generic-library/opencv
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Fixed warnings.

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This commit is contained in:
Marina Noskova
2016-02-10 19:46:24 +03:00
parent 0307dd19fd
commit 5496dedd6a
4 changed files with 30 additions and 29 deletions
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31
modules/ml/src/svmsgd.cpp
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@@ -172,7 +172,8 @@ void SVMSGDImpl::normalizeSamples(Mat &samples, Mat &average, float &multiplier)
average = Mat(1, featuresCount, samples.type());
for (int featureIndex = 0; featureIndex < featuresCount; featureIndex++)
{
average.at<float>(featureIndex) = mean(samples.col(featureIndex))[0];
Scalar scalAverage = mean(samples.col(featureIndex))[0];
average.at<float>(featureIndex) = static_cast<float>(scalAverage[0]);
}
for (int sampleIndex = 0; sampleIndex < samplesCount; sampleIndex++)
@@ -182,7 +183,7 @@ void SVMSGDImpl::normalizeSamples(Mat &samples, Mat &average, float &multiplier)
double normValue = norm(samples);
multiplier = sqrt(samples.total()) / normValue;
multiplier = static_cast<float>(sqrt(samples.total()) / normValue);
samples *= multiplier;
}
@@ -228,11 +229,11 @@ float SVMSGDImpl::calcShift(InputArray _samples, InputArray _responses) const
for (int samplesIndex = 0; samplesIndex < trainSamplesCount; samplesIndex++)
{
Mat currentSample = trainSamples.row(samplesIndex);
float dotProduct = currentSample.dot(weights_);
float dotProduct = static_cast<float>(currentSample.dot(weights_));
bool firstClass = isFirstClass(trainResponses.at<float>(samplesIndex));
int index = firstClass ? 0:1;
float signToMul = firstClass ? 1 : -1;
int index = firstClass ? 0 : 1;
float signToMul = firstClass ? 1.f : -1.f;
float curDistance = dotProduct * signToMul;
if (curDistance < distanceToClasses[index])
@@ -263,7 +264,7 @@ bool SVMSGDImpl::train(const Ptr<TrainData>& data, int)
if ( areEmpty.first || areEmpty.second )
{
weights_ = Mat::zeros(1, featureCount, CV_32F);
shift_ = areEmpty.first ? -1 : 1;
shift_ = areEmpty.first ? -1.f : 1.f;
return true;
}
@@ -329,7 +330,7 @@ bool SVMSGDImpl::train(const Ptr<TrainData>& data, int)
if (params.marginType == SOFT_MARGIN)
{
shift_ = extendedWeights.at<float>(featureCount) - weights_.dot(average);
shift_ = extendedWeights.at<float>(featureCount) - static_cast<float>(weights_.dot(average));
}
else
{
@@ -363,8 +364,8 @@ float SVMSGDImpl::predict( InputArray _samples, OutputArray _results, int ) cons
for (int sampleIndex = 0; sampleIndex < nSamples; sampleIndex++)
{
Mat currentSample = samples.row(sampleIndex);
float criterion = currentSample.dot(weights_) + shift_;
results.at<float>(sampleIndex) = (criterion >= 0) ? 1 : -1;
float criterion = static_cast<float>(currentSample.dot(weights_)) + shift_;
results.at<float>(sampleIndex) = (criterion >= 0) ? 1.f : -1.f;
}
return result;
@@ -530,9 +531,9 @@ void SVMSGDImpl::setOptimalParameters(int svmsgdType, int marginType)
params.svmsgdType = SGD;
params.marginType = (marginType == SOFT_MARGIN) ? SOFT_MARGIN :
(marginType == HARD_MARGIN) ? HARD_MARGIN : ILLEGAL_MARGIN_TYPE;
params.lambda = 0.0001;
params.gamma0 = 0.05;
params.c = 1;
params.lambda = 0.0001f;
params.gamma0 = 0.05f;
params.c = 1.f;
params.termCrit = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100000, 0.00001);
break;
@@ -540,9 +541,9 @@ void SVMSGDImpl::setOptimalParameters(int svmsgdType, int marginType)
params.svmsgdType = ASGD;
params.marginType = (marginType == SOFT_MARGIN) ? SOFT_MARGIN :
(marginType == HARD_MARGIN) ? HARD_MARGIN : ILLEGAL_MARGIN_TYPE;
params.lambda = 0.00001;
params.gamma0 = 0.05;
params.c = 0.75;
params.lambda = 0.00001f;
params.gamma0 = 0.05f;
params.c = 0.75f;
params.termCrit = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100000, 0.00001);
break;