thibauts / rbf

Radial Basis Function (RBF) interpolation

rbf

Radial Basis Function (RBF) interpolation

Builds Radial Basis Functions for input and output values of arbitrary dimensionality using standard or custom distance functions.

Installation

$ npm install rbf

Usage

var RBF = require('rbf');

var points = [
  [0, 0],
  [0, 100]
];

// values could be vectors of any dimensionality.
// The computed interpolant function will return values or vectors accordingly.
var values = [
  0.0,
  1.0
]

// RBF accepts a distance function as a third parameter :
// either one of the following strings or a custom distance function (defaults to 'linear').
//
// - linear: r
// - cubic: r**3
// - quintic: r**5
// - thin-plate: r**2 * log(r)
// - gaussian: exp(-(r/epsilon) ** 2)
// - multiquadric: sqrt((r/epsilon) ** 2 + 1)
// - inverse-multiquadric: 1 / sqrt((r/epsilon) ** 2 + 1)
//
// epsilon can be provided as a 4th parameter. Defaults to the average 
// euclidean distance between points.
//
var rbf = RBF(points, values /*, distanceFunction, epsilon */);

console.log(rbf([0, 50])); // => 0.5

Examples

Partial derivative of a gaussian, original and interpolated with 25 random samples (linear distance function).

Lena, original and interpolated with 4000 random samples (about 6% of the original pixels, linear distance function).

rbf's Issues

Not work in larger data set

When the data set is large, it will return all NaN in any position!