Merge pull request #6096 from mnoskova:mn/SVMSGD_to_opencv3_0

doc/opencv.bib

@@ -874,3 +874,11 @@
   year={2007},
   organization={IEEE}
 }
+@incollection{bottou2010large,
+  title={Large-scale machine learning with stochastic gradient descent},
+  author={Bottou, L{\'e}on},
+  booktitle={Proceedings of COMPSTAT'2010},
+  pages={177--186},
+  year={2010},
+  publisher={Springer}
+}

modules/ml/include/opencv2/ml.hpp

@@ -1499,6 +1499,165 @@ public:
     CV_WRAP static Ptr<LogisticRegression> create();
 };
 
+
+/****************************************************************************************\
+*                        Stochastic Gradient Descent SVM Classifier                      *
+\****************************************************************************************/
+
+/*!
+@brief Stochastic Gradient Descent SVM classifier
+
+SVMSGD provides a fast and easy-to-use implementation of the SVM classifier using the Stochastic Gradient Descent approach,
+as presented in @cite bottou2010large.
+
+The classifier has following parameters:
+- model type,
+- margin type,
+- margin regularization (\f$\lambda\f$),
+- initial step size (\f$\gamma_0\f$),
+- step decreasing power (\f$c\f$),
+- and termination criteria.
+
+The model type may have one of the following values: \ref SGD and \ref ASGD.
+
+- \ref SGD is the classic version of SVMSGD classifier: every next step is calculated by the formula
+  \f[w_{t+1} = w_t - \gamma(t) \frac{dQ_i}{dw} |_{w = w_t}\f]
+  where
+  - \f$w_t\f$ is the weights vector for decision function at step \f$t\f$,
+  - \f$\gamma(t)\f$ is the step size of model parameters at the iteration \f$t\f$, it is decreased on each step by the formula
+    \f$\gamma(t) = \gamma_0  (1 + \lambda  \gamma_0 t) ^ {-c}\f$
+  - \f$Q_i\f$ is the target functional from SVM task for sample with number \f$i\f$, this sample is chosen stochastically on each step of the algorithm.
+
+- \ref ASGD is Average Stochastic Gradient Descent SVM Classifier. ASGD classifier averages weights vector on each step of algorithm by the formula
+\f$\widehat{w}_{t+1} = \frac{t}{1+t}\widehat{w}_{t} + \frac{1}{1+t}w_{t+1}\f$
+
+The recommended model type is ASGD (following @cite bottou2010large).
+
+The margin type may have one of the following values: \ref SOFT_MARGIN or \ref HARD_MARGIN.
+
+- You should use \ref HARD_MARGIN type, if you have linearly separable sets.
+- You should use \ref SOFT_MARGIN type, if you have non-linearly separable sets or sets with outliers.
+- In the general case (if you know nothing about linear separability of your sets), use SOFT_MARGIN.
+
+The other parameters may be described as follows:
+- Margin regularization parameter is responsible for weights decreasing at each step and for the strength of restrictions on outliers
+  (the less the parameter, the less probability that an outlier will be ignored).
+  Recommended value for SGD model is 0.0001, for ASGD model is 0.00001.
+
+- Initial step size parameter is the initial value for the step size \f$\gamma(t)\f$.
+  You will have to find the best initial step for your problem.
+
+- Step decreasing power is the power parameter for \f$\gamma(t)\f$ decreasing by the formula, mentioned above.
+  Recommended value for SGD model is 1, for ASGD model is 0.75.
+
+- Termination criteria can be TermCriteria::COUNT, TermCriteria::EPS or TermCriteria::COUNT + TermCriteria::EPS.
+  You will have to find the best termination criteria for your problem.
+
+Note that the parameters margin regularization, initial step size, and step decreasing power should be positive.
+
+To use SVMSGD algorithm do as follows:
+
+- first, create the SVMSGD object. The algoorithm will set optimal parameters by default, but you can set your own parameters via functions setSvmsgdType(),
+  setMarginType(), setMarginRegularization(), setInitialStepSize(), and setStepDecreasingPower().
+
+- then the SVM model can be trained using the train features and the correspondent labels by the method train().
+
+- after that, the label of a new feature vector can be predicted using the method predict().
+
+@code
+// Create empty object
+cv::Ptr<SVMSGD> svmsgd = SVMSGD::create();
+
+// Train the Stochastic Gradient Descent SVM
+svmsgd->train(trainData);
+
+// Predict labels for the new samples
+svmsgd->predict(samples, responses);
+@endcode
+
+*/
+
+class CV_EXPORTS_W SVMSGD : public cv::ml::StatModel
+{
+public:
+
+    /** SVMSGD type.
+    ASGD is often the preferable choice. */
+    enum SvmsgdType
+    {
+        SGD, //!< Stochastic Gradient Descent
+        ASGD //!< Average Stochastic Gradient Descent
+    };
+
+    /** Margin type.*/
+    enum MarginType
+    {
+        SOFT_MARGIN, //!< General case, suits to the case of non-linearly separable sets, allows outliers.
+        HARD_MARGIN  //!< More accurate for the case of linearly separable sets.
+    };
+
+    /**
+     * @return the weights of the trained model (decision function f(x) = weights * x + shift).
+    */
+    CV_WRAP virtual Mat getWeights() = 0;
+
+    /**
+     * @return the shift of the trained model (decision function f(x) = weights * x + shift).
+    */
+    CV_WRAP virtual float getShift() = 0;
+
+    /** @brief Creates empty model.
+     * Use StatModel::train to train the model. Since %SVMSGD has several parameters, you may want to
+     * find the best parameters for your problem or use setOptimalParameters() to set some default parameters.
+    */
+    CV_WRAP static Ptr<SVMSGD> create();
+
+    /** @brief Function sets optimal parameters values for chosen SVM SGD model.
+     * @param svmsgdType is the type of SVMSGD classifier.
+     * @param marginType is the type of margin constraint.
+    */
+    CV_WRAP virtual void setOptimalParameters(int svmsgdType = SVMSGD::ASGD, int marginType = SVMSGD::SOFT_MARGIN) = 0;
+
+    /** @brief %Algorithm type, one of SVMSGD::SvmsgdType. */
+    /** @see setSvmsgdType */
+    CV_WRAP virtual int getSvmsgdType() const = 0;
+    /** @copybrief getSvmsgdType @see getSvmsgdType */
+    CV_WRAP virtual void setSvmsgdType(int svmsgdType) = 0;
+
+    /** @brief %Margin type, one of SVMSGD::MarginType. */
+    /** @see setMarginType */
+    CV_WRAP virtual int getMarginType() const = 0;
+    /** @copybrief getMarginType @see getMarginType */
+    CV_WRAP virtual void setMarginType(int marginType) = 0;
+
+    /** @brief Parameter marginRegularization of a %SVMSGD optimization problem. */
+    /** @see setMarginRegularization */
+    CV_WRAP virtual float getMarginRegularization() const = 0;
+    /** @copybrief getMarginRegularization @see getMarginRegularization */
+    CV_WRAP virtual void setMarginRegularization(float marginRegularization) = 0;
+
+    /** @brief Parameter initialStepSize of a %SVMSGD optimization problem. */
+    /** @see setInitialStepSize */
+    CV_WRAP virtual float getInitialStepSize() const = 0;
+    /** @copybrief getInitialStepSize @see getInitialStepSize */
+    CV_WRAP virtual void setInitialStepSize(float InitialStepSize) = 0;
+
+    /** @brief Parameter stepDecreasingPower of a %SVMSGD optimization problem. */
+    /** @see setStepDecreasingPower */
+    CV_WRAP virtual float getStepDecreasingPower() const = 0;
+    /** @copybrief getStepDecreasingPower @see getStepDecreasingPower */
+    CV_WRAP virtual void setStepDecreasingPower(float stepDecreasingPower) = 0;
+
+    /** @brief Termination criteria of the training algorithm.
+    You can specify the maximum number of iterations (maxCount) and/or how much the error could
+    change between the iterations to make the algorithm continue (epsilon).*/
+    /** @see setTermCriteria */
+    CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
+    /** @copybrief getTermCriteria @see getTermCriteria */
+    CV_WRAP virtual void setTermCriteria(const cv::TermCriteria &val) = 0;
+};
+
+
 /****************************************************************************************\
 *                           Auxilary functions declarations                              *
 \****************************************************************************************/

modules/ml/src/svmsgd.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Copyright (C) 2016, Itseez Inc, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not 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
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "precomp.hpp"
+#include "limits"
+
+#include <iostream>
+
+using std::cout;
+using std::endl;
+
+/****************************************************************************************\
+*                        Stochastic Gradient Descent SVM Classifier                      *
+\****************************************************************************************/
+
+namespace cv
+{
+namespace ml
+{
+
+class SVMSGDImpl : public SVMSGD
+{
+
+public:
+    SVMSGDImpl();
+
+    virtual ~SVMSGDImpl() {}
+
+    virtual bool train(const Ptr<TrainData>& data, int);
+
+    virtual float predict( InputArray samples, OutputArray results=noArray(), int flags = 0 ) const;
+
+    virtual bool isClassifier() const;
+
+    virtual bool isTrained() const;
+
+    virtual void clear();
+
+    virtual void write(FileStorage &fs) const;
+
+    virtual void read(const FileNode &fn);
+
+    virtual Mat getWeights(){ return weights_; }
+
+    virtual float getShift(){ return shift_; }
+
+    virtual int getVarCount() const { return weights_.cols; }
+
+    virtual String getDefaultName() const {return "opencv_ml_svmsgd";}
+
+    virtual void setOptimalParameters(int svmsgdType = ASGD, int marginType = SOFT_MARGIN);
+
+    CV_IMPL_PROPERTY(int, SvmsgdType, params.svmsgdType)
+    CV_IMPL_PROPERTY(int, MarginType, params.marginType)
+    CV_IMPL_PROPERTY(float, MarginRegularization, params.marginRegularization)
+    CV_IMPL_PROPERTY(float, InitialStepSize, params.initialStepSize)
+    CV_IMPL_PROPERTY(float, StepDecreasingPower, params.stepDecreasingPower)
+    CV_IMPL_PROPERTY_S(cv::TermCriteria, TermCriteria, params.termCrit)
+
+private:
+    void updateWeights(InputArray sample, bool positive, float stepSize, Mat &weights);
+
+    void writeParams( FileStorage &fs ) const;
+
+    void readParams( const FileNode &fn );
+
+    static inline bool isPositive(float val) { return val > 0; }
+
+    static void normalizeSamples(Mat &matrix, Mat &average, float &multiplier);
+
+    float calcShift(InputArray _samples, InputArray _responses) const;
+
+    static void makeExtendedTrainSamples(const Mat &trainSamples, Mat &extendedTrainSamples, Mat &average, float &multiplier);
+
+    // Vector with SVM weights
+    Mat weights_;
+    float shift_;
+
+    // Parameters for learning
+    struct SVMSGDParams
+    {
+        float marginRegularization;
+        float initialStepSize;
+        float stepDecreasingPower;
+        TermCriteria termCrit;
+        int svmsgdType;
+        int marginType;
+    };
+
+    SVMSGDParams params;
+};
+
+Ptr<SVMSGD> SVMSGD::create()
+{
+    return makePtr<SVMSGDImpl>();
+}
+
+void SVMSGDImpl::normalizeSamples(Mat &samples, Mat &average, float &multiplier)
+{
+    int featuresCount = samples.cols;
+    int samplesCount = samples.rows;
+
+    average = Mat(1, featuresCount, samples.type());
+    CV_Assert(average.type() ==  CV_32FC1);
+    for (int featureIndex = 0; featureIndex < featuresCount; featureIndex++)
+    {
+        average.at<float>(featureIndex) = static_cast<float>(mean(samples.col(featureIndex))[0]);
+    }
+
+    for (int sampleIndex = 0; sampleIndex < samplesCount; sampleIndex++)
+    {
+        samples.row(sampleIndex) -= average;
+    }
+
+    double normValue = norm(samples);
+
+    multiplier = static_cast<float>(sqrt(samples.total()) / normValue);
+
+    samples *= multiplier;
+}
+
+void SVMSGDImpl::makeExtendedTrainSamples(const Mat &trainSamples, Mat &extendedTrainSamples, Mat &average, float &multiplier)
+{
+    Mat normalizedTrainSamples = trainSamples.clone();
+    int samplesCount = normalizedTrainSamples.rows;
+
+    normalizeSamples(normalizedTrainSamples, average, multiplier);
+
+    Mat onesCol = Mat::ones(samplesCount, 1, CV_32F);
+    cv::hconcat(normalizedTrainSamples, onesCol, extendedTrainSamples);
+}
+
+void SVMSGDImpl::updateWeights(InputArray _sample, bool positive, float stepSize, Mat& weights)
+{
+    Mat sample = _sample.getMat();
+
+    int response = positive ? 1 : -1; // ensure that trainResponses are -1 or 1
+
+    if ( sample.dot(weights) * response > 1)
+    {
+        // Not a support vector, only apply weight decay
+        weights *= (1.f - stepSize * params.marginRegularization);
+    }
+    else
+    {
+        // It's a support vector, add it to the weights
+        weights -= (stepSize * params.marginRegularization) * weights - (stepSize * response) * sample;
+    }
+}
+
+float SVMSGDImpl::calcShift(InputArray _samples, InputArray _responses) const
+{
+    float margin[2] = { std::numeric_limits<float>::max(), std::numeric_limits<float>::max() };
+
+    Mat trainSamples = _samples.getMat();
+    int trainSamplesCount = trainSamples.rows;
+
+    Mat trainResponses = _responses.getMat();
+
+    CV_Assert(trainResponses.type() ==  CV_32FC1);
+    for (int samplesIndex = 0; samplesIndex < trainSamplesCount; samplesIndex++)
+    {
+        Mat currentSample = trainSamples.row(samplesIndex);
+        float dotProduct = static_cast<float>(currentSample.dot(weights_));
+
+        bool positive = isPositive(trainResponses.at<float>(samplesIndex));
+        int index = positive ? 0 : 1;
+        float signToMul = positive ? 1.f : -1.f;
+        float curMargin = dotProduct * signToMul;
+
+        if (curMargin < margin[index])
+        {
+            margin[index] = curMargin;
+        }
+    }
+
+    return -(margin[0] - margin[1]) / 2.f;
+}
+
+bool SVMSGDImpl::train(const Ptr<TrainData>& data, int)
+{
+    clear();
+    CV_Assert( isClassifier() );   //toDo: consider
+
+    Mat trainSamples = data->getTrainSamples();
+
+    int featureCount = trainSamples.cols;
+    Mat trainResponses = data->getTrainResponses();        // (trainSamplesCount x 1) matrix
+
+    CV_Assert(trainResponses.rows == trainSamples.rows);
+
+    if (trainResponses.empty())
+    {
+        return false;
+    }
+
+    int positiveCount = countNonZero(trainResponses >= 0);
+    int negativeCount = countNonZero(trainResponses < 0);
+
+    if ( positiveCount <= 0 || negativeCount <= 0 )
+    {
+        weights_ = Mat::zeros(1, featureCount, CV_32F);
+        shift_ = (positiveCount > 0) ? 1.f : -1.f;
+        return true;
+    }
+
+    Mat extendedTrainSamples;
+    Mat average;
+    float multiplier = 0;
+    makeExtendedTrainSamples(trainSamples, extendedTrainSamples, average, multiplier);
+
+    int extendedTrainSamplesCount = extendedTrainSamples.rows;
+    int extendedFeatureCount = extendedTrainSamples.cols;
+
+    Mat extendedWeights = Mat::zeros(1, extendedFeatureCount, CV_32F);
+    Mat previousWeights = Mat::zeros(1, extendedFeatureCount, CV_32F);
+    Mat averageExtendedWeights;
+    if (params.svmsgdType == ASGD)
+    {
+        averageExtendedWeights = Mat::zeros(1, extendedFeatureCount, CV_32F);
+    }
+
+    RNG rng(0);
+
+    CV_Assert (params.termCrit.type & TermCriteria::COUNT || params.termCrit.type & TermCriteria::EPS);
+    int maxCount = (params.termCrit.type & TermCriteria::COUNT) ? params.termCrit.maxCount : INT_MAX;
+    double epsilon = (params.termCrit.type & TermCriteria::EPS) ? params.termCrit.epsilon : 0;
+
+    double err = DBL_MAX;
+    CV_Assert (trainResponses.type() == CV_32FC1);
+    // Stochastic gradient descent SVM
+    for (int iter = 0; (iter < maxCount) && (err > epsilon); iter++)
+    {
+        int randomNumber = rng.uniform(0, extendedTrainSamplesCount);             //generate sample number
+
+        Mat currentSample = extendedTrainSamples.row(randomNumber);
+
+        float stepSize = params.initialStepSize * std::pow((1 + params.marginRegularization * params.initialStepSize * (float)iter), (-params.stepDecreasingPower));    //update stepSize
+
+        updateWeights( currentSample, isPositive(trainResponses.at<float>(randomNumber)), stepSize, extendedWeights );
+
+        //average weights (only for ASGD model)
+        if (params.svmsgdType == ASGD)
+        {
+            averageExtendedWeights = ((float)iter/ (1 + (float)iter)) * averageExtendedWeights  + extendedWeights / (1 + (float) iter);
+            err = norm(averageExtendedWeights - previousWeights);
+            averageExtendedWeights.copyTo(previousWeights);
+        }
+        else
+        {
+            err = norm(extendedWeights - previousWeights);
+            extendedWeights.copyTo(previousWeights);
+        }
+    }
+
+    if (params.svmsgdType == ASGD)
+    {
+        extendedWeights = averageExtendedWeights;
+    }
+
+    Rect roi(0, 0, featureCount, 1);
+    weights_ = extendedWeights(roi);
+    weights_ *= multiplier;
+
+    CV_Assert((params.marginType == SOFT_MARGIN || params.marginType == HARD_MARGIN) && (extendedWeights.type() ==  CV_32FC1));
+
+    if (params.marginType == SOFT_MARGIN)
+    {
+        shift_ = extendedWeights.at<float>(featureCount) - static_cast<float>(weights_.dot(average));
+    }
+    else
+    {
+        shift_ = calcShift(trainSamples, trainResponses);
+    }
+
+    return true;
+}
+
+float SVMSGDImpl::predict( InputArray _samples, OutputArray _results, int ) const
+{
+    float result = 0;
+    cv::Mat samples = _samples.getMat();
+    int nSamples = samples.rows;
+    cv::Mat results;
+
+    CV_Assert( samples.cols == weights_.cols && samples.type() == CV_32FC1);
+
+    if( _results.needed() )
+    {
+        _results.create( nSamples, 1, samples.type() );
+        results = _results.getMat();
+    }
+    else
+    {
+        CV_Assert( nSamples == 1 );
+        results = Mat(1, 1, CV_32FC1, &result);
+    }
+
+    for (int sampleIndex = 0; sampleIndex < nSamples; sampleIndex++)
+    {
+        Mat currentSample = samples.row(sampleIndex);
+        float criterion = static_cast<float>(currentSample.dot(weights_)) + shift_;
+        results.at<float>(sampleIndex) = (criterion >= 0) ? 1.f : -1.f;
+    }
+
+    return result;
+}
+
+bool SVMSGDImpl::isClassifier() const
+{
+    return (params.svmsgdType == SGD || params.svmsgdType == ASGD)
+            &&
+            (params.marginType == SOFT_MARGIN || params.marginType == HARD_MARGIN)
+            &&
+            (params.marginRegularization > 0) && (params.initialStepSize > 0) && (params.stepDecreasingPower >= 0);
+}
+
+bool SVMSGDImpl::isTrained() const
+{
+    return !weights_.empty();
+}
+
+void SVMSGDImpl::write(FileStorage& fs) const
+{
+    if( !isTrained() )
+        CV_Error( CV_StsParseError, "SVMSGD model data is invalid, it hasn't been trained" );
+
+    writeParams( fs );
+
+    fs << "weights" << weights_;
+    fs << "shift" << shift_;
+}
+
+void SVMSGDImpl::writeParams( FileStorage& fs ) const
+{
+    String SvmsgdTypeStr;
+
+    switch (params.svmsgdType)
+    {
+    case SGD:
+        SvmsgdTypeStr = "SGD";
+        break;
+    case ASGD:
+        SvmsgdTypeStr = "ASGD";
+        break;
+    default:
+        SvmsgdTypeStr = format("Unknown_%d", params.svmsgdType);
+    }
+
+    fs << "svmsgdType" << SvmsgdTypeStr;
+
+    String marginTypeStr;
+
+    switch (params.marginType)
+    {
+    case SOFT_MARGIN:
+        marginTypeStr = "SOFT_MARGIN";
+        break;
+    case HARD_MARGIN:
+        marginTypeStr = "HARD_MARGIN";
+        break;
+    default:
+        marginTypeStr = format("Unknown_%d", params.marginType);
+    }
+
+    fs << "marginType" << marginTypeStr;
+
+    fs << "marginRegularization" << params.marginRegularization;
+    fs << "initialStepSize" << params.initialStepSize;
+    fs << "stepDecreasingPower" << params.stepDecreasingPower;
+
+    fs << "term_criteria" << "{:";
+    if( params.termCrit.type & TermCriteria::EPS )
+        fs << "epsilon" << params.termCrit.epsilon;
+    if( params.termCrit.type & TermCriteria::COUNT )
+        fs << "iterations" << params.termCrit.maxCount;
+    fs << "}";
+}
+void SVMSGDImpl::readParams( const FileNode& fn )
+{
+    String svmsgdTypeStr = (String)fn["svmsgdType"];
+    int svmsgdType =
+            svmsgdTypeStr == "SGD" ? SGD :
+                                     svmsgdTypeStr == "ASGD" ? ASGD : -1;
+
+    if( svmsgdType < 0 )
+        CV_Error( CV_StsParseError, "Missing or invalid SVMSGD type" );
+
+    params.svmsgdType = svmsgdType;
+
+    String marginTypeStr = (String)fn["marginType"];
+    int marginType =
+            marginTypeStr == "SOFT_MARGIN" ? SOFT_MARGIN :
+                                             marginTypeStr == "HARD_MARGIN" ? HARD_MARGIN : -1;
+
+    if( marginType < 0 )
+        CV_Error( CV_StsParseError, "Missing or invalid margin type" );
+
+    params.marginType = marginType;
+
+    CV_Assert ( fn["marginRegularization"].isReal() );
+    params.marginRegularization = (float)fn["marginRegularization"];
+
+    CV_Assert ( fn["initialStepSize"].isReal() );
+    params.initialStepSize = (float)fn["initialStepSize"];
+
+    CV_Assert ( fn["stepDecreasingPower"].isReal() );
+    params.stepDecreasingPower = (float)fn["stepDecreasingPower"];
+
+    FileNode tcnode = fn["term_criteria"];
+    CV_Assert(!tcnode.empty());
+    params.termCrit.epsilon = (double)tcnode["epsilon"];
+    params.termCrit.maxCount = (int)tcnode["iterations"];
+    params.termCrit.type = (params.termCrit.epsilon > 0 ? TermCriteria::EPS : 0) +
+            (params.termCrit.maxCount > 0 ? TermCriteria::COUNT : 0);
+    CV_Assert ((params.termCrit.type & TermCriteria::COUNT || params.termCrit.type & TermCriteria::EPS));
+}
+
+void SVMSGDImpl::read(const FileNode& fn)
+{
+    clear();
+
+    readParams(fn);
+
+    fn["weights"] >> weights_;
+    fn["shift"] >> shift_;
+}
+
+void SVMSGDImpl::clear()
+{
+    weights_.release();
+    shift_ = 0;
+}
+
+
+SVMSGDImpl::SVMSGDImpl()
+{
+    clear();
+    setOptimalParameters();
+}
+
+void SVMSGDImpl::setOptimalParameters(int svmsgdType, int marginType)
+{
+    switch (svmsgdType)
+    {
+    case SGD:
+        params.svmsgdType = SGD;
+        params.marginType = (marginType == SOFT_MARGIN) ? SOFT_MARGIN :
+                                                          (marginType == HARD_MARGIN) ? HARD_MARGIN : -1;
+        params.marginRegularization = 0.0001f;
+        params.initialStepSize = 0.05f;
+        params.stepDecreasingPower = 1.f;
+        params.termCrit = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100000, 0.00001);
+        break;
+
+    case ASGD:
+        params.svmsgdType = ASGD;
+        params.marginType = (marginType == SOFT_MARGIN) ? SOFT_MARGIN :
+                                                          (marginType == HARD_MARGIN) ? HARD_MARGIN : -1;
+        params.marginRegularization = 0.00001f;
+        params.initialStepSize = 0.05f;
+        params.stepDecreasingPower = 0.75f;
+        params.termCrit = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100000, 0.00001);
+        break;
+
+    default:
+        CV_Error( CV_StsParseError, "SVMSGD model data is invalid" );
+    }
+}
+}   //ml
+}   //cv

modules/ml/test/test_mltests2.cpp

@@ -193,6 +193,25 @@ int str_to_boost_type( String& str )
 // 8. rtrees
 // 9. ertrees
 
+int str_to_svmsgd_type( String& str )
+{
+    if ( !str.compare("SGD") )
+        return SVMSGD::SGD;
+    if ( !str.compare("ASGD") )
+        return SVMSGD::ASGD;
+    CV_Error( CV_StsBadArg, "incorrect svmsgd type string" );
+    return -1;
+}
+
+int str_to_margin_type( String& str )
+{
+    if ( !str.compare("SOFT_MARGIN") )
+        return SVMSGD::SOFT_MARGIN;
+    if ( !str.compare("HARD_MARGIN") )
+        return SVMSGD::HARD_MARGIN;
+    CV_Error( CV_StsBadArg, "incorrect svmsgd margin type string" );
+    return -1;
+}
 // ---------------------------------- MLBaseTest ---------------------------------------------------
 
 CV_MLBaseTest::CV_MLBaseTest(const char* _modelName)
@@ -436,6 +455,27 @@ int CV_MLBaseTest::train( int testCaseIdx )
         model = m;
     }
 
+    else if( modelName == CV_SVMSGD )
+    {
+        String svmsgdTypeStr;
+        modelParamsNode["svmsgdType"] >> svmsgdTypeStr;
+
+        Ptr<SVMSGD> m = SVMSGD::create();
+        int svmsgdType = str_to_svmsgd_type( svmsgdTypeStr );
+        m->setSvmsgdType(svmsgdType);
+
+        String marginTypeStr;
+        modelParamsNode["marginType"] >> marginTypeStr;
+        int marginType = str_to_margin_type( marginTypeStr );
+        m->setMarginType(marginType);
+
+        m->setMarginRegularization(modelParamsNode["marginRegularization"]);
+        m->setInitialStepSize(modelParamsNode["initialStepSize"]);
+        m->setStepDecreasingPower(modelParamsNode["stepDecreasingPower"]);
+        m->setTermCriteria(TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 10000, 0.00001));
+        model = m;
+    }
+
     if( !model.empty() )
         is_trained = model->train(data, 0);
 
@@ -457,7 +497,7 @@ float CV_MLBaseTest::get_test_error( int /*testCaseIdx*/, vector<float> *resp )
     else if( modelName == CV_ANN )
         err = ann_calc_error( model, data, cls_map, type, resp );
     else if( modelName == CV_DTREE || modelName == CV_BOOST || modelName == CV_RTREES ||
-             modelName == CV_SVM || modelName == CV_NBAYES || modelName == CV_KNEAREST )
+             modelName == CV_SVM || modelName == CV_NBAYES || modelName == CV_KNEAREST || modelName == CV_SVMSGD )
         err = model->calcError( data, true, _resp );
     if( !_resp.empty() && resp )
         _resp.convertTo(*resp, CV_32F);
@@ -485,6 +525,8 @@ void CV_MLBaseTest::load( const char* filename )
         model = Algorithm::load<Boost>( filename );
     else if( modelName == CV_RTREES )
         model = Algorithm::load<RTrees>( filename );
+    else if( modelName == CV_SVMSGD )
+        model = Algorithm::load<SVMSGD>( filename );
     else
         CV_Error( CV_StsNotImplemented, "invalid stat model name");
 }

modules/ml/test/test_precomp.hpp

@@ -24,6 +24,7 @@
 #define CV_BOOST    "boost"
 #define CV_RTREES   "rtrees"
 #define CV_ERTREES  "ertrees"
+#define CV_SVMSGD   "svmsgd"
 
 enum { CV_TRAIN_ERROR=0, CV_TEST_ERROR=1 };
 
@@ -38,6 +39,7 @@ using cv::ml::ANN_MLP;
 using cv::ml::DTrees;
 using cv::ml::Boost;
 using cv::ml::RTrees;
+using cv::ml::SVMSGD;
 
 class CV_MLBaseTest : public cvtest::BaseTest
 {

modules/ml/test/test_save_load.cpp

@@ -156,6 +156,7 @@ TEST(ML_DTree, save_load) { CV_SLMLTest test( CV_DTREE ); test.safe_run(); }
 TEST(ML_Boost, save_load) { CV_SLMLTest test( CV_BOOST ); test.safe_run(); }
 TEST(ML_RTrees, save_load) { CV_SLMLTest test( CV_RTREES ); test.safe_run(); }
 TEST(DISABLED_ML_ERTrees, save_load) { CV_SLMLTest test( CV_ERTREES ); test.safe_run(); }
+TEST(MV_SVMSGD, save_load){ CV_SLMLTest test( CV_SVMSGD ); test.safe_run(); }
 
 class CV_LegacyTest : public cvtest::BaseTest
 {
@@ -201,6 +202,8 @@ protected:
             model = Algorithm::load<SVM>(filename);
         else if (modelName == CV_RTREES)
             model = Algorithm::load<RTrees>(filename);
+        else if (modelName == CV_SVMSGD)
+            model = Algorithm::load<SVMSGD>(filename);
         if (!model)
         {
             code = cvtest::TS::FAIL_INVALID_TEST_DATA;
@@ -260,6 +263,7 @@ TEST(ML_DTree, legacy_load) { CV_LegacyTest test(CV_DTREE, "_abalone.xml;_mushro
 TEST(ML_NBayes, legacy_load) { CV_LegacyTest test(CV_NBAYES, "_waveform.xml"); test.safe_run(); }
 TEST(ML_SVM, legacy_load) { CV_LegacyTest test(CV_SVM, "_poletelecomm.xml;_waveform.xml"); test.safe_run(); }
 TEST(ML_RTrees, legacy_load) { CV_LegacyTest test(CV_RTREES, "_waveform.xml"); test.safe_run(); }
+TEST(ML_SVMSGD, legacy_load) { CV_LegacyTest test(CV_SVMSGD, "_waveform.xml"); test.safe_run(); }
 
 /*TEST(ML_SVM, throw_exception_when_save_untrained_model)
 {

modules/ml/test/test_svmsgd.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                        Intel License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of Intel Corporation may not 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
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "test_precomp.hpp"
+#include "opencv2/highgui.hpp"
+
+using namespace cv;
+using namespace cv::ml;
+using cv::ml::SVMSGD;
+using cv::ml::TrainData;
+
+
+
+class CV_SVMSGDTrainTest : public cvtest::BaseTest
+{
+public:
+    enum TrainDataType
+    {
+        UNIFORM_SAME_SCALE,
+        UNIFORM_DIFFERENT_SCALES
+    };
+
+    CV_SVMSGDTrainTest(const Mat &_weights, float shift, TrainDataType type, double precision = 0.01);
+private:
+    virtual void run( int start_from );
+    static float decisionFunction(const Mat &sample, const Mat &weights, float shift);
+    void makeData(int samplesCount, const Mat &weights, float shift, RNG &rng, Mat &samples, Mat & responses);
+    void generateSameBorders(int featureCount);
+    void generateDifferentBorders(int featureCount);
+
+    TrainDataType type;
+    double precision;
+    std::vector<std::pair<float,float> > borders;
+    cv::Ptr<TrainData> data;
+    cv::Mat testSamples;
+    cv::Mat testResponses;
+    static const int TEST_VALUE_LIMIT = 500;
+};
+
+void CV_SVMSGDTrainTest::generateSameBorders(int featureCount)
+{
+    float lowerLimit = -TEST_VALUE_LIMIT;
+    float upperLimit = TEST_VALUE_LIMIT;
+
+    for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
+    {
+        borders.push_back(std::pair<float,float>(lowerLimit, upperLimit));
+    }
+}
+
+void CV_SVMSGDTrainTest::generateDifferentBorders(int featureCount)
+{
+    float lowerLimit = -TEST_VALUE_LIMIT;
+    float upperLimit = TEST_VALUE_LIMIT;
+    cv::RNG rng(0);
+
+    for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
+    {
+        int crit = rng.uniform(0, 2);
+
+        if (crit > 0)
+        {
+            borders.push_back(std::pair<float,float>(lowerLimit, upperLimit));
+        }
+        else
+        {
+            borders.push_back(std::pair<float,float>(lowerLimit/1000, upperLimit/1000));
+        }
+    }
+}
+
+float CV_SVMSGDTrainTest::decisionFunction(const Mat &sample, const Mat &weights, float shift)
+{
+    return static_cast<float>(sample.dot(weights)) + shift;
+}
+
+void CV_SVMSGDTrainTest::makeData(int samplesCount, const Mat &weights, float shift, RNG &rng, Mat &samples, Mat & responses)
+{
+    int featureCount = weights.cols;
+
+    samples.create(samplesCount, featureCount, CV_32FC1);
+    for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
+    {
+        rng.fill(samples.col(featureIndex), RNG::UNIFORM, borders[featureIndex].first, borders[featureIndex].second);
+    }
+
+    responses.create(samplesCount, 1, CV_32FC1);
+
+    for (int i = 0 ; i < samplesCount; i++)
+    {
+        responses.at<float>(i) = decisionFunction(samples.row(i), weights, shift) > 0 ? 1.f : -1.f;
+    }
+
+}
+
+CV_SVMSGDTrainTest::CV_SVMSGDTrainTest(const Mat &weights, float shift, TrainDataType _type, double _precision)
+{
+    type = _type;
+    precision = _precision;
+
+    int featureCount = weights.cols;
+
+    switch(type)
+    {
+    case UNIFORM_SAME_SCALE:
+        generateSameBorders(featureCount);
+        break;
+    case UNIFORM_DIFFERENT_SCALES:
+        generateDifferentBorders(featureCount);
+        break;
+    default:
+        CV_Error(CV_StsBadArg, "Unknown train data type");
+    }
+
+    RNG rng(0);
+
+    Mat trainSamples;
+    Mat trainResponses;
+    int trainSamplesCount = 10000;
+    makeData(trainSamplesCount, weights, shift, rng, trainSamples, trainResponses);
+    data = TrainData::create(trainSamples, cv::ml::ROW_SAMPLE, trainResponses);
+
+    int testSamplesCount = 100000;
+    makeData(testSamplesCount, weights, shift, rng, testSamples, testResponses);
+}
+
+void CV_SVMSGDTrainTest::run( int /*start_from*/ )
+{
+    cv::Ptr<SVMSGD> svmsgd = SVMSGD::create();
+
+    svmsgd->train(data);
+
+    Mat responses;
+
+    svmsgd->predict(testSamples, responses);
+
+    int errCount = 0;
+    int testSamplesCount = testSamples.rows;
+
+    CV_Assert((responses.type() == CV_32FC1) && (testResponses.type() == CV_32FC1));
+    for (int i = 0; i < testSamplesCount; i++)
+    {
+        if (responses.at<float>(i) * testResponses.at<float>(i) < 0)
+            errCount++;
+    }
+
+    float err = (float)errCount / testSamplesCount;
+
+    if ( err > precision )
+    {
+        ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
+    }
+}
+
+void makeWeightsAndShift(int featureCount, Mat &weights, float &shift)
+{
+    weights.create(1, featureCount, CV_32FC1);
+    cv::RNG rng(0);
+    double lowerLimit = -1;
+    double upperLimit = 1;
+
+    rng.fill(weights, RNG::UNIFORM, lowerLimit, upperLimit);
+    shift = static_cast<float>(rng.uniform(-featureCount, featureCount));
+}
+
+
+TEST(ML_SVMSGD, trainSameScale2)
+{
+    int featureCount = 2;
+
+    Mat weights;
+
+    float shift = 0;
+    makeWeightsAndShift(featureCount, weights, shift);
+
+    CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_SAME_SCALE);
+    test.safe_run();
+}
+
+TEST(ML_SVMSGD, trainSameScale5)
+{
+    int featureCount = 5;
+
+    Mat weights;
+
+    float shift = 0;
+    makeWeightsAndShift(featureCount, weights, shift);
+
+    CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_SAME_SCALE);
+    test.safe_run();
+}
+
+TEST(ML_SVMSGD, trainSameScale100)
+{
+    int featureCount = 100;
+
+    Mat weights;
+
+    float shift = 0;
+    makeWeightsAndShift(featureCount, weights, shift);
+
+    CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_SAME_SCALE, 0.02);
+    test.safe_run();
+}
+
+TEST(ML_SVMSGD, trainDifferentScales2)
+{
+    int featureCount = 2;
+
+    Mat weights;
+
+    float shift = 0;
+    makeWeightsAndShift(featureCount, weights, shift);
+
+    CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_DIFFERENT_SCALES, 0.01);
+    test.safe_run();
+}
+
+TEST(ML_SVMSGD, trainDifferentScales5)
+{
+    int featureCount = 5;
+
+    Mat weights;
+
+    float shift = 0;
+    makeWeightsAndShift(featureCount, weights, shift);
+
+    CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_DIFFERENT_SCALES, 0.01);
+    test.safe_run();
+}
+
+TEST(ML_SVMSGD, trainDifferentScales100)
+{
+    int featureCount = 100;
+
+    Mat weights;
+
+    float shift = 0;
+    makeWeightsAndShift(featureCount, weights, shift);
+
+    CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_DIFFERENT_SCALES, 0.01);
+    test.safe_run();
+}
+
+TEST(ML_SVMSGD, twoPoints)
+{
+    Mat samples(2, 2, CV_32FC1);
+    samples.at<float>(0,0) = 0;
+    samples.at<float>(0,1) = 0;
+    samples.at<float>(1,0) = 1000;
+    samples.at<float>(1,1) = 1;
+
+    Mat responses(2, 1, CV_32FC1);
+    responses.at<float>(0) = -1;
+    responses.at<float>(1) = 1;
+
+    cv::Ptr<TrainData> trainData = TrainData::create(samples, cv::ml::ROW_SAMPLE, responses);
+
+    Mat realWeights(1, 2, CV_32FC1);
+    realWeights.at<float>(0) = 1000;
+    realWeights.at<float>(1) = 1;
+
+    float realShift = -500000.5;
+
+    float normRealWeights = static_cast<float>(norm(realWeights));
+    realWeights /= normRealWeights;
+    realShift /= normRealWeights;
+
+    cv::Ptr<SVMSGD> svmsgd = SVMSGD::create();
+    svmsgd->setOptimalParameters();
+    svmsgd->train( trainData );
+
+    Mat foundWeights = svmsgd->getWeights();
+    float foundShift = svmsgd->getShift();
+
+    float normFoundWeights = static_cast<float>(norm(foundWeights));
+    foundWeights /= normFoundWeights;
+    foundShift /= normFoundWeights;
+    CV_Assert((norm(foundWeights - realWeights) < 0.001) && (abs((foundShift - realShift) / realShift) < 0.05));
+}

samples/cpp/train_svmsgd.cpp

+#include <opencv2/opencv.hpp>
+#include "opencv2/video/tracking.hpp"
+#include "opencv2/imgproc/imgproc.hpp"
+#include "opencv2/highgui/highgui.hpp"
+
+using namespace cv;
+using namespace cv::ml;
+
+
+struct Data
+{
+    Mat img;
+    Mat samples;          //Set of train samples. Contains points on image
+    Mat responses;        //Set of responses for train samples
+
+    Data()
+    {
+        const int WIDTH = 841;
+        const int HEIGHT = 594;
+        img = Mat::zeros(HEIGHT, WIDTH, CV_8UC3);
+        imshow("Train svmsgd", img);
+    }
+};
+
+//Train with SVMSGD algorithm
+//(samples, responses) is a train set
+//weights is a required vector for decision function of SVMSGD algorithm
+bool doTrain(const Mat samples, const Mat responses, Mat &weights, float &shift);
+
+//function finds two points for drawing line (wx = 0)
+bool findPointsForLine(const Mat &weights, float shift, Point points[], int width, int height);
+
+// function finds cross point of line (wx = 0) and segment ( (y = HEIGHT, 0 <= x <= WIDTH) or (x = WIDTH, 0 <= y <= HEIGHT) )
+bool findCrossPointWithBorders(const Mat &weights, float shift, const std::pair<Point,Point> &segment, Point &crossPoint);
+
+//segments' initialization ( (y = HEIGHT, 0 <= x <= WIDTH) and (x = WIDTH, 0 <= y <= HEIGHT) )
+void fillSegments(std::vector<std::pair<Point,Point> > &segments, int width, int height);
+
+//redraw points' set and line (wx = 0)
+void redraw(Data data, const Point points[2]);
+
+//add point in train set, train SVMSGD algorithm and draw results on image
+void addPointRetrainAndRedraw(Data &data, int x, int y, int response);
+
+
+bool doTrain( const Mat samples, const Mat responses, Mat &weights, float &shift)
+{
+    cv::Ptr<SVMSGD> svmsgd = SVMSGD::create();
+
+    cv::Ptr<TrainData> trainData = TrainData::create(samples, cv::ml::ROW_SAMPLE, responses);
+    svmsgd->train( trainData );
+
+    if (svmsgd->isTrained())
+    {
+        weights = svmsgd->getWeights();
+        shift = svmsgd->getShift();
+
+        return true;
+    }
+    return false;
+}
+
+void fillSegments(std::vector<std::pair<Point,Point> > &segments, int width, int height)
+{
+    std::pair<Point,Point> currentSegment;
+
+    currentSegment.first = Point(width, 0);
+    currentSegment.second = Point(width, height);
+    segments.push_back(currentSegment);
+
+    currentSegment.first = Point(0, height);
+    currentSegment.second = Point(width, height);
+    segments.push_back(currentSegment);
+
+    currentSegment.first = Point(0, 0);
+    currentSegment.second = Point(width, 0);
+    segments.push_back(currentSegment);
+
+    currentSegment.first = Point(0, 0);
+    currentSegment.second = Point(0, height);
+    segments.push_back(currentSegment);
+}
+
+
+bool findCrossPointWithBorders(const Mat &weights, float shift, const std::pair<Point,Point> &segment, Point &crossPoint)
+{
+    int x = 0;
+    int y = 0;
+    int xMin = std::min(segment.first.x, segment.second.x);
+    int xMax = std::max(segment.first.x, segment.second.x);
+    int yMin = std::min(segment.first.y, segment.second.y);
+    int yMax = std::max(segment.first.y, segment.second.y);
+
+    CV_Assert(weights.type() == CV_32FC1);
+    CV_Assert(xMin == xMax || yMin == yMax);
+
+    if (xMin == xMax && weights.at<float>(1) != 0)
+    {
+        x = xMin;
+        y = static_cast<int>(std::floor( - (weights.at<float>(0) * x + shift) / weights.at<float>(1)));
+        if (y >= yMin && y <= yMax)
+        {
+            crossPoint.x = x;
+            crossPoint.y = y;
+            return true;
+        }
+    }
+    else if (yMin == yMax && weights.at<float>(0) != 0)
+    {
+        y = yMin;
+        x = static_cast<int>(std::floor( - (weights.at<float>(1) * y + shift) / weights.at<float>(0)));
+        if (x >= xMin && x <= xMax)
+        {
+            crossPoint.x = x;
+            crossPoint.y = y;
+            return true;
+        }
+    }
+    return false;
+}
+
+bool findPointsForLine(const Mat &weights, float shift, Point points[2], int width, int height)
+{
+    if (weights.empty())
+    {
+        return false;
+    }
+
+    int foundPointsCount = 0;
+    std::vector<std::pair<Point,Point> > segments;
+    fillSegments(segments, width, height);
+
+    for (uint i = 0; i < segments.size(); i++)
+    {
+        if (findCrossPointWithBorders(weights, shift, segments[i], points[foundPointsCount]))
+            foundPointsCount++;
+        if (foundPointsCount >= 2)
+            break;
+    }
+
+    return true;
+}
+
+void redraw(Data data, const Point points[2])
+{
+    data.img.setTo(0);
+    Point center;
+    int radius = 3;
+    Scalar color;
+    CV_Assert((data.samples.type() == CV_32FC1) && (data.responses.type() == CV_32FC1));
+    for (int i = 0; i < data.samples.rows; i++)
+    {
+        center.x = static_cast<int>(data.samples.at<float>(i,0));
+        center.y = static_cast<int>(data.samples.at<float>(i,1));
+        color = (data.responses.at<float>(i) > 0) ? Scalar(128,128,0) : Scalar(0,128,128);
+        circle(data.img, center, radius, color, 5);
+    }
+    line(data.img, points[0], points[1],cv::Scalar(1,255,1));
+
+    imshow("Train svmsgd", data.img);
+}
+
+void addPointRetrainAndRedraw(Data &data, int x, int y, int response)
+{
+    Mat currentSample(1, 2, CV_32FC1);
+
+    currentSample.at<float>(0,0) = (float)x;
+    currentSample.at<float>(0,1) = (float)y;
+    data.samples.push_back(currentSample);
+    data.responses.push_back(response);
+
+    Mat weights(1, 2, CV_32FC1);
+    float shift = 0;
+
+    if (doTrain(data.samples, data.responses, weights, shift))
+    {
+        Point points[2];
+        findPointsForLine(weights, shift, points, data.img.cols, data.img.rows);
+
+        redraw(data, points);
+    }
+}
+
+
+static void onMouse( int event, int x, int y, int, void* pData)
+{
+    Data &data = *(Data*)pData;
+
+    switch( event )
+    {
+    case CV_EVENT_LBUTTONUP:
+        addPointRetrainAndRedraw(data, x, y, 1);
+        break;
+
+    case CV_EVENT_RBUTTONDOWN:
+        addPointRetrainAndRedraw(data, x, y, -1);
+        break;
+    }
+
+}
+
+int main()
+{
+    Data data;
+
+    setMouseCallback( "Train svmsgd", onMouse, &data );
+    waitKey();
+
+    return 0;
+}