Hi, I tried using Matlab with the same matrix and its eig() function. It can diagonalize the matrix with a correct result, which is not the case for linalg.eigh(). Strange.
Matthieu 2008/4/17 Matthieu Brucher <[EMAIL PROTECTED]>: > Hi, > > Ive implemented the classical MultiDimensional Scaling for the scikit learn > using both functions. Their behavior surprised me for "big" arrays (10000 by > 10000, symmetric as it is a similarity matrix). > linalg.svd() raises a memory error because it tries to allocate a > (7000000,) array (in fact bigger than that !). This is strange because the > test was made on a 64bits Linux, so memory should not have been a problem. > linalg.eigh() fails to diagonalize the matrix, it gives me NaN as a result, > and this is not very useful. > A direct optimization of the underlying cost function can give me an > adequate solution. > > I cannot attach the matrix file (more than 700MB when pickled), but if > anyone has a clue, I'll be glad. > > Matthieu > -- > French PhD student > Website : http://matthieu-brucher.developpez.com/ > Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92 > LinkedIn : http://www.linkedin.com/in/matthieubrucher -- French PhD student Website : http://matthieu-brucher.developpez.com/ Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92 LinkedIn : http://www.linkedin.com/in/matthieubrucher
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