[mlpack-svn] r10718 - mlpack/trunk/src/mlpack/methods/kpca

[fastlab-svn at coffeetalk-1.cc.gatech.edu]

Author: ajinkya
Date: 2011-12-12 05:47:10 -0500 (Mon, 12 Dec 2011)
New Revision: 10718

Removed:
   mlpack/trunk/src/mlpack/methods/kpca/kpca.cpp
Log:


Deleted: mlpack/trunk/src/mlpack/methods/kpca/kpca.cpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/kpca/kpca.cpp	2011-12-12 10:45:30 UTC (rev 10717)
+++ mlpack/trunk/src/mlpack/methods/kpca/kpca.cpp	2011-12-12 10:47:10 UTC (rev 10718)
@@ -1,120 +0,0 @@
-/**
- * @file kpca.cpp
- * @author Ajinkya Kale
- *
- * Implementation of KPCA class to perform Kernel Principal Components Analysis on the
- * specified data set.
- */
-#include "kpca.hpp"
-#include <mlpack/core.hpp>
-#include <iostream>
-
-using namespace std;
-namespace mlpack {
-namespace kpca {
-
-template <typename KernelType>
-KPCA<KernelType>::KPCA(/*const KernelType kernel,*/
-                       const bool centerData,
-                       const bool scaleData) :
-      /*kernel_(kernel),*/
-      centerData_(centerData),
-      scaleData_(scaleData)
-{
-}
-
-/**
- * Apply Kernel Principal Component Analysis to the provided data set.
- *
- * @param data - Data matrix
- * @param transformedData - Data with KPCA applied
- * @param eigVal - contains eigen values in a column vector
- * @param coeff - KPCA Loadings/Coeffs/EigenVectors
- */
-template <typename KernelType>
-void KPCA<KernelType>::Apply(const arma::mat& data, arma::mat& transformedData,
-           arma::vec& eigVal, arma::mat& coeffs)
-{
-  arma::mat transData = trans(data);
-
-  if(centerData_)
-  {
-    arma::rowvec means = arma::mean(transData, 0);
-    transData = transData - arma::ones<arma::colvec>(transData.n_rows) * means;
-  }
-
-  arma::mat kernelMat;
-
-  for(int i = 0; i < transData.n_rows; i++)
-  {
-    for(int j = 0; j < transData.n_cols; j++)
-    {
-      kernelMat(i, j) = kernel_.Evaluate(transData.row(i), transData.col(i));
-    }
-  }
-
-  transData = kernelMat; //use the kernel-matrix to do the transformations after this point.
-
-  if(scaleData_)
-  {
-    transData = transData / (arma::ones<arma::colvec>(transData.n_rows) * stddev(transData, 0, 0));
-  }
-
-  arma::mat covMat = cov(transData);
-  arma::eig_sym(eigVal, coeffs, covMat);
-
-  int n_eigVal = eigVal.n_elem;
-  for(int i = 0; i < floor(n_eigVal / 2); i++)
-    eigVal.swap_rows(i, (n_eigVal - 1) - i);
-
-  coeffs = arma::fliplr(coeffs);
-  transformedData = trans(coeffs) * data;
-  arma::colvec transformedDataMean = arma::mean(transformedData, 1);
-  transformedData = transformedData - (transformedDataMean * arma::ones<arma::rowvec>(transformedData.n_cols));
-}
-
-/**
- * Apply Kernel Principal Component Analysis to the provided data set.
- *
- * @param data - Data matrix
- * @param transformedData - Data with KPCA applied
- * @param eigVal - contains eigen values in a column vector
- */
-template <typename KernelType>
-void KPCA<KernelType>::Apply(const arma::mat& data, arma::mat& transformedData,
-           arma::vec& eigVal)
-{
-  arma::mat coeffs;
-  Apply(data, transformedData,
-              eigVal, coeffs);
-}
-
-/**
- * Apply Dimensionality Reduction using Kernel Principal Component Analysis
- * to the provided data set.
- *
- * @param data - M x N Data matrix
- * @param newDimension - matrix consisting of N column vectors,
- * where each vector is the projection of the corresponding data vector
- * from data matrix onto the basis vectors contained in the columns of
- * coeff/eigen vector matrix with only newDimension number of columns chosen.
- */
-template <typename KernelType>
-void KPCA<KernelType>::Apply(arma::mat& data, const int newDimension)
-{
-  arma::mat coeffs;
-  arma::vec eigVal;
-
-  Apply(data, data, eigVal, coeffs);
-
-  if(newDimension < coeffs.n_rows && newDimension > 0)
-    data.shed_rows(newDimension, data.n_rows - 1);
-}
-
-/*template <typename KernelType>
-KPCA<KernelType>::~KPCA()
-{
-}*/
-
-}; // namespace mlpack
-}; // namespace kpca