- colour.utilities.metric_mse(a: ArrayLike, b: ArrayLike, axis: Optional[Union[Integer, Tuple[Integer]]] = None) FloatingOrNDArray #
Compute the mean squared error (MSE) or mean squared deviation (MSD) between given variables \(a\) and \(b\).
a (ArrayLike) – Variable \(a\).
b (ArrayLike) – Variable \(b\).
axis (Optional[Union[Integer, Tuple[Integer]]]) – Axis or axes along which the means are computed. The default is to compute the mean of the flattened array. If this is a tuple of ints, a mean is performed over multiple axes, instead of a single axis or all the axes as before.
Mean squared error (MSE).
- Return type
>>> a = np.array([0.48222001, 0.31654775, 0.22070353]) >>> b = a * 0.9 >>> metric_mse(a, b) 0.0012714...