| Safe Haskell | None |
|---|
HLearn.Models.Distributions.Univariate.KernelDensityEstimator
Description
Kernel Density Estimation (KDE) is a generic and powerful method for estimating a probability distribution. See wikipedia for more information: http://en.wikipedia.org/wiki/Kernel_density_estimation
- newtype KDE kernel h prob dp = KDE {
- freemod :: SortedVector dp
Documentation
newtype KDE kernel h prob dp Source
The KDE type is implemented as an isomorphism with the FreeModule
Constructors
| KDE | |
Fields
| |
Instances
| Functor (KDE kernel h prob) | |
| Eq dp => Eq (KDE kernel h prob dp) | |
| Ord dp => Ord (KDE kernel h prob dp) | |
| Read dp => Read (KDE kernel h prob dp) | |
| Show dp => Show (KDE kernel h prob dp) | |
| Ord dp => Monoid (KDE kernel h prob dp) | |
| (Num prob, NumDP (SortedVector dp)) => NumDP (KDE kernel h prob dp) | |
| (Num prob, Ord prob) => HomTrainer (KDE kernel h prob prob) | |
| Num (Ring (SortedVector dp)) => HasRing (KDE kernel h prob dp) | |
| Ord dp => Abelian (KDE kernel h prob dp) | |
| (Ord dp, Invertible dp) => Group (KDE kernel h prob dp) | |
| NFData dp => NFData (KDE kernel h prob dp) | |
| (Kernel kernel prob, SingI Nat h, Fractional prob, ~ * prob (Ring (SortedVector prob)), NumDP (SortedVector prob)) => PDF (KDE kernel h prob prob) | |
| Probabilistic (KDE kernel h prob dp) |