API Reference

fastloess-wasm

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fastloess-wasm / SmoothOptions

Interface: SmoothOptions

Defined in: fastloess_wasm.d.ts:6

Properties

auto_converge?

optional auto_converge?: number

Defined in: fastloess_wasm.d.ts:27

Auto-convergence tolerance. Disabled when absent.

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boundary_degree_fallback?

optional boundary_degree_fallback?: boolean

Defined in: fastloess_wasm.d.ts:45

Fall back to lower polynomial degree at boundaries. Default: true.

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boundary_policy?

optional boundary_policy?: string

Defined in: fastloess_wasm.d.ts:23

Boundary handling (“extend”, “reflect”, “zero”, “noboundary”). Default: “extend”.

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cell?

optional cell?: number

Defined in: fastloess_wasm.d.ts:41

Cell parameter for interpolation (fraction of data). Default: 0.2.

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cv?

optional cv?: object

Defined in: fastloess_wasm.d.ts:11

Grouped batch cross-validation configuration.

fractions

fractions: number[]

k?

optional k?: number

method?

optional method?: string

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degree?

optional degree?: string

Defined in: fastloess_wasm.d.ts:31

Polynomial degree (“constant”, “linear”, “quadratic”, “cubic”, “quartic”). Default: “linear”.

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dimensions?

optional dimensions?: number

Defined in: fastloess_wasm.d.ts:33

Number of predictor dimensions. Default: 1.

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distance_metric?

optional distance_metric?: string

Defined in: fastloess_wasm.d.ts:35

Distance metric (“normalized”, “euclidean”, “manhattan”, “chebyshev”, “minkowski:p”, “weighted”). Default: “normalized”.

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fraction?

optional fraction?: number

Defined in: fastloess_wasm.d.ts:13

Smoothing fraction (0 < fraction <= 1). Default: 0.67.

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interpolation_vertices?

optional interpolation_vertices?: number

Defined in: fastloess_wasm.d.ts:43

Number of interpolation vertices. Default: auto.

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intervals?

optional intervals?: IntervalsOptions

Defined in: fastloess_wasm.d.ts:9

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iterations?

optional iterations?: number

Defined in: fastloess_wasm.d.ts:15

Number of robustness iterations. Default: 3.

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missing?

optional missing?: string

Defined in: fastloess_wasm.d.ts:49

Policy for non-finite (NaN/Inf) values in input data (“error”, “drop”). Default: “error”.

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outputs?

optional outputs?: string[]

Defined in: fastloess_wasm.d.ts:8

Optional output components: diagnostics, residuals, weights, gradient (or derivative), se, sorted.

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parallel?

optional parallel?: boolean

Defined in: fastloess_wasm.d.ts:29

Enable parallel execution. Default: true.

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retain_model?

optional retain_model?: boolean

Defined in: fastloess_wasm.d.ts:51

Retain the fitted model’s training data, enabling LoessResult.predict(). Default: false.

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robustness_method?

optional robustness_method?: string

Defined in: fastloess_wasm.d.ts:19

Robustness method (“bisquare”, “huber”, “talwar”). Default: “bisquare”.

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scaling_method?

optional scaling_method?: string

Defined in: fastloess_wasm.d.ts:25

Scaling method (“mad”, “mar”, “mean”). Default: “mad”.

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seed?

optional seed?: number

Defined in: fastloess_wasm.d.ts:47

Non-negative safe-integer seed for cross-validation (at most Number.MAX_SAFE_INTEGER).

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surface_mode?

optional surface_mode?: string

Defined in: fastloess_wasm.d.ts:37

Surface computation mode (“interpolation” or “direct”). Default: “interpolation”.

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weight_function?

optional weight_function?: string

Defined in: fastloess_wasm.d.ts:17

Kernel function (“tricube”, “epanechnikov”, “gaussian”, “uniform”, “biweight”, “triangle”, “cosine”). Default: “tricube”.

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weighted_metric_weights?

optional weighted_metric_weights?: number[]

Defined in: fastloess_wasm.d.ts:39

Per-dimension weights for the weighted distance metric.

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zero_weight_fallback?

optional zero_weight_fallback?: string

Defined in: fastloess_wasm.d.ts:21

Fallback when all weights are zero (“use_local_mean”, “return_original”, “return_none”). Default: “use_local_mean”.