remove epanechnikov_kernel

Signed-off-by: Omar Shrit <omar@shrit.me>

src/mlpack/core/kernels/epanechnikov_kernel.cpp

@@ -14,67 +14,4 @@
 using namespace mlpack;
 using namespace mlpack::kernel;
 
-/**
- * Compute the normalizer of this Epanechnikov kernel for the given dimension.
- *
- * @param dimension Dimension to calculate the normalizer for.
- */
-double EpanechnikovKernel::Normalizer(const size_t dimension)
-{
-  return 2.0 * pow(bandwidth, (double) dimension) *
-      std::pow(M_PI, dimension / 2.0) /
-      (std::tgamma(dimension / 2.0 + 1.0) * (dimension + 2.0));
-}
 
-/**
- * Evaluate the kernel not for two points but for a numerical value.
- */
-double EpanechnikovKernel::Evaluate(const double distance) const
-{
-  return std::max(0.0, 1 - std::pow(distance, 2.0) * inverseBandwidthSquared);
-}
-
-/**
- * Evaluate gradient of the kernel not for two points
- * but for a numerical value.
- */
-double EpanechnikovKernel::Gradient(const double distance) const
-{
-  if (std::abs(bandwidth) < std::abs(distance))
-  {
-    return 0;
-  }
-  else if (std::abs(bandwidth) > std::abs(distance))
-  {
-    return -2 * inverseBandwidthSquared * distance;
-  }
-  else
-  {
-    // The gradient doesn't exist.
-    return arma::datum::nan;
-  }
-}
-
-/**
- * Evaluate gradient of the kernel not for two points
- * but for a numerical value.
- */
-double EpanechnikovKernel::GradientForSquaredDistance(const double
-                                                  distanceSquared) const
-{
-  double bandwidthSquared = bandwidth * bandwidth;
-  if (distanceSquared < bandwidthSquared)
-  {
-    return -1 * inverseBandwidthSquared;
-  }
-  else if (distanceSquared > bandwidthSquared &&
-             distanceSquared >= 0)
-  {
-    return  0;
-  }
-  else
-  {
-    // The gradient doesn't exist.
-    return arma::datum::nan;
-  }
-}