SpatialKernel

sklears_gaussian_process::spatial

Enum SpatialKernel 

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pub enum SpatialKernel {
    Spherical {
        sill: f64,
        range: f64,
        nugget: f64,
    },
    Exponential {
        sill: f64,
        range: f64,
        nugget: f64,
    },
    Gaussian {
        sill: f64,
        range: f64,
        nugget: f64,
    },
    Matern {
        sill: f64,
        range: f64,
        nugget: f64,
        nu: f64,
    },
    Power {
        scale: f64,
        exponent: f64,
    },
    Linear {
        slope: f64,
        nugget: f64,
    },
    HoleEffect {
        sill: f64,
        range: f64,
        nugget: f64,
        damping: f64,
    },
    Anisotropic {
        base_kernel: Box<SpatialKernel>,
        anisotropy_matrix: Array2<f64>,
    },
}
Expand description

Spatial kernel types for geostatistical modeling

Variants§

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Spherical

Spherical model: commonly used in geostatistics

Fields

§sill: f64
§range: f64
§nugget: f64
§

Exponential

Exponential model: smooth spatial correlation

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§sill: f64
§range: f64
§nugget: f64
§

Gaussian

Gaussian model: very smooth spatial correlation

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§sill: f64
§range: f64
§nugget: f64
§

Matern

Matérn model: flexible smoothness parameter

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§sill: f64
§range: f64
§nugget: f64
§nu: f64
§

Power

Power model: unbounded variance

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§scale: f64
§exponent: f64
§

Linear

Linear model: linear increase with distance

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§slope: f64
§nugget: f64
§

HoleEffect

Hole effect model: oscillatory correlation

Fields

§sill: f64
§range: f64
§nugget: f64
§damping: f64
§

Anisotropic

Anisotropic kernel with directional correlation

Fields

§base_kernel: Box<SpatialKernel>
§anisotropy_matrix: Array2<f64>

Implementations§

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impl SpatialKernel

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pub fn spherical(sill: f64, range: f64) -> Self

Create a spherical spatial kernel

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pub fn spherical_with_nugget(sill: f64, range: f64, nugget: f64) -> Self

Create a spherical kernel with nugget effect

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pub fn exponential(sill: f64, range: f64) -> Self

Create an exponential spatial kernel

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pub fn gaussian(sill: f64, range: f64) -> Self

Create a Gaussian spatial kernel

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pub fn matern(sill: f64, range: f64, nu: f64) -> Self

Create a Matérn spatial kernel

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pub fn anisotropic( base_kernel: SpatialKernel, anisotropy_matrix: Array2<f64>, ) -> Self

Create an anisotropic kernel

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pub fn compute_covariance(&self, distance: f64) -> f64

Compute the covariance for a given distance

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pub fn compute_variogram(&self, distance: f64) -> f64

Compute the semi-variogram for a given distance

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pub fn effective_range(&self) -> f64

Get the effective range of the kernel

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pub fn sill(&self) -> f64

Get the sill (maximum variance) of the kernel

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pub fn nugget(&self) -> f64

Get the nugget effect

Trait Implementations§

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impl Clone for SpatialKernel

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fn clone(&self) -> SpatialKernel

Returns a duplicate of the value. Read more
1.0.0 · Source§

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more
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impl Debug for SpatialKernel

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fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter. Read more
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impl Kernel for SpatialKernel

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fn compute_kernel_matrix( &self, x1: &Array2<f64>, x2: Option<&Array2<f64>>, ) -> SklResult<Array2<f64>>

Compute the kernel matrix between X1 and X2
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fn clone_box(&self) -> Box<dyn Kernel>

Clone the kernel
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fn kernel(&self, x1: &ArrayView1<'_, f64>, x2: &ArrayView1<'_, f64>) -> f64

Compute the kernel between two points
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fn get_params(&self) -> Vec<f64>

Get hyperparameters
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fn set_params(&mut self, params: &[f64]) -> SklResult<()>

Set hyperparameters
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fn compute_kernel_gradient( &self, X1: &Array2<f64>, X2: Option<&Array2<f64>>, ) -> SklResult<Vec<Array2<f64>>>

Compute the gradient of the kernel matrix with respect to hyperparameters Returns a vector of gradient matrices, one for each hyperparameter
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fn kernel_gradient( &self, x1: &ArrayView1<'_, f64>, x2: &ArrayView1<'_, f64>, ) -> SklResult<Vec<f64>>

Compute the gradient of the kernel function with respect to hyperparameters for a single pair of points

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