[mlpack-svn] r11617 - mlpack/trunk/src/mlpack/core/kernels

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

Author: rcurtin
Date: 2012-02-27 23:07:39 -0500 (Mon, 27 Feb 2012)
New Revision: 11617

Removed:
   mlpack/trunk/src/mlpack/core/kernels/exponential_kernel.hpp
Log:
This is a copy of the Gaussian kernel... oops...


Deleted: mlpack/trunk/src/mlpack/core/kernels/exponential_kernel.hpp
===================================================================
--- mlpack/trunk/src/mlpack/core/kernels/exponential_kernel.hpp	2012-02-28 02:29:49 UTC (rev 11616)
+++ mlpack/trunk/src/mlpack/core/kernels/exponential_kernel.hpp	2012-02-28 04:07:39 UTC (rev 11617)
@@ -1,92 +0,0 @@
-/**
- * @file exponential_kernel.hpp
- * @author Ajinkya Kale <kaleajinkya at gmail.com>
- *
- * Implementation of the Exponential kernel (ExponentialKernel).
- */
-#ifndef __MLPACK_CORE_KERNELS_EXPONENTIAL_KERNEL_HPP
-#define __MLPACK_CORE_KERNELS_EXPONENTIAL_KERNEL_HPP
-
-#include <mlpack/core.hpp>
-
-namespace mlpack {
-namespace kernel {
-
-/**
- * The standard Exponential kernel.  Given two vectors @f$ x @f$, @f$ y @f$, and a
- * bandwidth @f$ \mu @f$ (set in the constructor),
- *
- * @f[
- * K(x, y) = \exp(-\frac{|| x - y ||}{2 \mu^2}).
- * @f]
- *
- * The implementation is all in the header file because it is so simple.
- */
-class ExponentialKernel
-{
- public:
-  /**
-   * Default constructor; sets bandwidth to 1.0.
-   */
-  ExponentialKernel() : bandwidth(1.0), gamma(-0.5)
-  { }
-
-  /**
-   * Construct the Exponential kernel with a custom bandwidth.
-   *
-   * @param bandwidth The bandwidth of the kernel (@f$\mu at f$).
-   */
-  ExponentialKernel(double bandwidth) :
-    bandwidth(bandwidth),
-    gamma(-0.5 * pow(bandwidth, -2.0))
-  { }
-
-  /**
-   * Evaluation of the Exponential kernel.  This could be generalized to use any
-   * distance metric, not the Euclidean distance, but for now, the Euclidean
-   * distance is used.
-   *
-   * @tparam VecType Type of vector (likely arma::vec or arma::spvec).
-   * @param a First vector.
-   * @param b Second vector.
-   * @return K(a, b) using the bandwidth (@f$\mu at f$) specified in the
-   *   constructor.
-   */
-  template<typename VecType>
-  double Evaluate(const VecType& a, const VecType& b) const
-  {
-    // The precalculation of gamma saves us a little computation time.
-    return exp(gamma * sqrt(metric::SquaredEuclideanDistance::Evaluate(a, b)));
-  }
-
-  /**
-   * Evaluation of the Exponential kernel using a double precision argument.
-   *
-   * @param t double value.
-   * @return K(t) using the bandwidth (@f$\mu at f$) specified in the
-   *     constructor.
-   */
-  double Evaluate(double t) const
-  {
-    // The precalculation of gamma saves us a little computation time.
-    return exp(gamma * t);
-  }
-
-  //! Get the bandwidth.
-  const double& Bandwidth() const { return bandwidth; }
-  //! Get the precalculated constant.
-  const double& Gamma() const { return gamma; }
-
- private:
-  //! Kernel bandwidth.
-  double bandwidth;
-
-  //! Precalculated constant depending on the bandwidth;
-  //! @f$ \gamma = -\frac{1}{2 \mu^2} @f$.
-  double gamma;
-};
-
-}; // namespace kernel
-}; // namespace mlpack
-
-#endif