本文整理汇总了C++中GMatrix::heap方法的典型用法代码示例。如果您正苦于以下问题：C++ GMatrix::heap方法的具体用法？C++ GMatrix::heap怎么用？C++ GMatrix::heap使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类GMatrix的用法示例。
在下文中一共展示了GMatrix::heap方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: splitClass
void splitClass(GArgReader& args)
{
const char* filename = args.pop_string();
GMatrix* pData = loadData(filename);
Holder<GMatrix> hData(pData);
size_t classAttr = args.pop_uint();
bool dropClass = false;
while(args.size() > 0)
{
if(args.if_pop("-dropclass"))
dropClass = true;
else
ThrowError("Invalid option: ", args.peek());
}
for(size_t i = 0; i < pData->relation()->valueCount(classAttr); i++)
{
GMatrix tmp(pData->relation(), pData->heap());
pData->splitByNominalValue(&tmp, classAttr, i);
std::ostringstream oss;
PathData pd;
GFile::parsePath(filename, &pd);
string fn;
fn.assign(filename + pd.fileStart, pd.extStart - pd.fileStart);
oss << fn << "_";
pData->relation()->printAttrValue(oss, classAttr, (double)i);
oss << ".arff";
string s = oss.str();
if(dropClass)
tmp.deleteColumn(classAttr);
tmp.saveArff(s.c_str());
}
}示例2: trainInnerInner
// virtual
void GBag::trainInnerInner(GMatrix& features, GMatrix& labels)
{
// Train all the models
size_t nLearnerCount = m_models.size();
size_t nDrawSize = size_t(m_trainSize * features.rows());
GMatrix drawnFeatures(features.relation(), features.heap());
GMatrix drawnLabels(labels.relation(), labels.heap());
drawnFeatures.reserve(nDrawSize);
drawnLabels.reserve(nDrawSize);
{
for(size_t i = 0; i < nLearnerCount; i++)
{
if(m_pCB)
m_pCB(m_pThis, i, nLearnerCount);
// Randomly draw some data (with replacement)
GReleaseDataHolder hDrawnFeatures(&drawnFeatures);
GReleaseDataHolder hDrawnLabels(&drawnLabels);
for(size_t j = 0; j < nDrawSize; j++)
{
size_t r = (size_t)m_rand.next(features.rows());
drawnFeatures.takeRow(features[r]);
drawnLabels.takeRow(labels[r]);
}
// Train the learner with the drawn data
m_models[i]->m_pModel->train(drawnFeatures, drawnLabels);
}
if(m_pCB)
m_pCB(m_pThis, nLearnerCount, nLearnerCount);
}
// Determine the weights
determineWeights(features, labels);
normalizeWeights();
}