本文整理汇总了C++中typenamestd::vector::Size方法的典型用法代码示例。如果您正苦于以下问题：C++ vector::Size方法的具体用法？C++ vector::Size怎么用？C++ vector::Size使用的例子？那么恭喜您, 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typenamestd::vector的用法示例。
在下文中一共展示了vector::Size方法的1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: if
inline boost::shared_ptr< Base > Construct(
std::vector< t_OriginalVectorType >& vectors,
std::vector< double >& labels,
unsigned int const trainingSize,
std::vector< std::string > const& kernelParameters,
unsigned int const cacheSize = 0
)
{
uint64_t sparseSize = 0;
if ( t_Traits::SPARSE_ALLOWED ) {
typename std::vector< SparseVector< t_Type > >::const_iterator ii = vectors.begin();
typename std::vector< SparseVector< t_Type > >::const_iterator iiEnd = vectors.end();
for ( ; ii != iiEnd; ++ii )
sparseSize += ii->Size();
}
uint64_t spanSize = 0;
if ( t_Traits::SPAN_ALLOWED ) {
typename std::vector< SparseVector< t_Type > >::const_iterator ii = vectors.begin();
typename std::vector< SparseVector< t_Type > >::const_iterator iiEnd = vectors.end();
for ( ; ii != iiEnd; ++ii )
spanSize += SpanVector< t_Type >( *ii ).Size();
}
uint64_t denseSize = 0;
{ typename std::vector< SparseVector< t_Type > >::const_iterator ii = vectors.begin();
typename std::vector< SparseVector< t_Type > >::const_iterator iiEnd = vectors.end();
for ( ; ii != iiEnd; ++ii )
denseSize += DenseVector< t_Type >( *ii ).Size();
}
boost::shared_ptr< Base > result;
if (
t_Traits::SPARSE_ALLOWED &&
( ( ! t_Traits::SPAN_ALLOWED ) || ( sparseSize <= spanSize ) ) &&
( sparseSize <= denseSize )
)
{
result = boost::shared_ptr< Base >( new t_KernelType< SparseVector< t_Type >, t_Traits >( vectors, labels, trainingSize, kernelParameters, cacheSize ) );
}
else if (
t_Traits::SPAN_ALLOWED &&
( spanSize <= denseSize )
)
{
result = boost::shared_ptr< Base >( new t_KernelType< SpanVector< t_Type >, t_Traits >( vectors, labels, trainingSize, kernelParameters, cacheSize ) );
}
else {
result = boost::shared_ptr< Base >( new t_KernelType< DenseVector< t_Type >, t_Traits >( vectors, labels, trainingSize, kernelParameters, cacheSize ) );
}
return result;
}