Struct GaussianND 

pub struct GaussianND { /* private fields */ }

Axis-aligned D-dimensional Gaussian peak (n_dims() == d).

f(x) = A · exp( −Σ_{i=0}^{D-1} (x_i − center_i)² / (2·σ_i²) )

Parameters (in order): [amplitude, center_0 … center_{D-1}, sigma_0 … sigma_{D-1}] — length 1 + 2D. Indexed (not x/y/z) so the naming scales to arbitrary N.

Implementations

impl GaussianND

pub fn new(d: usize) -> Self

Construct a D-dimensional Gaussian. d comes from the node’s explicit n_dims field (the compiler passes it in).

Trait Implementations

impl Model for GaussianND

fn eval(&self, x: &[f64], params: &[f64]) -> f64

Evaluate the model at coordinate x with the given params.

fn jacobian(&self, x: &[f64], params: &[f64]) -> Vec<f64>

Jacobian — one derivative per parameter, in the same order as param_names().

fn jacobian_into(&self, x: &[f64], params: &[f64], out: &mut [f64])

Fill a pre-allocated slice with Jacobian values (one entry per parameter).

fn param_names(&self) -> Vec<Cow<'static, str>>

Ordered parameter names, matching the layout expected by eval and jacobian.

fn n_dims(&self) -> usize

Number of coordinate dimensions consumed from x. Defaults to 1.

fn eval_slice_into(&self, xs: &[f64], params: &[f64], out: &mut [f64])

Batch evaluation of a 1-D model: fill out[i] = eval([xs[i]], params).

fn jac_slice_into(&self, xs: &[f64], params: &[f64], out: &mut [f64])

Batch Jacobian for a 1-D model: fill out in row-major layout [i * params.len() + j] = d(model)/d(params[j]) at xs[i].