The Epanechnikov kernel, defined as.
More...
|
| EpanechnikovKernel (const double bandwidth=1.0) |
| Instantiate the Epanechnikov kernel with the given bandwidth (default 1.0). More...
|
|
template<typename VecType > |
double | ConvolutionIntegral (const VecType &a, const VecType &b) |
| Obtains the convolution integral [integral of K(||x-a||) K(||b-x||) dx] for the two vectors. More...
|
|
template<typename Vec1Type , typename Vec2Type > |
double | Evaluate (const Vec1Type &a, const Vec2Type &b) const |
| Evaluate the Epanechnikov kernel on the given two inputs. More...
|
|
double | Evaluate (const double distance) const |
| Evaluate the Epanechnikov kernel given that the distance between the two input points is known. More...
|
|
double | Normalizer (const size_t dimension) |
| Compute the normalizer of this Epanechnikov kernel for the given dimension. More...
|
|
The Epanechnikov kernel, defined as.
where
is the bandwidth the of the kernel (defaults to 1.0).
Definition at line 39 of file epanechnikov_kernel.hpp.
mlpack::kernel::EpanechnikovKernel::EpanechnikovKernel |
( |
const double |
bandwidth = 1.0 | ) |
|
|
inline |
Instantiate the Epanechnikov kernel with the given bandwidth (default 1.0).
- Parameters
-
bandwidth | Bandwidth of the kernel. |
Definition at line 47 of file epanechnikov_kernel.hpp.
template<typename VecType >
double mlpack::kernel::EpanechnikovKernel::ConvolutionIntegral |
( |
const VecType & |
a, |
|
|
const VecType & |
b |
|
) |
| |
Obtains the convolution integral [integral of K(||x-a||) K(||b-x||) dx] for the two vectors.
- Template Parameters
-
VecType | Type of vector (arma::vec, arma::spvec should be expected). |
- Parameters
-
a | First vector. |
b | Second vector. |
- Returns
- the convolution integral value.
template<typename Vec1Type , typename Vec2Type >
double mlpack::kernel::EpanechnikovKernel::Evaluate |
( |
const Vec1Type & |
a, |
|
|
const Vec2Type & |
b |
|
) |
| const |
Evaluate the Epanechnikov kernel on the given two inputs.
- Parameters
-
a | One input vector. |
b | The other input vector. |
double mlpack::kernel::EpanechnikovKernel::Evaluate |
( |
const double |
distance | ) |
const |
Evaluate the Epanechnikov kernel given that the distance between the two input points is known.
double mlpack::kernel::EpanechnikovKernel::Normalizer |
( |
const size_t |
dimension | ) |
|
Compute the normalizer of this Epanechnikov kernel for the given dimension.
- Parameters
-
dimension | Dimension to calculate the normalizer for. |
double mlpack::kernel::EpanechnikovKernel::bandwidth |
|
private |
double mlpack::kernel::EpanechnikovKernel::inverseBandwidthSquared |
|
private |
Cached value of the inverse bandwidth squared (to speed up computation).
Definition at line 90 of file epanechnikov_kernel.hpp.
The documentation for this class was generated from the following file: