Den 30.01.2011 21:40, skrev Charles R Harris:
> Well, strictly speaking, both documentations say the same thing, but
> the old version was somewhat obfuscated. Either svd returns v.H and A
> = dot(u*d, v.H) or svd returns v and A = dot(u*d,v). I think the
> second is a clearer statement of the r
On Sun, Jan 30, 2011 at 10:35 AM, Sturla Molden wrote:
> Den 30.01.2011 17:04, skrev Charles R Harris:
>
>
> The v.H is the old, incorrect, version of the documentation. The current
> documentation is correct.
>
>
> !!!
>
> Was it just the documentation that was false, or did SVD return v.H befo
On Sun, 30 Jan 2011 18:35:56 +0100, Sturla Molden wrote:
> Den 30.01.2011 17:04, skrev Charles R Harris:
>> The v.H is the old, incorrect, version of the documentation. The
>> current documentation is correct.
>
> !!!
>
> Was it just the documentation that was false, or did SVD return v.H
> befor
Den 30.01.2011 17:04, skrev Charles R Harris:
The v.H is the old, incorrect, version of the documentation. The
current documentation is correct.
!!!
Was it just the documentation that was false, or did SVD return v.H before?
Sturla
___
NumPy-Discu
On Sun, Jan 30, 2011 at 8:25 AM, Sturla Molden wrote:
> Den 30.01.2011 02:58, skrev Jason Grout:
> > Factors the matrix a as u * S * v,
>
> It actually returns the Hermitian of v, as almost any use of SVD will
> require v.H. And by the way, the documentation does not say that the
> factorization
Den 30.01.2011 02:58, skrev Jason Grout:
> Factors the matrix a as u * S * v,
It actually returns the Hermitian of v, as almost any use of SVD will
require v.H. And by the way, the documentation does not say that the
factorization is u * S * v, but u * np.diag(s) * v.H.
Sturla
_
On Sat, Jan 29, 2011 at 6:58 PM, Jason Grout wrote:
> The SVD documentation seems a bit misleading. It says:
>
> Factors the matrix a as u * np.diag(s) * v, where u and v are unitary
> and s is a 1-d array of a‘s singular values.
>
> However, that only is true (i.e., you just have to do np.diag(s
On Sat, Jan 29, 2011 at 8:58 PM, Jason Grout
wrote:
> The SVD documentation seems a bit misleading. It says:
>
> Factors the matrix a as u * np.diag(s) * v, where u and v are unitary
> and s is a 1-d array of a‘s singular values.
>
> However, that only is true (i.e., you just have to do np.diag(s
The SVD documentation seems a bit misleading. It says:
Factors the matrix a as u * np.diag(s) * v, where u and v are unitary
and s is a 1-d array of a‘s singular values.
However, that only is true (i.e., you just have to do np.diag(s) to get
S) in general if full_matrices is False, which is no
