Namespaces

Types in MathNet.Numerics

Type Fit

Namespace MathNet.Numerics

Least-Squares Curve Fitting Routines

Static Functions

Public Static Functions

Tuple<double, double> Line(Double[] x, Double[] y)

Least-Squares fitting the points (x,y) to a line y : x -> a+b*x, returning its best fitting parameters as [a, b] array, where a is the intercept and b the slope.

Double[] LinearCombination(Double[] x, Double[] y, Func`2[] functions)

Double[] LinearCombination(Double[] x, Double[] y, DirectRegressionMethod method, Func`2[] functions)

Func<double, double> LinearCombinationFunc(Double[] x, Double[] y, DirectRegressionMethod method, Func`2[] functions)

Func<double, double> LinearCombinationFunc(Double[] x, Double[] y, Func`2[] functions)

Double[] LinearGeneric<T>(T[] x, Double[] y, DirectRegressionMethod method, Func`2[] functions)

Double[] LinearGeneric<T>(T[] x, Double[] y, Func`2[] functions)

Func<T, double> LinearGenericFunc<T>(T[] x, Double[] y, DirectRegressionMethod method, Func`2[] functions)

Func<T, double> LinearGenericFunc<T>(T[] x, Double[] y, Func`2[] functions)

Double[] LinearMultiDim(Double[][] x, Double[] y, Func`2[] functions)

Double[] LinearMultiDim(Double[][] x, Double[] y, DirectRegressionMethod method, Func`2[] functions)

Func<Double[], double> LinearMultiDimFunc(Double[][] x, Double[] y, DirectRegressionMethod method, Func`2[] functions)

Func<Double[], double> LinearMultiDimFunc(Double[][] x, Double[] y, Func`2[] functions)

Func<double, double> LineFunc(Double[] x, Double[] y)

Least-Squares fitting the points (x,y) to a line y : x -> a+b*x, returning a function y' for the best fitting line.

Double[] MultiDim(Double[][] x, Double[] y, bool intercept, DirectRegressionMethod method)

Func<Double[], double> MultiDimFunc(Double[][] x, Double[] y, bool intercept, DirectRegressionMethod method)

Double[] MultiDimWeighted(Double[][] x, Double[] y, Double[] w)

Double[] Polynomial(Double[] x, Double[] y, int order, DirectRegressionMethod method)

Least-Squares fitting the points (x,y) to a k-order polynomial y : x -> p0 + p1*x + p2*x^2 +... + pk*x^k, returning its best fitting parameters as [p0, p1, p2,..., pk] array, compatible with Evaluate.Polynomial. A polynomial with order/degree k has (k+1) coefficients and thus requires at least (k+1) samples.

Func<double, double> PolynomialFunc(Double[] x, Double[] y, int order, DirectRegressionMethod method)

Least-Squares fitting the points (x,y) to a k-order polynomial y : x -> p0 + p1*x + p2*x^2 +... + pk*x^k, returning a function y' for the best fitting polynomial. A polynomial with order/degree k has (k+1) coefficients and thus requires at least (k+1) samples.

Double[] PolynomialWeighted(Double[] x, Double[] y, Double[] w, int order)

Weighted Least-Squares fitting the points (x,y) and weights w to a k-order polynomial y : x -> p0 + p1*x + p2*x^2 +... + pk*x^k, returning its best fitting parameters as [p0, p1, p2,..., pk] array, compatible with Evaluate.Polynomial. A polynomial with order/degree k has (k+1) coefficients and thus requires at least (k+1) samples.