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cdd6ada582
* Type definition for the library 'regression' * Fix to match the commonJS style used by the real library
96 lines
3.0 KiB
TypeScript
96 lines
3.0 KiB
TypeScript
// Type definitions for regression 2.0
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// Project: https://github.com/Tom-Alexander/regression-js
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// Definitions by: Mattias B. Martens <https://github.com/MattiasMartens>
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// Definitions: https://github.com/MattiasMartens/DefinitelyTyped
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/**
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* [x, y]
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*/
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export type DataPoint = [number, number];
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export interface Options {
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/**
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* The number of decimal places to round to.
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* This is used to round the calculated fitting coefficients,
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* the output predictions, and the value of r^2.
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*/
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precision?: number;
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/**
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* The number of terms to solve for (and therefore
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* the number of coefficients to calculate). Only
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* relevant for polynomial fitting.
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*/
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order?: number;
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}
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export interface Result {
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/**
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* A human-readable string representation of the derived
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* formula in the form y = f(x) where f depends on the
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* fitting method used and the coefficients that were
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* calculated.
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*/
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string: string;
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/**
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* For each point (x, y) in the input data, a point
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* corresponding to the regression prediction for that
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* value of x.
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* One could use this to directly evaluate the quality
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* of the fit.
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*/
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points: ReadonlyArray<DataPoint>;
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/**
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* Function that takes an arbitrary value of x and
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* produces a coordinate representing the y-value of
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* the regression curve at that point.
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* Both the resulting x- and y-values are rounded to
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* a number of decimal places defined in the options
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* (default is 2).
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*/
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predict: (x: number) => DataPoint;
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/**
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* The generated coefficients describing the equation
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* of best fit.
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*
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* For a linear fit, the coefficients are `[a, b]` in `y = a * x + b`.
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* For an exponential fit, the coefficients are `[a, b]` in `y = a * e ^ (b * x)`.
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* For a logarithmic fit, the coefficients are `[a, b]` in `y = a + b * ln(x)`.
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* For a power fit, the coefficients are `[a, b]` in `y = a * x^b`.
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* For a polynomial fit, the coefficients are `[a0, a1, a2, ...aN]` in:
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* ```y = a0 * x ^ N + a1 * x ^ (N - 1) + ... + aN```
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* where N is the order (default 2).
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*/
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equation: number[];
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/**
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* The value of R squared, a statistical measure of the conformance of the
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* fitted curve to the input data where 1 is an exact fit and 0 is no fit
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* at all.
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*
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* This value is rounded to the number of decimal places defined by
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* the precision option (default 2).
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*/
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r2: number;
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}
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export function _round(number: number, precision: number): number;
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export function linear(
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data: ReadonlyArray<DataPoint>,
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options?: Options
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): Result;
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export function exponential(
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data: ReadonlyArray<DataPoint>,
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options?: Options
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): Result;
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export function logarithmic(
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data: ReadonlyArray<DataPoint>,
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options?: Options
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): Result;
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export function power(
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data: ReadonlyArray<DataPoint>,
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options?: Options
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): Result;
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export function polynomial(
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data: ReadonlyArray<DataPoint>,
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options?: Options
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): Result;
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