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Fit Class

Least-Squares Curve Fitting Routines
Inheritance Hierarchy
SystemObject
  Altaxo.CalcFit

Namespace: Altaxo.Calc
Assembly: AltaxoCore (in AltaxoCore.dll) Version: 4.8.3179.0 (4.8.3179.0)
Syntax
C#
public static class Fit

The Fit type exposes the following members.

Methods
 NameDescription
Public methodStatic memberCurve(Double, Double, FuncDouble, Double, Double, Double, Double, Int32) Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p, x), returning its best fitting parameter p.
Public methodStatic memberCurve(Double, Double, FuncDouble, Double, Double, Double, Double, Double, Double, Int32) 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.
Public methodStatic memberCurve(Double, Double, FuncDouble, Double, Double, Double, Double, Double, Double, Double, Double, Int32) 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.
Public methodStatic memberCurve(Double, Double, FuncDouble, Double, Double, Double, Double, Double, Double, Double, Double, Double, Double, Int32) 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.
Public methodStatic memberCurve(Double, Double, FuncDouble, Double, Double, Double, Double, Double, Double, Double, Double, Double, Double, Double, Double, Int32) 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.
Public methodStatic memberCurveFunc(Double, Double, FuncDouble, Double, Double, Double, Double, Int32) 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.
Public methodStatic memberCurveFunc(Double, Double, FuncDouble, Double, Double, Double, Double, Double, Double, Int32) 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.
Public methodStatic memberCurveFunc(Double, Double, FuncDouble, Double, Double, Double, Double, Double, Double, Double, Double, Int32) 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.
Public methodStatic memberCurveFunc(Double, Double, FuncDouble, Double, Double, Double, Double, Double, Double, Double, Double, Double, Double, Int32) 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.
Public methodStatic memberCurveFunc(Double, Double, FuncDouble, Double, Double, Double, Double, Double, Double, Double, Double, Double, Double, Double, Double, Int32) 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.
Public methodStatic memberExponential 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.
Public methodStatic memberExponentialFunc 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.
Public methodStatic memberLine 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.
Public methodStatic memberLinearCombination(Double, Double, FuncDouble, Double) Least-Squares fitting the points (x,y) to an arbitrary linear combination y : x -> p0*f0(x) + p1*f1(x) + ... + pk*fk(x), returning its best fitting parameters as [p0, p1, p2, ..., pk] array.
Public methodStatic memberLinearCombination(Double, Double, DirectRegressionMethod, FuncDouble, Double) Least-Squares fitting the points (x,y) to an arbitrary linear combination y : x -> p0*f0(x) + p1*f1(x) + ... + pk*fk(x), returning its best fitting parameters as [p0, p1, p2, ..., pk] array.
Public methodStatic memberLinearCombinationFunc(Double, Double, FuncDouble, Double) Least-Squares fitting the points (x,y) to an arbitrary linear combination y : x -> p0*f0(x) + p1*f1(x) + ... + pk*fk(x), returning a function y' for the best fitting combination.
Public methodStatic memberLinearCombinationFunc(Double, Double, DirectRegressionMethod, FuncDouble, Double) Least-Squares fitting the points (x,y) to an arbitrary linear combination y : x -> p0*f0(x) + p1*f1(x) + ... + pk*fk(x), returning a function y' for the best fitting combination.
Public methodStatic memberLinearGenericT(T, Double, FuncT, Double) Least-Squares fitting the points (T,y) = (T,y) to an arbitrary linear combination y : X -> p0*f0(T) + p1*f1(T) + ... + pk*fk(T), returning its best fitting parameters as [p0, p1, p2, ..., pk] array.
Public methodStatic memberLinearGenericT(T, Double, DirectRegressionMethod, FuncT, Double) Least-Squares fitting the points (T,y) = (T,y) to an arbitrary linear combination y : X -> p0*f0(T) + p1*f1(T) + ... + pk*fk(T), returning its best fitting parameters as [p0, p1, p2, ..., pk] array.
Public methodStatic memberLinearGenericFuncT(T, Double, FuncT, Double) Least-Squares fitting the points (T,y) = (T,y) to an arbitrary linear combination y : X -> p0*f0(T) + p1*f1(T) + ... + pk*fk(T), returning a function y' for the best fitting combination.
Public methodStatic memberLinearGenericFuncT(T, Double, DirectRegressionMethod, FuncT, Double) Least-Squares fitting the points (T,y) = (T,y) to an arbitrary linear combination y : X -> p0*f0(T) + p1*f1(T) + ... + pk*fk(T), returning a function y' for the best fitting combination.
Public methodStatic memberLinearMultiDim(Double, Double, FuncDouble, Double) Least-Squares fitting the points (X,y) = ((x0,x1,..,xk),y) to an arbitrary linear combination y : X -> p0*f0(x) + p1*f1(x) + ... + pk*fk(x), returning its best fitting parameters as [p0, p1, p2, ..., pk] array.
Public methodStatic memberLinearMultiDim(Double, Double, DirectRegressionMethod, FuncDouble, Double) Least-Squares fitting the points (X,y) = ((x0,x1,..,xk),y) to an arbitrary linear combination y : X -> p0*f0(x) + p1*f1(x) + ... + pk*fk(x), returning its best fitting parameters as [p0, p1, p2, ..., pk] array.
Public methodStatic memberLinearMultiDimFunc(Double, Double, FuncDouble, Double) Least-Squares fitting the points (X,y) = ((x0,x1,..,xk),y) to an arbitrary linear combination y : X -> p0*f0(x) + p1*f1(x) + ... + pk*fk(x), returning a function y' for the best fitting combination.
Public methodStatic memberLinearMultiDimFunc(Double, Double, DirectRegressionMethod, FuncDouble, Double) Least-Squares fitting the points (X,y) = ((x0,x1,..,xk),y) to an arbitrary linear combination y : X -> p0*f0(x) + p1*f1(x) + ... + pk*fk(x), returning a function y' for the best fitting combination.
Public methodStatic memberLineFunc Least-Squares fitting the points (x,y) to a line y : x -> a+b*x, returning a function y' for the best fitting line.
Public methodStatic memberLineThroughOrigin 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.
Public methodStatic memberLineThroughOriginFunc 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.
Public methodStatic memberLogarithm 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.
Public methodStatic memberLogarithmFunc 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.
Public methodStatic memberMultiDim Least-Squares fitting the points (X,y) = ((x0,x1,..,xk),y) to a linear surface y : X -> p0*x0 + p1*x1 + ... + pk*xk, returning its best fitting parameters as [p0, p1, p2, ..., pk] array. If an intercept is added, its coefficient will be prepended to the resulting parameters.
Public methodStatic memberMultiDimFunc Least-Squares fitting the points (X,y) = ((x0,x1,..,xk),y) to a linear surface y : X -> p0*x0 + p1*x1 + ... + pk*xk, returning a function y' for the best fitting combination. If an intercept is added, its coefficient will be prepended to the resulting parameters.
Public methodStatic memberMultiDimWeighted Weighted Least-Squares fitting the points (X,y) = ((x0,x1,..,xk),y) and weights w to a linear surface y : X -> p0*x0 + p1*x1 + ... + pk*xk, returning its best fitting parameters as [p0, p1, p2, ..., pk] array.
Public methodStatic memberPolynomial 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.
Public methodStatic memberPolynomialFunc 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.
Public methodStatic memberPolynomialWeighted 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.
Public methodStatic memberPower Least-Squares fitting the points (x,y) to a power y : x -> a*x^b, returning its best fitting parameters as (a, b) tuple.
Public methodStatic memberPowerFunc Least-Squares fitting the points (x,y) to a power y : x -> a*x^b, returning a function y' for the best fitting line.
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