interp

PiecewiseConstant

The PiecewiseConstant performs piecewise constant interpolation on a dataset. It approximates the value of a function by using the nearest data point in each interval. This method is useful for step-like data.

Usage example

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const m = require('mathx');
x = [1, 2, 3, 4];
y = [10, 20, 30, 40];
interp = new m.PiecewiseConstant();
interp.fit(x,y);
console.log(interp.predict(2.5)); // 30

PiecewiseLinear

The PiecewiseLinear performs piecewise linear interpolation on a dataset. It approximates the value of a function by connecting data points with straight lines. This method is useful for smooth transitions between data points.

Usage example

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const m = require('mathx');
x = [1, 2, 3, 4];
y = [10, 20, 30, 40];
interp = new m.PiecewiseLinear();
interp.fit(x,y);
console.log(interp.predict(2.5)); // 25

AkimaSpline

The AkimaSpline performs Akima spline interpolation on a dataset. This method creates a smooth curve that passes through the data points, avoiding oscillations in regions with sparse data.

Usage example

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const m = require('mathx');
x = [1, 2, 3, 4];
y = [10, 20, 30, 40];
interp = new m.AkimaSpline();
interp.fit(x,y);
console.log(interp.predict(2.5)); // 25

FritschButland

The FritschButland performs Fritsch-Butland interpolation on a dataset. This method ensures monotonicity in the interpolated values, making it suitable for datasets where preserving order is important.

Usage example

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const m = require('mathx');
x = [1, 2, 3, 4];
y = [10, 20, 30, 40];
interp = new m.FritschButland();
interp.fit(x,y);
console.log(interp.predict(2.5)); // 25

LinearRegression

The LinearRegression performs linear regression-based interpolation on a dataset. It predicts the value of a function at a given point using the best-fit line derived from the data.

Usage example

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const m = require('mathx');
x = [1, 2, 3, 4];
y = [10, 20, 30, 40];
interp = new m.LinearRegression();
interp.fit(x,y);
console.log(interp.predict(2.5)); // 25

ClampedCubic

The ClampedCubic performs linear clamped-cubic interpolation on a dataset. It predicts the value of a function at a given point using the best-fit line derived from the data.

Usage example

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const m = require('mathx');
x = [1, 2, 3, 4];
y = [10, 20, 30, 40];
interp = new m.ClampedCubic();
interp.fit(x,y);
console.log(interp.predict(2.5)); // 25

NaturalCubic

The ClampedCubic performs linear natural-cubic interpolation on a dataset. It predicts the value of a function at a given point using the best-fit line derived from the data.

Usage example

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const m = require('mathx');
x = [1, 2, 3, 4];
y = [10, 20, 30, 40];
interp = new m.NaturalCubic();
interp.fit(x,y);
console.log(interp.predict(2.5)); // 25

NotAKnotCubic

The ClampedCubic performs linear not-a-knot cubic spline interpolation on a dataset. It predicts the value of a function at a given point using the best-fit line derived from the data.

Usage example

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const m = require('mathx');
x = [1, 2, 3, 4];
y = [10, 20, 30, 40];
interp = new m.NotAKnotCubic();
interp.fit(x,y);
console.log(interp.predict(2.5)); // 25