On 11/6/06, Alexandar Hansen <viochemist@xxxxxxxxx> wrote:
Whoa! I thought I had changed my 'runs' lines to not choose anything higher than tm3. I tried what you said and reduced them models to tm0-tm3 and it still failed. Through some process of elimination, the one that spits out the error is only tm3 (tm, S2, and Rex). tm2 finishes without any error.
I received another chi-squared = infinity value when running the prolate, oblate, and ellipsoid models. I used only tm0, tm1, and tm2 for the local_tm selection and then I used m0-m5 for the diffusion model selection. The chi-square = infinity error only occured on the second round of the minimization for prolate (ie full_analysis.py finished twice but failed the 3rd time for prolate) and on the first round for oblate and ellipsoid. The sphere model finished without an error.
On 11/6/06, Chris MacRaild <c.a.macraild@xxxxxxxxxxx> wrote:
Something of a side issue, I know, but this appears to be the
optimisation of a 3-parameter model to 3 data points. Hence the chi2
value of 0.
I thought we had coded residue selection to deselect residues in this
case. Any idea why it is apparently not happening here?
In any case, a quick workaround for the problem is to simply not
optimise models with more parameters than you have data points (even if
relax did ever give you results for these models, the results would be
meaningless).
Chris
On Tue, 2006-11-07 at 04:18 +1100, Edward d'Auvergne wrote:
> As it is the chi-squared value which is optimised in model-free
> analysis, this is synonymous with the 'function value' or 'fk'. Gary,
> you might need to handle this one.
>
> Cheers,
>
> Edward
>
>
>
> On 11/7/06, Alexandar Hansen <
viochemist@xxxxxxxxx> wrote:
> > I'm not entirely sure what the processor specs are, but I'm running this on
> > a 64 bit system running RedHat 7 (??). I haven't had any issues prior to
> > this due to a different platform.
> >
> > This is the last thing to happen before the RelaxError:
> >
> > Fitting to residue: 23 G
> > ~~~~~~~~~~~~~~~~~~~~~~~~
> >
> >
> > Method of Multipliers
> > ~~~~~~~~~~~~~~~~~~~~~
> >
> > k: 0 xk: array([ 1.20000000e+04, 9.00000000e-01, 5.00000000e+00])
> > fk: 327.68236595759424
> > Entering sub-algorithm.
> >
> > Newton minimisation
> > ~~~~~~~~~~~~~~~~~~~
> > Line search: Backtracking line search.
> > Hessian modification: The Gill, Murray, and Wright modified
> > Cholesky algorithm.
> >
> > k: 0 xk: array([ 1.20000000e+04, 9.00000000e-01,
> > 5.00000000e+00]) fk: 327.68236595759424
> >
> > Parameter values: array([
2.97262710e+03, 2.75282891e-01,
> > 2.22776495e+01])
> > Function value: 1.1399366931634486e-11
> > Iterations: 7
> > Function calls: 16
> > Gradient calls: 8
> > Hessian calls: 7
> > Warning: None
> >
> >
> > k: 1 xk: array([ 2.97262710e+03 , 2.75282891e-01,
> > 2.22776495e+01]) fk:
1.1399366931634486e-11
> > Entering sub-algorithm.
> >
> > Newton minimisation
> > ~~~~~~~~~~~~~~~~~~~
> > Line search: Backtracking line search.
> > Hessian modification: The Gill, Murray, and Wright modified
> > Cholesky algorithm.
> >
> > k: 0 xk: array([ 2.97262710e+03, 2.75282891e-01,
> > 2.22776495e+01]) fk: 1.1399366931634486e-11
> >
> > Parameter values: array([
2.97262779e+03, 2.75282907e-01,
> > 2.22776492e+01])
> > Function value: 1.368250643042848e-23
> > Iterations: 1
> > Function calls: 3
> > Gradient calls: 2
> > Hessian calls: 1
> > Warning: None
> >
> >
> > k: 2 xk: array([ 2.97262779e+03, 2.75282907e-01, 2.22776492e+01])
> > fk:
1.368250643042848e-23
> > Entering sub-algorithm.
> >
> > Newton minimisation
> > ~~~~~~~~~~~~~~~~~~~
> > Line search: Backtracking line search.
> > Hessian modification: The Gill, Murray, and Wright modified
> > Cholesky algorithm.
> >
> > k: 0 xk: array([ 2.97262779e+03, 2.75282907e-01,
> > 2.22776492e+01]) fk: 1.368250643042848e-23
> >
> > Parameter values: array([
2.97262779e+03, 2.75282907e-01,
> > 2.22776492e+01])
> > Function value: 2.5466842239831223e-29
> > Iterations: 1
> > Function calls: 3
> > Gradient calls: 2
> > Hessian calls: 1
> > Warning: None
> >
> >
> > k: 3 xk: array([ 2.97262779e+03, 2.75282907e-01, 2.22776492e+01])
> > fk:
2.5466842239831223e-29
> > Entering sub-algorithm.
> >
> > Newton minimisation
> > ~~~~~~~~~~~~~~~~~~~
> > Line search: Backtracking line search.
> > Hessian modification: The Gill, Murray, and Wright modified
> > Cholesky algorithm.
> >
> > k: 0 xk: array([ 2.97262779e+03 , 2.75282907e-01,
> > 2.22776492e+01]) fk: 2.5466842239831223e-29
> >
> > Parameter values: array([
2.97262779e+03, 2.75282907e-01,
> > 2.22776492e+01])
> > Function value: 0.0
> > Iterations: 1
> > Function calls: 3
> > Gradient calls: 2
> > Hessian calls: 1
> > Warning: None
> >
> >
> > Parameter values: array([ 2.97262779e+03, 2.75282907e-01,
> > 2.22776492e+01])
> > Function value:
0.0
> > Iterations: 10
> > Function calls: 25
> > Gradient calls: 14
> > Hessian calls: 10
> > Warning: None
> >
> > RelaxError: The invalid chi-squared floating point value of infinity has
> > occurred.
> >
> >
> >
> >
> >
> > On 11/6/06, Gary S. Thompson <
garyt@xxxxxxxxxxxxxxx> wrote:
> > > Chris MacRaild wrote:
> > >
> > > >Hi Alex,
> > > >
> > > >I can't reproduce this with my own data, so I suspect its something to
> > > >do with either your data or the way relax is handling it. There are a
> > > >few things you could do to try and track this down. First try running
> > > >your script with the --debug flag. This should cause relax to save state
> > > >in response to the error. You can then load this state in an interactive
> > > >relax session and explore the data to find any sign of the problems. You
> > > >will want to type:
> > > >
> > > >self.relax.data.res[run_name][index]
> > > >
> > > >(where index is any integer from 0 to the number of residues you have)
> > > >This will give you all the residue specific data for this residue. Check
> > > >whether chi2 is finite (in which case that residue is probably not the
> > > >cause of your problems), and check that the relax_data and relax_error
> > > >fields match your expectations.
> > > >
> > > >Hope that helps,
> > > >Chris
> > > >
> > > >
> > > One other question to ask here is what architecture are you running this
> > > on. It isn't a powerpc based mac, an sgi or a sun by any chance? The
> > > code in float.py has only been tested on intel based platforms (linux
> > > x86 and windows) currently.....
> > >
> > > regards
> > > gary
> > >
> > > --
> > >
> > -------------------------------------------------------------------
> > > Dr Gary Thompson
> > > Astbury Centre for Structural Molecular Biology,
> > > University of Leeds, Astbury Building,
> > > Leeds, LS2 9JT, West-Yorkshire, UK Tel. +44-113-3433024
> > > email:
garyt@xxxxxxxxxxxxxxx
Fax +44-113-2331407
> > >
> > -------------------------------------------------------------------
> > >
> > >
> > >
> > > _______________________________________________
> > > relax (http://nmr-relax.com)
> > >
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> >
> >
> >
> > --
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Re: [bug #7641] Infinite Chi2 warning