[SciPy-user] eig() segfaults on SuSE 9.3 with ACML, Numeric's eigenvectors works
dickrp at wckn.com
Sun Nov 13 23:59:42 CST 2005
SuSE 9.3 comes with libblas and liblapack. However, they don't define srotmg.
Therefore, SciPy doesn't work with them.
AMD provides an optimized BLAS/LAPACK library called ACML. After installing
that and directing SciPy to it with site.cfg, SciPy builds. Unfortunately,
linalg.eig(), produces results that do not conform to the documentation (the
matrix holding the eigenvectors is transposed). I transpose this matrix in
my own code but find that eig() also intermittantly segfaults for matrices of
significant size. My machine is otherwise quite stable: this is probably not
caused by a hardware problem.
Has anybody been able to get SciPy working reliably on an Athlon SuSE 9.3
Does the following code work without segfaulting for anybody else? It runs
fine as long as I use Numeric but segfaults if I use SciPy. By the way, I'm
linking against AMD's ACML BLAS/LAPACK library from both Numeric and SciPy.
import Numeric as n1
import LinearAlgebra as la1
import scipy as n2
import scipy.linalg as la2
mat2 = n2.stats.rand(5, 5)
mat1 = n1.array(mat2)
# print la2.eig(mat2)
Does the following code work reliably for anybody else?
import scipy.linalg as la1
import scipy as n1
la1.eig(n1.array([[1.0, 1.0], [1.0, 1.0]]))
Should be (from Numeric's LinearAlgebra)
(array([ 2., 0.]), array([[ 0.70710678, 0.70710678],
Unfortunately, it is transposed
(array([ 2.+0.j, 0.+0.j]), array([[ 0.70710678, -0.70710678],
[ 0.70710678, 0.70710678]]))
I have some PCA and non-linear multi-layer neural network backprop code I
would be happy to give to SciPy. However, I can't properly test this code
with SciPy unless I have a stable eig().
Thanks for any suggestions,
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