colour.algebra.least_square_mapping_MoorePenrose¶
-
colour.algebra.
least_square_mapping_MoorePenrose
(y, x)[source]¶ Computes the least-squares mapping from dependent variable \(y\) to independent variable \(x\) using Moore-Penrose inverse.
Parameters: - y (array_like) – Dependent and already known \(y\) variable.
- x (array_like, optional) – Independent \(x\) variable(s) values corresponding with \(y\) variable.
Returns: Least-squares mapping.
Return type: ndarray
References
Examples
>>> prng = np.random.RandomState(2) >>> y = prng.random_sample((24, 3)) >>> x = y + (prng.random_sample((24, 3)) - 0.5) * 0.5 >>> least_square_mapping_MoorePenrose(y, x) # doctest: +ELLIPSIS array([[ 1.0526376..., 0.1378078..., -0.2276339...], [ 0.0739584..., 1.0293994..., -0.1060115...], [ 0.0572550..., -0.2052633..., 1.1015194...]])