Struct KernelDensity

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pub struct KernelDensity {
    pub x: Vec<f64>,
    pub y: Vec<f64>,
}

Expand description

Kernel Density Estimation using Gaussian kernel with FFT acceleration

This is a simplified implementation of R’s density() function with automatic bandwidth selection using Silverman’s rule of thumb. Uses FFT-based convolution for O(n log n) performance instead of O(n*m).

Fields§

§x: Vec<f64>

Grid points

§y: Vec<f64>

Density values

Implementations§

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impl KernelDensity

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pub fn estimate(data: &[f64], adjust: f64, n_points: usize) -> KdeResult<Self>

Compute kernel density estimate using FFT-based convolution

§Arguments

data - Input data
adjust - Bandwidth adjustment factor (default: 1.0)
n_points - Number of grid points (default: 512)

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pub fn find_peaks(&self, peak_removal: f64) -> Vec<f64>

Find local maxima (peaks) in the density estimate

§Arguments

peak_removal - Minimum peak height as fraction of max density

§Returns

Vector of x-coordinates where peaks occur

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pub fn density_at(&self, x: f64) -> f64

Get density value at a specific point using linear interpolation

§Arguments

x - The point at which to evaluate the density

§Returns

The interpolated density value, or 0.0 if x is outside the grid range

Auto Trait Implementations§

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impl UnwindSafe for KernelDensity

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