#9011: the numpy SVD decomposition docstring is wrong
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Reporter: was | Owner: jason, was
Type: defect | Status: new
Priority: minor | Milestone: sage-4.6.2
Component: linear algebra | Keywords:
Author: | Upstream: N/A
Reviewer: | Merged:
Work_issues: |
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Comment(by jason):
Remember that multiplication of numpy arrays is *not* matrix
multiplication. Matrix multiplication is through np.dot().
Here I've mirrored your example using the format in numpy docs:
{{{
sage: A = np.array([[2,0,0,0],[0,0,0,0]])
sage: U,s,V=np.linalg.svd(A,full_matrices=False)
sage: S=np.diag(s)
sage: np.dot(U,np.dot(S,V))
array([[ 2., 0., 0., 0.],
[ 0., 0., 0., 0.]])
}}}
Note that by default, full_matrices is True, though, so you'd have to do
something like the first example in the numpy docs:
http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.svd.html#numpy.linalg.svd
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Ticket URL: <http://trac.sagemath.org/sage_trac/ticket/9011#comment:3>
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