/**
 * @file methods/kernel_pca/kernel_rules/naive_method.hpp
 * @author Ajinkya Kale
 *
 * Use the naive method to construct the kernel matrix.
 *
 * 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_KERNEL_PCA_NAIVE_METHOD_HPP
#define MLPACK_METHODS_KERNEL_PCA_NAIVE_METHOD_HPP

#include <mlpack/prereqs.hpp>

namespace mlpack {

template<typename KernelType>
class NaiveKernelRule
{
 public:
  /**
   * Construct the exact kernel matrix.
   *
   * @param data Input data points.
   * @param transformedData Matrix to output results into.
   * @param eigval KPCA eigenvalues will be written to this vector.
   * @param eigvec KPCA eigenvectors will be written to this matrix.
   * @param * (rank) Rank to be used for matrix approximation.
   * @param kernel Kernel to be used for computation.
   */
  static void ApplyKernelMatrix(const arma::mat& data,
                                arma::mat& transformedData,
                                arma::vec& eigval,
                                arma::mat& eigvec,
                                const size_t /* rank */,
                                KernelType kernel = KernelType())
{
  // Construct the kernel matrix.
  arma::mat kernelMatrix;
  // Resize the kernel matrix to the right size.
  kernelMatrix.set_size(data.n_cols, data.n_cols);

  // Note that we only need to calculate the upper triangular part of the
  // kernel matrix, since it is symmetric. This helps minimize the number of
  // kernel evaluations.
  for (size_t i = 0; i < data.n_cols; ++i)
  {
    for (size_t j = i; j < data.n_cols; ++j)
    {
      // Evaluate the kernel on these two points.
      kernelMatrix(i, j) = kernel.Evaluate(data.unsafe_col(i),
                                           data.unsafe_col(j));
    }
  }

  // Copy to the lower triangular part of the matrix.
  for (size_t i = 1; i < data.n_cols; ++i)
    for (size_t j = 0; j < i; ++j)
      kernelMatrix(i, j) = kernelMatrix(j, i);

  // For PCA the data has to be centered, even if the data is centered. But it
  // is not guaranteed that the data, when mapped to the kernel space, is also
  // centered. Since we actually never work in the feature space we cannot
  // center the data. So, we perform a "psuedo-centering" using the kernel
  // matrix.
  arma::rowvec rowMean = arma::sum(kernelMatrix, 0) / kernelMatrix.n_cols;
  kernelMatrix.each_col() -= arma::sum(kernelMatrix, 1) / kernelMatrix.n_cols;
  kernelMatrix.each_row() -= rowMean;
  kernelMatrix += arma::sum(rowMean) / kernelMatrix.n_cols;

  // Eigendecompose the centered kernel matrix.
  kernelMatrix = arma::symmatu(kernelMatrix);
  if (!arma::eig_sym(eigval, eigvec, kernelMatrix))
  {
    Log::Fatal << "Failed to construct the kernel matrix." << std::endl;
  }

  // Swap the eigenvalues since they are ordered backwards (we need largest to
  // smallest).
  for (size_t i = 0; i < floor(eigval.n_elem / 2.0); ++i)
    eigval.swap_rows(i, (eigval.n_elem - 1) - i);

  // Flip the coefficients to produce the same effect.
  eigvec = arma::fliplr(eigvec);

  transformedData = eigvec.t() * kernelMatrix;
  transformedData.each_col() /= arma::sqrt(eigval);
}
};

} // namespace mlpack

#endif
