Paulo van Breugel wrote: > > The main speed-up will come from "inlining" distance.mahalanobis(), > > which is essentially: > > > > delta = u - v > > m = np.dot(np.dot(delta, VI), delta) > > return np.sqrt(m) > > > > np.dot(v, m) is equivalent to np.sum(v * m,axis=0), and np.dot(u,v) is > > equivalent to np.sum(u * v), so the second line above is equivalent to > > > > m = np.sum(np.sum(delta[:,None] * VI * delta[None,:], axis=-1), > > axis=-1) > > > Smart! I can follow the logic, but I am not sure how to solve the problem > below: > > Traceback (most recent call last): > File "<stdin>", line 1, in <module> > ValueError: operands could not be broadcast together with shapes > (3,1,77,78) (3,3) > > Which refers to the different dimensions of delta and VI?
The first version of the code (which is quoted above) will only work with 1-D arrays. It's just a fairly direct translation of the existing distance.mahalanobis() function, given to explain how it can then be extened to 3-D arrays (i.e. a 2-D array of 1-D vectors). The second version: > > VI = np.linalg.inv(covar) > > delta = dat_ref - stat_mean[:,None,None] > > m = np.sum(np.sum(delta[:,:,:,None] * delta[:,:,None,:] * > > VI[None,None,:,:],axis=-1),axis=-1) > > stat_mah = np.sqrt(m) should work with delta being a 3-D array. -- Glynn Clements <gl...@gclements.plus.com> _______________________________________________ grass-dev mailing list grass-dev@lists.osgeo.org http://lists.osgeo.org/mailman/listinfo/grass-dev
