Corrected spelling mistakes

modules/ml/include/opencv2/ml.hpp

@@ -1535,7 +1535,7 @@ The margin type may have one of the following values: \ref SOFT_MARGIN or \ref H
 
 - 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 linearly separability of your sets), use SOFT_MARGIN.
+- 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:
 - \f$\lambda\f$ parameter is responsible for weights decreasing at each step and for the strength of restrictions on outliers

modules/ml/src/svmsgd.cpp

@@ -11,7 +11,7 @@
 //                For Open Source Computer Vision Library
 //
 // Copyright (C) 2000, Intel Corporation, all rights reserved.
-// Copyright (C) 2014, Itseez Inc, 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,
@@ -103,7 +103,7 @@ public:
     CV_IMPL_PROPERTY_S(cv::TermCriteria, TermCriteria, params.termCrit)
 
 private:
-    void updateWeights(InputArray sample, bool isFirstClass, float gamma, Mat &weights);
+    void updateWeights(InputArray sample, bool isPositive, float gamma, Mat &weights);
 
     std::pair<bool,bool> areClassesEmpty(Mat responses);
 
@@ -111,7 +111,7 @@ private:
 
     void readParams( const FileNode &fn );
 
-    static inline bool isFirstClass(float val) { return val > 0; }
+    static inline bool isPositive(float val) { return val > 0; }
 
     static void normalizeSamples(Mat &matrix, Mat &average, float &multiplier);
 
@@ -152,7 +152,7 @@ std::pair<bool,bool> SVMSGDImpl::areClassesEmpty(Mat responses)
 
     for(int index = 0; index < limit_index; index++)
     {
-        if (isFirstClass(responses.at<float>(index)))
+        if (isPositive(responses.at<float>(index)))
             emptyInClasses.first = false;
         else
             emptyInClasses.second = false;
@@ -172,7 +172,7 @@ void SVMSGDImpl::normalizeSamples(Mat &samples, Mat &average, float &multiplier)
     average = Mat(1, featuresCount, samples.type());
     for (int featureIndex = 0; featureIndex < featuresCount; featureIndex++)
     {
-        Scalar scalAverage = mean(samples.col(featureIndex))[0];
+        Scalar scalAverage = mean(samples.col(featureIndex));
         average.at<float>(featureIndex) = static_cast<float>(scalAverage[0]);
     }
 
@@ -190,13 +190,13 @@ void SVMSGDImpl::normalizeSamples(Mat &samples, Mat &average, float &multiplier)
 
 void SVMSGDImpl::makeExtendedTrainSamples(const Mat &trainSamples, Mat &extendedTrainSamples, Mat &average, float &multiplier)
 {
-    Mat normalisedTrainSamples = trainSamples.clone();
-    int samplesCount = normalisedTrainSamples.rows;
+    Mat normalizedTrainSamples = trainSamples.clone();
+    int samplesCount = normalizedTrainSamples.rows;
 
-    normalizeSamples(normalisedTrainSamples, average, multiplier);
+    normalizeSamples(normalizedTrainSamples, average, multiplier);
 
     Mat onesCol = Mat::ones(samplesCount, 1, CV_32F);
-    cv::hconcat(normalisedTrainSamples, onesCol, extendedTrainSamples);
+    cv::hconcat(normalizedTrainSamples, onesCol, extendedTrainSamples);
 }
 
 void SVMSGDImpl::updateWeights(InputArray _sample, bool firstClass, float gamma, Mat& weights)
@@ -231,7 +231,7 @@ float SVMSGDImpl::calcShift(InputArray _samples, InputArray _responses) const
         Mat currentSample = trainSamples.row(samplesIndex);
         float dotProduct = static_cast<float>(currentSample.dot(weights_));
 
-        bool firstClass = isFirstClass(trainResponses.at<float>(samplesIndex));
+        bool firstClass = isPositive(trainResponses.at<float>(samplesIndex));
         int index = firstClass ? 0 : 1;
         float signToMul = firstClass ? 1.f : -1.f;
         float curDistance = dotProduct * signToMul;
@@ -297,11 +297,10 @@ bool SVMSGDImpl::train(const Ptr<TrainData>& data, int)
         int randomNumber = rng.uniform(0, extendedTrainSamplesCount);             //generate sample number
 
         Mat currentSample = extendedTrainSamples.row(randomNumber);
-        bool firstClass = isFirstClass(trainResponses.at<float>(randomNumber));
 
         float gamma = params.gamma0 * std::pow((1 + params.lambda * params.gamma0 * (float)iter), (-params.c));    //update gamma
 
-        updateWeights( currentSample, firstClass, gamma, extendedWeights );
+        updateWeights( currentSample, isPositive(trainResponses.at<float>(randomNumber)), gamma, extendedWeights );
 
         //average weights (only for ASGD model)
         if (params.svmsgdType == ASGD)

modules/ml/test/test_svmsgd.cpp

@@ -134,7 +134,7 @@ void CV_SVMSGDTrainTest::makeTestData(Mat weights, float shift)
 {
     int testSamplesCount = 100000;
     int featureCount = weights.cols;
-    cv::RNG rng(0);
+    cv::RNG rng(42);
 
     testSamples.create(testSamplesCount, featureCount, CV_32FC1);
     for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)

samples/cpp/train_svmsgd.cpp

@@ -6,8 +6,6 @@
 using namespace cv;
 using namespace cv::ml;
 
-#define WIDTH 841
-#define HEIGHT 594
 
 struct Data
 {
@@ -17,6 +15,8 @@ struct Data
 
     Data()
     {
+        const int WIDTH = 841;
+        const int HEIGHT = 594;
         img = Mat::zeros(HEIGHT, WIDTH, CV_8UC3);
         imshow("Train svmsgd", img);
     }