generic-library/opencv
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Corrected spelling mistakes

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This commit is contained in:
Marina Noskova
2016-02-15 14:35:36 +03:00
parent 5496dedd6a
commit ff54952769
4 changed files with 15 additions and 16 deletions
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23
modules/ml/src/svmsgd.cpp
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@@ -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)