@mytskine/curve-fitting

Curve fitting
=============

Given some points in R×R and a parametered function f_p : R → R,
find an optimal set of parameters so that f "fits" the points.

E.g. given some points (x_i, y_i) and the model function f(p1, p2)(x) = p1 * x² + p2,
find p=[p1, p2] so that the least squares error Σ (f(p)(x_i) - y_i)² is minimal.

The only algorithm available in this library is Levenberg-Marquardt.
It is suitable for non-linear least-square approximation.
See for implementation details.
Other algorithms may be implemented later.

For examples and benchmarks :

Usage
-----

$3

In a JS web code, load build/web/curve-fitting.js.
This file is in each release and in the dist branch.
You can build the latest version yourself from the master branch.

In a JS Node code, require build/node/curve-fitting.js.

In both cases, an alternative is to import the module @mytskine/curve-fitting.

In a TypeScript code, you can import src/main.ts.

$3

The parameters and the result are documented alongside the code.
The default values should cover most needs.

Web (declares a global variable CurveFitting)
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Module (web)
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