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
> > > > >
> > > >
-------------------------------------------------------------------
> > > > >
> > > > >
> > > > >
> > > > > _______________________________________________
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> > > >
> > > >
> > > > --
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>
Re: [bug #7641] Infinite Chi2 warning