/**
 * @file core/metrics/ip_metric.hpp
 * @author Ryan Curtin
 *
 * Inner product induced metric.  If given a kernel function, this gives the
 * complementary metric.
 *
 * mlpack is free software; you may redistribute it and/or modify it under the
 * terms of the 3-clause BSD license.  You should have received a copy of the
 * 3-clause BSD license along with mlpack.  If not, see
 * http://www.opensource.org/licenses/BSD-3-Clause for more information.
 */
#ifndef MLPACK_METHODS_FASTMKS_IP_METRIC_HPP
#define MLPACK_METHODS_FASTMKS_IP_METRIC_HPP

namespace mlpack {

/**
 * The inner product metric, IPMetric, takes a given Mercer kernel (KernelType),
 * and when Evaluate() is called, returns the distance between the two points in
 * kernel space:
 *
 * @f[
 * d(x, y) = \sqrt{ K(x, x) + K(y, y) - 2K(x, y) }.
 * @f]
 *
 * @tparam KernelType Type of Kernel to use.  This must be a Mercer kernel
 *     (positive definite), otherwise the metric may not be valid.
 */
template<typename KernelType>
class IPMetric
{
 public:
  //! Create the IPMetric without an instantiated kernel.
  IPMetric();

  //! Create the IPMetric with an instantiated kernel.
  IPMetric(KernelType& kernel);

  //! Destroy the IPMetric object.
  ~IPMetric();

  //! Copy the parameters of the given metric.
  IPMetric(const IPMetric& other);

  //! Assign this metric to be a copy of the given metric.
  IPMetric& operator=(const IPMetric& other);

  /**
   * Evaluate the metric.
   *
   * @tparam VecTypeA Type of first vector.
   * @tparam VecTypeB Type of second vector.
   * @param a First vector.
   * @param b Second vector.
   * @return Distance between the two points in kernel space.
   */
  template<typename VecTypeA, typename VecTypeB>
  typename VecTypeA::elem_type Evaluate(const VecTypeA& a, const VecTypeB& b);

  //! Get the kernel.
  const KernelType& Kernel() const { return *kernel; }
  //! Modify the kernel.
  KernelType& Kernel() { return *kernel; }

  //! Serialize the metric.
  template<typename Archive>
  void serialize(Archive& ar, const uint32_t version);

 private:
  //! The kernel we are using.
  KernelType* kernel;
  //! If true, we are responsible for deleting the kernel.
  bool kernelOwner;
};

} // namespace mlpack

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

#endif
