;
|
||||
params.c = 1;
|
||||
params.termCrit.maxCount = 50000;
|
||||
params.termCrit.epsilon = 0.00000001;
|
||||
break;
|
||||
|
||||
case ASGD:
|
||||
params.svmsgdType = ASGD;
|
||||
params.lambda = 0.00001;
|
||||
params.gamma0 = 0.5;
|
||||
params.c = 0.75;
|
||||
params.termCrit.maxCount = 100000;
|
||||
params.termCrit.epsilon = 0.000001;
|
||||
break;
|
||||
|
||||
default:
|
||||
CV_Error( CV_StsParseError, "SVMSGD model data is invalid" );
|
||||
}
|
||||
}
|
||||
|
||||
void SVMSGDImpl::setType(int type)
|
||||
{
|
||||
switch (type)
|
||||
{
|
||||
case SGD:
|
||||
params.svmsgdType = SGD;
|
||||
break;
|
||||
case ASGD:
|
||||
params.svmsgdType = ASGD;
|
||||
break;
|
||||
default:
|
||||
params.svmsgdType = ILLEGAL_VALUE;
|
||||
}
|
||||
}
|
||||
|
||||
int SVMSGDImpl::getType() const
|
||||
{
|
||||
return params.svmsgdType;
|
||||
}
|
||||
} //ml
|
||||
} //cv
|
||||
|
||||
Reference in New Issue
Block a user