Manhattan distance metric and conversion to/from Eucledian distances.
remarks
The conversion are always based on n-D squares, i.e. the assumption that the distance is equally shared in each dimension.
E.g. a Manhattan distance of 30 could be obtained from [0,0] -> [10,20], but would be interpreted here as distance from [0,0] -> [15,15], which produces the same Manhattan value, but yields a different Eucledian result. For lack of any other information about the distance values, this is however the only way to approach conversion and is sufficient for the purposes of this package...
Manhattan distance metric and conversion to/from Eucledian distances.
The conversion are always based on n-D squares, i.e. the assumption that the distance is equally shared in each dimension.
E.g. a Manhattan distance of 30 could be obtained from [0,0] -> [10,20], but would be interpreted here as distance from [0,0] -> [15,15], which produces the same Manhattan value, but yields a different Eucledian result. For lack of any other information about the distance values, this is however the only way to approach conversion and is sufficient for the purposes of this package...