# scipy.spatial.distance.correlation

scipy.spatial.distance.correlation(u, v, w=None, centered=True)

  Compute the correlation distance between two 1-D arrays.

  The correlation distance between u and v, is defined as

  \[1 - \frac{(u - \bar{u}) \cdot (v - \bar{v})} {{||(u - \bar{u})||}_2 {||(v - \bar{v})||}_2}\]

  where \(\bar{u}\) is the mean of the elements of u and \(x \cdot y\) is the dot product of \(x\) and \(y\).

  Parameters
    u(N,) array_like

      Input array.

    v(N,) array_like

      Input array.

    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

    centered bool, optional

      If True, u and v will be centered. Default is True.

  Returns
    correlation double

      The correlation distance between 1-D array u and v.

scipy.spatial.distance.cityblock scipy.spatial.distance.cosine