I don't think I can do that. I can go to the normalized results but not the other way.
On Tue, Dec 20, 2011 at 9:45 PM, Olivier Delalleau <sh...@keba.be> wrote: > Hmm, sorry, I don't see any obvious logic that would explain how Octave > obtains this result, although of course there is probably some logic... > > Anyway, since you seem to know what you want, can't you obtain the same > result by doing whatever un-normalizing operation you are after? > > > -=- Olivier > > 2011/12/20 Fahreddın Basegmez <mangab...@gmail.com> > >> I should include the scipy response too I guess. >> >> >> scipy.linalg.eig(STIFM, MASSM) >> (array([ 3937.15984097+0.j, 3937.15984097+0.j, 3937.15984097+0.j, >> 3923.07692308+0.j, 3923.07692308+0.j, 7846.15384615+0.j]), >> array([[ 1., 0., 0., 0., 0., 0.], >> [ 0., 1., 0., 0., 0., 0.], >> [ 0., 0., 1., 0., 0., 0.], >> [ 0., 0., 0., 1., 0., 0.], >> [ 0., 0., 0., 0., 1., 0.], >> [ 0., 0., 0., 0., 0., 1.]])) >> >> On Tue, Dec 20, 2011 at 9:14 PM, Fahreddın Basegmez >> <mangab...@gmail.com>wrote: >> >>> If I can get the same response as Matlab I would be all set. >>> >>> >>> Octave results >>> >>> >> STIFM >>> STIFM = >>> >>> Diagonal Matrix >>> >>> 1020 0 0 0 0 0 >>> 0 1020 0 0 0 0 >>> 0 0 1020 0 0 0 >>> 0 0 0 102000 0 0 >>> 0 0 0 0 102000 0 >>> 0 0 0 0 0 204000 >>> >>> >> MASSM >>> MASSM = >>> >>> Diagonal Matrix >>> >>> 0.25907 0 0 0 0 0 >>> 0 0.25907 0 0 0 0 >>> 0 0 0.25907 0 0 0 >>> 0 0 0 26.00000 0 0 >>> 0 0 0 0 26.00000 0 >>> 0 0 0 0 0 26.00000 >>> >>> >> [a, b] = eig(STIFM, MASSM) >>> a = >>> >>> 0.00000 0.00000 0.00000 1.96468 0.00000 0.00000 >>> 0.00000 0.00000 0.00000 0.00000 1.96468 0.00000 >>> 0.00000 0.00000 1.96468 0.00000 0.00000 0.00000 >>> 0.19612 0.00000 0.00000 0.00000 0.00000 0.00000 >>> 0.00000 0.19612 0.00000 0.00000 0.00000 0.00000 >>> 0.00000 0.00000 0.00000 0.00000 0.00000 0.19612 >>> >>> b = >>> >>> Diagonal Matrix >>> >>> 3923.1 0 0 0 0 0 >>> 0 3923.1 0 0 0 0 >>> 0 0 3937.2 0 0 0 >>> 0 0 0 3937.2 0 0 >>> 0 0 0 0 3937.2 0 >>> 0 0 0 0 0 7846.2 >>> >>> >>> Numpy Results >>> >>> >>> STIFM >>> array([[ 1020., 0., 0., 0., 0., 0.], >>> [ 0., 1020., 0., 0., 0., 0.], >>> [ 0., 0., 1020., 0., 0., 0.], >>> [ 0., 0., 0., 102000., 0., 0.], >>> [ 0., 0., 0., 0., 102000., 0.], >>> [ 0., 0., 0., 0., 0., 204000.]]) >>> >>> >>> MASSM >>> >>> array([[ 0.25907, 0. , 0. , 0. , 0. , 0. >>> ], >>> [ 0. , 0.25907, 0. , 0. , 0. , 0. >>> ], >>> [ 0. , 0. , 0.25907, 0. , 0. , 0. >>> ], >>> [ 0. , 0. , 0. , 26. , 0. , 0. >>> ], >>> [ 0. , 0. , 0. , 0. , 26. , 0. >>> ], >>> [ 0. , 0. , 0. , 0. , 0. , 26. >>> ]]) >>> >>> >>> a, b = linalg.eig(dot( linalg.pinv(MASSM), STIFM)) >>> >>> >>> a >>> >>> array([ 3937.15984097, 3937.15984097, 3937.15984097, 3923.07692308, >>> 3923.07692308, 7846.15384615]) >>> >>> >>> b >>> >>> array([[ 1., 0., 0., 0., 0., 0.], >>> [ 0., 1., 0., 0., 0., 0.], >>> [ 0., 0., 1., 0., 0., 0.], >>> [ 0., 0., 0., 1., 0., 0.], >>> [ 0., 0., 0., 0., 1., 0.], >>> [ 0., 0., 0., 0., 0., 1.]]) >>> >>> On Tue, Dec 20, 2011 at 8:40 PM, Olivier Delalleau <sh...@keba.be>wrote: >>> >>>> Hmm... ok ;) (sorry, I can't follow you there) >>>> >>>> Anyway, what kind of non-normalization are you after? I looked at the >>>> doc for Matlab and it just says eigenvectors are not normalized, without >>>> additional details... so it looks like it could be anything. >>>> >>>> >>>> -=- Olivier >>>> >>>> 2011/12/20 Fahreddın Basegmez <mangab...@gmail.com> >>>> >>>>> I am computing normal-mode frequency response of a mass-spring system. >>>>> The algorithm I am using requires it. >>>>> >>>>> On Tue, Dec 20, 2011 at 8:10 PM, Olivier Delalleau <sh...@keba.be>wrote: >>>>> >>>>>> I'm probably missing something, but... Why would you want >>>>>> non-normalized eigenvectors? >>>>>> >>>>>> -=- Olivier >>>>>> >>>>>> >>>>>> 2011/12/20 Fahreddın Basegmez <mangab...@gmail.com> >>>>>> >>>>>>> Howdy, >>>>>>> >>>>>>> Is it possible to get non-normalized eigenvectors from >>>>>>> scipy.linalg.eig(a, b)? Preferably just by using numpy. >>>>>>> >>>>>>> BTW, Matlab/Octave provides this with its eig(a, b) function but I >>>>>>> would like to use numpy for obvious reasons. >>>>>>> >>>>>>> Regards, >>>>>>> >>>>>>> Fahri >>>>>>> >>>>>> >>>> _______________________________________________ >>>> NumPy-Discussion mailing list >>>> NumPy-Discussion@scipy.org >>>> http://mail.scipy.org/mailman/listinfo/numpy-discussion >>>> >>>> >>> >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
