Fit Class |
Least-Squares Curve Fitting Routines
Inheritance HierarchyNamespace: Altaxo.Calc
Assembly: AltaxoCore (in AltaxoCore.dll) Version: 4.8.3261.0 (4.8.3261.0)
SyntaxC#
public static class Fit
The Fit type exposes the following members.
Methods| Name | Description | |
|---|---|---|
  | Curve(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. |
| Curve(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. |
| Curve(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. |
| Curve(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. |
| Curve(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. |
| CurveFunc(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. |
| CurveFunc(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. |
| CurveFunc(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. |
| CurveFunc(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. |
| CurveFunc(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. |
| Exponential | 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. |
| ExponentialFunc | 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. |
| Line | 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. |
| LinearCombination(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. |
| LinearCombination(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. |
| LinearCombinationFunc(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. |
| LinearCombinationFunc(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. |
| LinearGenericT(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. |
| LinearGenericT(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. |
| LinearGenericFuncT(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. |
| LinearGenericFuncT(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. |
| LinearMultiDim(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. |
| LinearMultiDim(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. |
| LinearMultiDimFunc(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. |
| LinearMultiDimFunc(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. |
| LineFunc | Least-Squares fitting the points (x,y) to a line y : x -> a+b*x, returning a function y' for the best fitting line. |
| LineThroughOrigin | 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. |
| LineThroughOriginFunc | 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. |
| Logarithm | 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. |
| LogarithmFunc | 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. |
| MultiDim | 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. |
| MultiDimFunc | 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. |
| MultiDimWeighted | 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. |
| Polynomial | 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. |
| PolynomialFunc | 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. |
| PolynomialWeighted | 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. |
| Power | Least-Squares fitting the points (x,y) to a power y : x -> a*x^b, returning its best fitting parameters as (a, b) tuple. |
| PowerFunc | Least-Squares fitting the points (x,y) to a power y : x -> a*x^b, returning a function y' for the best fitting line. |
See Also