MatrixXf nn,xx;
xx = nn.colwise().normalized();
Somehow it makes sense, since it is not a partial reduction in this case, because it returns a vector and not a single element (if it existed of course)
On Thu, Jul 22, 2010 at 12:25 PM, Carlos Becker
<carlosbecker@xxxxxxxxx> wrote:
Nice idea, but .rowwise().normalized() doesn't compile.I guess that normalized() hasn't been coded into row or col-wise ops, since this doesn't work either:
MatrixXf nn;
VectorXf a,b;
b = nn.colwise().normalized();
cheers
On Wed, Jul 21, 2010 at 11:17 PM, Christoph Hertzberg
<chtz@xxxxxxxxxxxxxxxxxxxxxxxx> wrote:
Carlos Becker wrote:
Hi everyone,
(first, I am using the dev branch)
I am trying to do something like (a.array() * b.array().conjugate()) / (
a.array().abs() * b.array().abs() ), where a and b are two VectorXcf.
Wouldn't something like this work in your case? (I didn't actually try it ...)
(a.array() * b.array().conjugate()).rowwise().normalized();
Because:
abs(a)*abs(b) = abs(a)*abs(conj(b)) = abs(a*conj(b))
Actually, an elementwise signum function would be helpful in that case.
--
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Dipl.-Inf. Christoph Hertzberg
Cartesium 0.051
Universität Bremen
Enrique-Schmidt-Straße 5
28359 Bremen
Tel: (+49) 421-218-64252
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