python - mean of 3d points as minimization task -


i have set of n 3d points (x,y,z) , compute mean.

in particular purpose compare differences between several metric.

euclidean distance: d_e(d_1,d_2) = ||d_1 - d_2||

riemannian distance: d_r(d_1,d_2) = ||log(d_1^(-1/2) * d_2 * d_1^(-1/2))||

once fix metric, should compute minimization problem.

i founded in python scipy.optimize kind of task, not know how formulate problem. should use loop?

edit:

i found scipy.optimize.leastsq . seems useful, goal. how use in gradient descent framework?

>>> import numpy np >>> = np.array([[1,2,3],[1,2,3],[7,-100,8]]) >>> a.mean(axis=0) array([  3.        , -32.        ,   4.66666667])

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