Types in MathNet.Numerics
Public Static Functions
ValueTuple<double, double, double, double> Curve(Double[] x, Double[] y, Func<double, double, double, double, double, double> f, double initialGuess0, double initialGuess1, double initialGuess2, double initialGuess3, double tolerance, int maxIterations)
Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, p2, p3, x),
returning its best fitting parameter p0, p1, p2 and p3.
ValueTuple<double, double, double, double, double> Curve(Double[] x, Double[] y, Func<double, double, double, double, double, double, double> f, double initialGuess0, double initialGuess1, double initialGuess2, double initialGuess3, double initialGuess4, double tolerance, int maxIterations)
Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, p2, p3, p4, x),
returning its best fitting parameter p0, p1, p2, p3 and p4.
ValueTuple<double, double, double> Curve(Double[] x, Double[] y, Func<double, double, double, double, double> f, double initialGuess0, double initialGuess1, double initialGuess2, double tolerance, int maxIterations)
Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, p2, x),
returning its best fitting parameter p0, p1 and p2.
double Curve(Double[] x, Double[] y, Func<double, double, double> f, double initialGuess, double tolerance, int maxIterations)
Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p, x),
returning its best fitting parameter p.
ValueTuple<double, double> Curve(Double[] x, Double[] y, Func<double, double, double, double> f, double initialGuess0, double initialGuess1, double tolerance, int maxIterations)
Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, x),
returning its best fitting parameter p0 and p1.
Func<double, double> CurveFunc(Double[] x, Double[] y, Func<double, double, double, double, double, double, double> f, double initialGuess0, double initialGuess1, double initialGuess2, double initialGuess3, double initialGuess4, double tolerance, int maxIterations)
Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, p2, p3, p4, x),
returning a function y' for the best fitting curve.
Func<double, double> CurveFunc(Double[] x, Double[] y, Func<double, double, double, double, double, double> f, double initialGuess0, double initialGuess1, double initialGuess2, double initialGuess3, double tolerance, int maxIterations)
Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, p2, p3, x),
returning a function y' for the best fitting curve.
Func<double, double> CurveFunc(Double[] x, Double[] y, Func<double, double, double, double, double> f, double initialGuess0, double initialGuess1, double initialGuess2, double tolerance, int maxIterations)
Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, p2, x),
returning a function y' for the best fitting curve.
Func<double, double> CurveFunc(Double[] x, Double[] y, Func<double, double, double, double> f, double initialGuess0, double initialGuess1, double tolerance, int maxIterations)
Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, x),
returning a function y' for the best fitting curve.
Func<double, double> CurveFunc(Double[] x, Double[] y, Func<double, double, double> f, double initialGuess, double tolerance, int maxIterations)
Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p, x),
returning a function y' for the best fitting curve.
ValueTuple<double, double> Exponential(Double[] x, Double[] y, DirectRegressionMethod method)
Least-Squares fitting the points (x,y) to an exponential y : x -> a*exp(r*x),
returning its best fitting parameters as (a, r) tuple.
Func<double, double> ExponentialFunc(Double[] x, Double[] y, DirectRegressionMethod method)
Least-Squares fitting the points (x,y) to an exponential y : x -> a*exp(r*x),
returning a function y' for the best fitting line.
ValueTuple<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) tuple,
where a is the intercept and b the slope.
Double[] LinearCombination(Double[] x, Double[] y, 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, Func`2[] functions)
Func<T, double> LinearGenericFunc<T>(T[] x, Double[] y, Func`2[] functions)
Double[] LinearMultiDim(Double[][] x, Double[] y, Func`2[] functions)
Func<Double[], double> LinearMultiDimFunc(Double[][] x, Double[] y, Func`2[] functions)
Func<Double[], double> LinearMultiDimFunc(Double[][] x, Double[] y, DirectRegressionMethod method, 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 LineThroughOrigin(Double[] x, Double[] y)
Least-Squares fitting the points (x,y) to a line through origin y : x -> b*x,
returning its best fitting parameter b,
where the intercept is zero and b the slope.
Func<double, double> LineThroughOriginFunc(Double[] x, Double[] y)
Least-Squares fitting the points (x,y) to a line through origin y : x -> b*x,
returning a function y' for the best fitting line.
ValueTuple<double, double> Logarithm(Double[] x, Double[] y, DirectRegressionMethod method)
Least-Squares fitting the points (x,y) to a logarithm y : x -> a + b*ln(x),
returning its best fitting parameters as (a, b) tuple.
Func<double, double> LogarithmFunc(Double[] x, Double[] y, DirectRegressionMethod method)
Least-Squares fitting the points (x,y) to a logarithm y : x -> a + b*ln(x),
returning a function y' for the best fitting line.
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 Polynomial.Evaluate.
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 Polynomial.Evaluate.
A polynomial with order/degree k has (k+1) coefficients and thus requires at least (k+1) samples.
ValueTuple<double, double> Power(Double[] x, Double[] y, DirectRegressionMethod method)
Least-Squares fitting the points (x,y) to a power y : x -> a*x^b,
returning its best fitting parameters as (a, b) tuple.
Func<double, double> PowerFunc(Double[] x, Double[] y, DirectRegressionMethod method)
Least-Squares fitting the points (x,y) to a power y : x -> a*x^b,
returning a function y' for the best fitting line.