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| | nmrLinearRegressionWindowSolver (size_t length) |
| | ~nmrLinearRegressionWindowSolver () |
| virtual size_t | WindowLength () const |
| bool | Sample (const _elementType &x, const _elementType &y) |
| bool | Sample (const vctDynamicVector< _elementType > &CMN_UNUSED(x), const vctDynamicVector< _elementType > &CMN_UNUSED(y)) |
| bool | Sample (const vctDynamicConstVectorRef< _elementType > &CMN_UNUSED(x), const vctDynamicConstVectorRef< _elementType > &CMN_UNUSED(y)) |
| bool | Sample (const std::vector< _elementType > &CMN_UNUSED(x), const std::vector< _elementType > &CMN_UNUSED(y)) |
| virtual bool | Recalculate () |
| bool | EstimateAsFractions (SummationType &slope_num, SummationType &yint_num, SummationType &denom, SummationType *tse_num=0) |
| | nmrLinearRegressionSolver (_elementType tol=cmnTypeTraits< _elementType >::DefaultTolerance) |
| virtual | ~nmrLinearRegressionSolver () |
| size_t | NumPoints () const |
| _elementType | GetTolerance () const |
| void | SetTolerance (_elementType tol) |
| virtual void | Clear () |
| virtual bool | Sample (const vctFixedSizeVector< _elementType, 2 > &in) |
| virtual bool | Sample (const vctFixedSizeConstVectorRef< _elementType, 2, 1 > &in) |
| virtual bool | Sample (const vctDynamicVector< _elementType > &x, const vctDynamicVector< _elementType > &y) |
| virtual bool | Sample (const vctDynamicConstVectorRef< _elementType > &x, const vctDynamicConstVectorRef< _elementType > &y) |
| virtual bool | Sample (const std::vector< _elementType > &x, const std::vector< _elementType > &y) |
| virtual bool | Estimate (_elementType &slope, _elementType &yint, _elementType *mse=0) |
template<class _elementType>
class nmrLinearRegressionWindowSolver< _elementType >
This class provides a moving window linear regression solver using a least-squares solution. The window size is specified in the constructor. This implementation recomputes the intermediate sums each time the Estimate or EstimateAsFractions method is called; for a more efficient recursive implementation, use the nmrLinearRegressionWindowRecursive solver. But, note that for floating point types, there is a chance for accumulation of round-off error with the recursive solver.