JJ schrieb: > Travis Oliphant <oliphant <at> ee.byu.edu> writes:
> >> Svd returns matrices now. Except for the list of singular values >> which is still an array. Do you want a 1xn matrix instead of an >> array? Although I'm a matrix supporter, I'm not sure here. Afaics the pro argument is to have *everything* a matrix when you're in that camp. Fair enough. But then it's already not clear if you want a row or a column, and you carry an extra dimension around, which is sometimes annoying e.g. for cumulation of the values, which I do a lot (for eigenvalues, that is). So for my personal use I came to the conclusion that the status quo of numpy (array for the value list, matrix for the decomp) is just fine. So maybe the people in favor of values-in-1xn-matrices can tell why they need to matrix-multiply the value array afterwards, because that's the only benefit I can see here. > > I had just tried this with my new version of numpy, but I had used svd > as follows: > import scipy.linalg as la > res = la.svd(M) > That returned arrays, but I see that using: > res = linalg.svd(M) > returns matrices. Apparently, both numpy and scipy have linalg > packages, which differ. I did not know that. Whoops. > I'm trying to get by with numpy (good that kron was brought over!), but eventually I will need scipy -- I was hoping that all the matrix discussion in the numpy list implicitly applied to scipy as well. Is that not true? Cheers, Sven ------------------------------------------------------------------------- Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642 _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
