| 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.
|