Linear least squares calculations and properties.
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#include <linearleastsquares.hpp>
template<class Scalar>
class Opm::LinearLeastSquares< Scalar >
Linear least squares calculations and properties.
- Warning
- Do not use with large matrices since matrix inversion is LU decomposition
◆ LinearLeastSquares()
Constructor.
- Parameters
-
| A | Coefficient matrix |
| b | Right-hand side (data) vector |
◆ evaluate()
Evaluate regression model at input x vector.
- Parameters
-
- Returns
- Regression model value
◆ explainedSumOfSquares()
Value for how well regression model represents the model.
- Returns
- Model sum of squares
◆ residualSumOfSquares()
Measure of the discrepancy between data and regression model.
- Returns
- Sum of the square of residual
◆ RSquared()
Coefficient of determination.
Typical value for how well a regression model fits the data
- Returns
- R^2 value
◆ solve()
Solve linear least squares system.
Done by solving the normal equations: (A^T*A)*x = A^T*b
◆ totalSumOfSquares()
Sum of all squared differences.
Total sum of squares = explained sum of squares + residual sum of squares
- Returns
- Total sum of squares
◆ x()
Read-only vector of calculated coefficient vector.
- Returns
- Coeff. vector
The documentation for this class was generated from the following file: