Struct ParallelLocalPolynomialRegression 

pub struct ParallelLocalPolynomialRegression<F>
where
    F: Float + FromPrimitive + Debug + Send + Sync + 'static + PartialOrd + FloatCore,
{ /* private fields */ }

Parallel Local Polynomial Regression model

This struct extends the standard LocalPolynomialRegression with parallel computation capabilities. It uses a spatial index (KD-tree) for efficient neighbor searching and distributes work across multiple CPU cores.

Examples

use scirs2_core::ndarray::{Array1, Array2};
use scirs2_interpolate::parallel::{ParallelLocalPolynomialRegression, ParallelConfig};
use scirs2_interpolate::local::polynomial::LocalPolynomialConfig;
use scirs2_interpolate::local::mls::{WeightFunction, PolynomialBasis};

// Create sample 1D data
let x = Array1::<f64>::linspace(0.0, 10.0, 100);
let mut y = Array1::<f64>::zeros(100);
for (i, x_val) in x.iter().enumerate() {
    // y = sin(x) + noise
    y[i] = x_val.sin() + 0.1 * 0.3;
}

// Create 2D points array from 1D data
let points = x.clone().insert_axis(scirs2_core::ndarray::Axis(1));

// Configure LOESS model
let config = LocalPolynomialConfig {
    bandwidth: 0.3,
    weight_fn: WeightFunction::Gaussian,
    basis: PolynomialBasis::Quadratic,
    ..LocalPolynomialConfig::default()
};

// Create parallel LOESS model
let parallel_loess = ParallelLocalPolynomialRegression::with_config(
    points.clone(),
    y.clone(),
    config,
).unwrap();

// Create test points
let test_x = Array1::<f64>::linspace(0.0, 10.0, 50);
let testpoints = test_x.clone().insert_axis(scirs2_core::ndarray::Axis(1));

// Parallel evaluation
let parallel_config = ParallelConfig::new();
let results = parallel_loess.fit_multiple_parallel(
    &testpoints.view(),
    &parallel_config
).unwrap();

Implementations

impl<F> ParallelLocalPolynomialRegression<F>

where F: Float + FromPrimitive + Debug + Send + Sync + 'static + PartialOrd + FloatCore,

pub fn new( points: Array2<F>, values: Array1<F>, bandwidth: F, ) -> InterpolateResult<Self>

Create a new parallel local polynomial regression model

Arguments

• points - Point coordinates with shape (npoints, n_dims)
• values - Values at each point with shape (npoints,)
• bandwidth - Bandwidth parameter controlling locality

Returns

A new ParallelLocalPolynomialRegression model

pub fn with_config( points: Array2<F>, values: Array1<F>, config: LocalPolynomialConfig<F>, ) -> InterpolateResult<Self>

Create a new parallel local polynomial regression with custom configuration

Arguments

• points - Point coordinates with shape (npoints, n_dims)
• values - Values at each point with shape (npoints,)
• config - Configuration for the regression

Returns

A new ParallelLocalPolynomialRegression model

pub fn fit_at_point( &self, x: &ArrayView1<'_, F>, ) -> InterpolateResult<RegressionResult<F>>

Fit the model at a single point

Arguments

• x - Query point coordinates

Returns

Regression result at the query point

pub fn fit_multiple_parallel( &self, points: &ArrayView2<'_, F>, config: &ParallelConfig, ) -> InterpolateResult<Array1<F>>

Fit the model at multiple points in parallel

This method distributes the fitting of multiple points across available CPU cores, potentially providing significant speedup for large datasets or many query points.

Arguments

• points - Query points with shape (npoints, n_dims)
• config - Parallel execution configuration

Returns

Array of fitted values at the query points

pub fn fit_with_kdtree( &self, points: &ArrayView2<'_, F>, config: &ParallelConfig, ) -> InterpolateResult<Array1<F>>

Fit the model at multiple points using KD-tree for neighbor search

This method uses the KD-tree to efficiently find nearest neighbors for each query point, which significantly accelerates the fitting process, especially for large datasets.

Arguments

• points - Query points with shape (npoints, n_dims)
• config - Parallel execution configuration

Returns

Array of fitted values at the query points

Trait Implementations

impl<F> Clone for ParallelLocalPolynomialRegression<F>

where F: Float + FromPrimitive + Debug + Send + Sync + 'static + PartialOrd + FloatCore + Clone,

fn clone(&self) -> ParallelLocalPolynomialRegression<F>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F> Debug for ParallelLocalPolynomialRegression<F>

where F: Float + FromPrimitive + Debug + Send + Sync + 'static + PartialOrd + FloatCore + Debug,

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

Formats the value using the given formatter.

impl<F> ParallelEvaluate<F, ArrayBase<OwnedRepr<F>, Dim<[usize; 1]>>> for ParallelLocalPolynomialRegression<F>

where F: Float + FromPrimitive + Debug + Send + Sync + 'static + PartialOrd + FloatCore,

fn evaluate_parallel( &self, points: &ArrayView2<'_, F>, config: &ParallelConfig, ) -> InterpolateResult<Array1<F>>

Evaluate at multiple points in parallel