utils.py 文件源码

def mae(reference, predictions, idx=None, n=1):
    '''
    Mean absolute error (MAE) for (p x n) raster arrays, where p is the number
    of bands and n is the number of pixels. Arguments:
        reference   Raster array of reference ("truth" or measured) data
        predictions Raster array of predictions
        idx         Optional array of indices at which to sample the arrays
        n           A normalizing constant for residuals; e.g., the number
                    of endmembers when calculating RMSE for modeled reflectance
    '''
    if idx is None:
        r = reference.shape[1]
        residuals = reference - predictions

    else:
        r = len(idx)
        residuals = reference[:, idx] - predictions[:, idx]

    # Divide the MSE by the number of bands before taking the root
    return np.apply_along_axis(lambda x: np.divide(np.abs(x).sum(), n), 0,
            residuals)