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Deleted default value for parameters in docs.

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Added some asserts.
This commit is contained in:
Marina Noskova
2016-02-25 19:12:54 +03:00
parent d484893839
commit 53711ec29d
4 changed files with 38 additions and 59 deletions
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53
modules/ml/src/svmsgd.cpp
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@@ -97,7 +97,7 @@ public:
CV_IMPL_PROPERTY_S(cv::TermCriteria, TermCriteria, params.termCrit)
private:
void updateWeights(InputArray sample, bool isPositive, float stepSize, Mat &weights);
void updateWeights(InputArray sample, bool positive, float stepSize, Mat &weights);
void writeParams( FileStorage &fs ) const;
@@ -111,8 +111,6 @@ private:
static void makeExtendedTrainSamples(const Mat &trainSamples, Mat &extendedTrainSamples, Mat &average, float &multiplier);
// Vector with SVM weights
Mat weights_;
float shift_;
@@ -263,11 +261,12 @@ bool SVMSGDImpl::train(const Ptr<TrainData>& data, int)
RNG rng(0);
CV_Assert ((params.termCrit.type & TermCriteria::COUNT || params.termCrit.type & TermCriteria::EPS) && (trainResponses.type() == CV_32FC1));
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++)
{
@@ -288,8 +287,8 @@ bool SVMSGDImpl::train(const Ptr<TrainData>& data, int)
}
else
{
err = norm(extendedWeights - previousWeights);
extendedWeights.copyTo(previousWeights);
err = norm(extendedWeights - previousWeights);
extendedWeights.copyTo(previousWeights);
}
}
@@ -316,7 +315,6 @@ bool SVMSGDImpl::train(const Ptr<TrainData>& data, int)
return true;
}
float SVMSGDImpl::predict( InputArray _samples, OutputArray _results, int ) const
{
float result = 0;
@@ -417,17 +415,6 @@ void SVMSGDImpl::writeParams( FileStorage& fs ) const
fs << "iterations" << params.termCrit.maxCount;
fs << "}";
}
void SVMSGDImpl::read(const FileNode& fn)
{
clear();
readParams(fn);
fn["weights"] >> weights_;
fn["shift"] >> shift_;
}
void SVMSGDImpl::readParams( const FileNode& fn )
{
String svmsgdTypeStr = (String)fn["svmsgdType"];
@@ -443,7 +430,7 @@ void SVMSGDImpl::readParams( const FileNode& fn )
String marginTypeStr = (String)fn["marginType"];
int marginType =
marginTypeStr == "SOFT_MARGIN" ? SOFT_MARGIN :
marginTypeStr == "HARD_MARGIN" ? HARD_MARGIN : -1;
marginTypeStr == "HARD_MARGIN" ? HARD_MARGIN : -1;
if( marginType < 0 )
CV_Error( CV_StsParseError, "Missing or invalid margin type" );
@@ -460,16 +447,22 @@ void SVMSGDImpl::readParams( const FileNode& fn )
params.stepDecreasingPower = (float)fn["stepDecreasingPower"];
FileNode tcnode = fn["term_criteria"];
if( !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);
}
else
params.termCrit = TermCriteria( TermCriteria::EPS + TermCriteria::COUNT, 100000, FLT_EPSILON );
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()
@@ -492,7 +485,7 @@ void SVMSGDImpl::setOptimalParameters(int svmsgdType, int marginType)
case SGD:
params.svmsgdType = SGD;
params.marginType = (marginType == SOFT_MARGIN) ? SOFT_MARGIN :
(marginType == HARD_MARGIN) ? HARD_MARGIN : -1;
(marginType == HARD_MARGIN) ? HARD_MARGIN : -1;
params.marginRegularization = 0.0001f;
params.initialStepSize = 0.05f;
params.stepDecreasingPower = 1.f;
@@ -502,7 +495,7 @@ void SVMSGDImpl::setOptimalParameters(int svmsgdType, int marginType)
case ASGD:
params.svmsgdType = ASGD;
params.marginType = (marginType == SOFT_MARGIN) ? SOFT_MARGIN :
(marginType == HARD_MARGIN) ? HARD_MARGIN : -1;
(marginType == HARD_MARGIN) ? HARD_MARGIN : -1;
params.marginRegularization = 0.00001f;
params.initialStepSize = 0.05f;
params.stepDecreasingPower = 0.75f;