On Fri, Jul 25, 2008 at 12:36 PM, Keith Goodman <kwgoodman@gmail.com> wrote:
On Fri, Jul 25, 2008 at 12:32 PM, Frank Lagor <dfranci@seas.upenn.edu> wrote:
Perhaps I do not understand something properly, if so could someone please explain the behavior I notice with numpy.linalg.svd when acting on arrays. It gives the incorrect answer, but works fine with matrices. My numpy is 1.1.0.
R = n.array([[3.6,.35],[.35,1.8]]) V,D,W = n.linalg.svd(R) V*n.diag(D)*W.transpose() array([[ 3.5410365 , 0. ], [ 0. , 1.67537611]]) R = n.matrix([[3.6,.35],[.35,1.8]]) V,D,W = n.linalg.svd(R) V*n.diag(D)*W.transpose() matrix([[ 3.6 , 0.35], [ 0.35, 1.8 ]])
'*' does element-by-element multiplication for arrays but matrix multiplication for matrices.
As a check (for the array case):
n.dot(V, n.dot(n.diag(D), W.transpose())) # That's hard to read!
array([[ 3.6 , 0.35], [ 0.35, 1.8 ]])