scipy.spatial.distance.minkowski#
- scipy.spatial.distance.minkowski(u, v, p=2, w=None)[source]#
Compute the Minkowski distance between two 1-D arrays.
The Minkowski distance between 1-D arrays u and v, is defined as
\[ \begin{align}\begin{aligned}{\|u-v\|}_p = (\sum{|u_i - v_i|^p})^{1/p}.\\\left(\sum{w_i(|(u_i - v_i)|^p)}\right)^{1/p}.\end{aligned}\end{align} \]- Parameters
- u(N,) array_like
Input array.
- v(N,) array_like
Input array.
- pscalar
The order of the norm of the difference \({\|u-v\|}_p\). Note that for \(0 < p < 1\), the triangle inequality only holds with an additional multiplicative factor, i.e. it is only a quasi-metric.
- w(N,) array_like, optional
The weights for each value in u and v. Default is None, which gives each value a weight of 1.0
- Returns
- minkowskidouble
The Minkowski distance between vectors u and v.
Examples
>>> from scipy.spatial import distance >>> distance.minkowski([1, 0, 0], [0, 1, 0], 1) 2.0 >>> distance.minkowski([1, 0, 0], [0, 1, 0], 2) 1.4142135623730951 >>> distance.minkowski([1, 0, 0], [0, 1, 0], 3) 1.2599210498948732 >>> distance.minkowski([1, 1, 0], [0, 1, 0], 1) 1.0 >>> distance.minkowski([1, 1, 0], [0, 1, 0], 2) 1.0 >>> distance.minkowski([1, 1, 0], [0, 1, 0], 3) 1.0