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LinearRegression_M
Header: | AVL.h |
---|---|
Namespace: | avl |
Module: | FoundationPro |
Computes linear regression of given point set using selected M-estimator for outlier suppression.
Syntax
C++
C#
void avl::LinearRegression_M ( const atl::Array<float>& inYValues, atl::Optional<const atl::Array<float>&> inXValues, avl::MEstimator::Type inOutlierSuppression, float inClippingFactor, int inIterationCount, atl::Optional<const avl::LinearFunction&> inInitialLinearFunction, avl::LinearFunction& outLinearFunction, atl::Array<float>& outEstimatedValues, atl::Array<float>& outResiduals, atl::Optional<atl::Array<float>&> outYInliers = atl::NIL, atl::Optional<atl::Array<float>&> outXInliers = atl::NIL )
Parameters
Name | Type | Range | Default | Description | |
---|---|---|---|---|---|
inYValues | const Array<float>& | Sequence of ordinates | |||
inXValues | Optional<const Array<float>&> | NIL | Sequence of abscissae, or {0, 1, 2, ...} by default | ||
inOutlierSuppression | MEstimator::Type | ||||
inClippingFactor | float | 0.675 - 6.0 | 2.5f | Multitude of standard deviation within which points are considered inliers | |
inIterationCount | int | 0 - | 5 | Number of iterations of outlier suppressing algorithm | |
inInitialLinearFunction | Optional<const LinearFunction&> | NIL | Initial approximation of the output linear function (if available) | ||
outLinearFunction | LinearFunction& | Linear function approximating the given point set | |||
outEstimatedValues | Array<float>& | The result of application of the computed function to the X values | |||
outResiduals | Array<float>& | Difference between an input Y value and the corresponding estimated value | |||
outYInliers | Optional<Array<float>&> | NIL | Coordinate of the inlying points of the best line | ||
outXInliers | Optional<Array<float>&> | NIL | Coordinate of the inlying points of the best line |
Optional Outputs
The computation of following outputs can be switched off by passing value atl::NIL
to these parameters: outYInliers, outXInliers.
Read more about Optional Outputs.
Errors
List of possible exceptions:
Error type | Description |
---|---|
DomainError | Inconsistent size of arrays in LinearRegression_M. |