Strange behaviour of linalg.svd() and linalg.eigh()
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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
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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 <matthieu.brucher@gmail.com>:
-- 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
![](https://secure.gravatar.com/avatar/60e03bd1fd9f2dbc750e0899b9e7e71d.jpg?s=120&d=mm&r=g)
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 <matthieu.brucher@gmail.com>:
-- 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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Matthieu Brucher