mlpack-doxygen/cosine__distance_8hpp_source.html

 #ifndef MLPACK_CORE_KERNELS_COSINE_DISTANCE_HPP
 #define MLPACK_CORE_KERNELS_COSINE_DISTANCE_HPP

 #include <mlpack/prereqs.hpp>
 #include <mlpack/core/kernels/kernel_traits.hpp>

 namespace mlpack {
 namespace kernel {

 class CosineDistance
 {
  public:
   template<typename VecTypeA, typename VecTypeB>
   static double Evaluate(const VecTypeA& a, const VecTypeB& b);

   template<typename Archive>
   void Serialize(Archive& /* ar */, const unsigned int /* version */) { }
 };

 template<>
 class KernelTraits<CosineDistance>
 {
  public:
   static const bool IsNormalized = true;

   static const bool UsesSquaredDistance = false;
 };

 } // namespace kernel
 } // namespace mlpack

 // Include implementation.
 #include "cosine_distance_impl.hpp"

 #endif
mlpack::kernel::CosineDistance::Serialize
void Serialize(Archive &, const unsigned int)
Serialize the class (there&#39;s nothing to save).
Definition: cosine_distance.hpp:46

mlpack::kernel::KernelTraits
This is a template class that can provide information about various kernels.
Definition: kernel_traits.hpp:27

mlpack
Linear algebra utility functions, generally performed on matrices or vectors.
Definition: binarize.hpp:18

mlpack::kernel::CosineDistance
The cosine distance (or cosine similarity).
Definition: cosine_distance.hpp:31

prereqs.hpp
The core includes that mlpack expects; standard C++ includes and Armadillo.

mlpack::kernel::CosineDistance::Evaluate
static double Evaluate(const VecTypeA &a, const VecTypeB &b)
Computes the cosine distance between two points.

kernel_traits.hpp