>
> And if you are trying to solve a least-squares, I think that
> you should be using a ridge (or Tikhonov) regularisation:
> http://en.wikipedia.org/wiki/Tikhonov_regularization
> read in particular the paragraph above the table of content:
> you are most likely interested in Gamma = alpha ident
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
Hi all,
Thanks for all of the answers, it gives me a lot of new ideas and new
functions I didn't know of.
@Charles:
The reshape way is a great idea! It gives a great alternative to the
for loop for your code to be vectorized. I tested it I get
%timeit scale_and_add_reshape(R,w,Msr)
1 loops, best o
2011/1/28 Christopher Barker :
> On 1/28/11 7:01 AM, Asmi Shah wrote:
>> I am using python for a while now and I have a requirement of creating a
>> numpy array of microscopic tiff images ( this data is 3d, meaning there are
>> 100 z slices of 512 X 512 pixels.) How can I create an array of images?
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 04:15:34PM +0100, Sturla Molden wrote:
> Den 30.01.2011 07:28, skrev Algis Kabaila:
> > Why not simply numply.linalg.cond? This gives the condition
> > number directly (and presumably performs the inspection of
> > sv's). Or do you think that sv's give more useful informatio
Am 29.1.2011 um 22:01 schrieb Nicolas SCHEFFER:
> Hi all,
>
> First email to the list for me, I just want to say how grateful I am
> to have python+numpy+ipython etc... for my day to day needs. Great
> combination of software.
>
> Anyway, I've been having this bottleneck in one my algorithms th
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
On Sun, Jan 30, 2011 at 9:15 AM, Sturla Molden wrote:
> Den 30.01.2011 07:28, skrev Algis Kabaila:
>> Why not simply numply.linalg.cond? This gives the condition
>> number directly (and presumably performs the inspection of
>> sv's). Or do you think that sv's give more useful information?
>
> You
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
_
Den 30.01.2011 07:28, skrev Algis Kabaila:
> Why not simply numply.linalg.cond? This gives the condition
> number directly (and presumably performs the inspection of
> sv's). Or do you think that sv's give more useful information?
You can use the singular value decomposition to invert the matrix,
Hi list,
This is just a note that an extra track at FEMTEC, a conference for
computational methods in engineering and science, is open for open source
scientific software. The organisers have a taste for Python, so if you
want to submit a paper on numerical methods with Python, this is an
excellen
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