Interface KernelDensityFunction
-
- All Known Implementing Classes:
BiweightKernelDensityFunction
,CosineKernelDensityFunction
,EpanechnikovKernelDensityFunction
,GaussianKernelDensityFunction
,TriangularKernelDensityFunction
,TricubeKernelDensityFunction
,TriweightKernelDensityFunction
,UniformKernelDensityFunction
public interface KernelDensityFunction
Inner function of a kernel density estimator.Note: as of now, this API does not support asymmetric kernels, which would be difficult in the multivariate case.
- Since:
- 0.6.0
- Author:
- Erich Schubert
-
-
Method Summary
All Methods Instance Methods Abstract Methods Modifier and Type Method Description double
canonicalBandwidth()
Get the canonical bandwidth for this kernel.double
density(double delta)
Density contribution of a point at the given relative distancedelta >= 0
.double
getR()
Get the R integral of the kernel, \int K^2(x) dxdouble
standardDeviation()
Get the standard deviation of the kernel function.
-
-
-
Method Detail
-
density
double density(double delta)
Density contribution of a point at the given relative distancedelta >= 0
.Note that for
delta < 0
, in particular fordelta < 1
, the results may become invalid. So usually, you will want to invoke this as:kernel.density(Math.abs(delta))
- Parameters:
delta
- Relative distance- Returns:
- density contribution
-
canonicalBandwidth
@Reference(authors="J. S. Marron, D. Nolan", title="Canonical kernels for density estimation", booktitle="Statistics & Probability Letters, Volume 7, Issue 3", url="https://doi.org/10.1016/0167-7152(88)90050-8", bibkey="doi:10.1016/0167-71528890050-8") double canonicalBandwidth()
Get the canonical bandwidth for this kernel.Note: R uses a different definition of "canonical bandwidth", and also uses differently scaled kernels.
- Returns:
- Canonical bandwidth
-
standardDeviation
double standardDeviation()
Get the standard deviation of the kernel function.- Returns:
- Standard deviation
-
getR
double getR()
Get the R integral of the kernel, \int K^2(x) dxTODO: any better name for this?
- Returns:
- R value
-
-