generic-library/opencv
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Delete function areClassesEmpty().

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This commit is contained in:
Marina Noskova
2016-02-25 15:31:07 +03:00
parent 068677ad50
commit 74c87a26a5
4 changed files with 12 additions and 46 deletions
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48
modules/ml/src/svmsgd.cpp
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@@ -99,8 +99,6 @@ public:
private:
void updateWeights(InputArray sample, bool isPositive, float stepSize, Mat &weights);
std::pair<bool,bool> areClassesEmpty(Mat responses);
void writeParams( FileStorage &fs ) const;
void readParams( const FileNode &fn );
@@ -138,26 +136,6 @@ Ptr<SVMSGD> SVMSGD::create()
return makePtr<SVMSGDImpl>();
}
std::pair<bool,bool> SVMSGDImpl::areClassesEmpty(Mat responses)
{
CV_Assert(responses.cols == 1 || responses.rows == 1);
std::pair<bool,bool> emptyInClasses(true, true);
int limitIndex = responses.rows;
for(int index = 0; index < limitIndex; index++)
{
if (isPositive(responses.at<float>(index)))
emptyInClasses.first = false;
else
emptyInClasses.second = false;
if (!emptyInClasses.first && ! emptyInClasses.second)
break;
}
return emptyInClasses;
}
void SVMSGDImpl::normalizeSamples(Mat &samples, Mat &average, float &multiplier)
{
int featuresCount = samples.cols;
@@ -248,16 +226,20 @@ bool SVMSGDImpl::train(const Ptr<TrainData>& data, int)
int featureCount = trainSamples.cols;
Mat trainResponses = data->getTrainResponses(); // (trainSamplesCount x 1) matrix
std::pair<bool,bool> areEmpty = areClassesEmpty(trainResponses);
CV_Assert(trainResponses.rows == trainSamples.rows);
if ( areEmpty.first && areEmpty.second )
if (trainResponses.empty())
{
return false;
}
if ( areEmpty.first || areEmpty.second )
int positiveCount = countNonZero(trainResponses >= 0);
int negativeCount = countNonZero(trainResponses < 0);
if ( positiveCount <= 0 || negativeCount <= 0 )
{
weights_ = Mat::zeros(1, featureCount, CV_32F);
shift_ = areEmpty.first ? -1.f : 1.f;
shift_ = (positiveCount > 0) ? 1.f : -1.f;
return true;
}
@@ -340,7 +322,7 @@ float SVMSGDImpl::predict( InputArray _samples, OutputArray _results, int ) cons
int nSamples = samples.rows;
cv::Mat results;
CV_Assert( samples.cols == weights_.cols && samples.type() == CV_32F );
CV_Assert( samples.cols == weights_.cols && samples.type() == CV_32FC1);
if( _results.needed() )
{
@@ -498,17 +480,7 @@ void SVMSGDImpl::clear()
SVMSGDImpl::SVMSGDImpl()
{
clear();
params.svmsgdType = -1;
params.marginType = -1;
// Parameters for learning
params.marginRegularization = 0; // regularization
params.initialStepSize = 0; // learning rate (ideally should be large at beginning and decay each iteration)
params.stepDecreasingPower = 0;
TermCriteria _termCrit(TermCriteria::COUNT + TermCriteria::EPS, 0, 0);
params.termCrit = _termCrit;
setOptimalParameters();
}
void SVMSGDImpl::setOptimalParameters(int svmsgdType, int marginType)