[Numpy-discussion] svd
Charles Doutriaux
doutriaux1 at llnl.gov
Wed Jul 16 18:24:17 EDT 2008
doh...
Thanks Charles... I guess I've been staring at this code for too long
now...
C.
Charles R Harris wrote:
>
>
> On Wed, Jul 16, 2008 at 3:58 PM, Charles Doutriaux
> <doutriaux1 at llnl.gov <mailto:doutriaux1 at llnl.gov>> wrote:
>
> Hello,
>
> I'm using 1.1.0 and I have a bizarre thing happening
>
> it seems as if:
> doing:
> import numpy
> SVD = numpy.linalg.svd
>
> if different as doing
> import numpy.oldnumeric.linear_algebra
> SVD = numpy.oldnumeric.linear_algebra.singular_value_decomposition
>
> In the first case passing an array (204,1484) retuns array of shape:
> svd: (204, 204) (204,) (1484, 1484)
>
> in the second case I get (what i expected actually):
> svd: (204, 204) (204,) (204, 1484)
>
> But looking at the code, it seems like
> numpy.oldnumeric.linear_algebra.singular_value_decomposition
> is basicalyy numpy.linalg.svd
>
> Any idea on what's happening here?
>
>
> There is a full_matrices flag that determines if you get the full
> orthogonal matrices, or the the minimum size needed, i.e.
>
> In [12]: l,d,r = linalg.svd(x, full_matrices=0)
>
> In [13]: shape(r)
> Out[13]: (2, 4)
>
> In [14]: x = zeros((2,4))
>
> In [15]: l,d,r = linalg.svd(x)
>
> In [16]: shape(r)
> Out[16]: (4, 4)
>
> In [17]: l,d,r = linalg.svd(x, full_matrices=0)
>
> In [18]: shape(r)
> Out[18]: (2, 4)
>
>
> Chuck
>
>
>
>
>
>
> Thx,
>
> C.
>
>
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