gaussj¶

sofia_redux.toolkit.fitting.polynomial.gaussj(alpha, beta, invert=False, preserve=True)[source]¶

Linear equation solution by Gauss-Jordan elimination and matrix inversion

Parameters:

alphaarray_like of float (N, N): Coefficient array where N is the number of unknown variables to be solved, and therefore is the number of linear equations.
betaarray_like of float (N, M): Constant array containing the M right-hand side vectors
invertbool, optional: If True, return A^-1 in addition to x.
preservebool, optional: If True, creates copies the input alpha and beta arrays. Otherwise, alpha and beta will be modified inplace if they are already arrays of type numpy.float64.

Returns:

x [, inv_A)]numpy.ndarray [, numpy.ndarray]: The solution (x) to Ax=b (N, M). If invert is True, then inv_A (N, N) will also be returned.

Notes