Hi Troels,
To help with the implementation, I would recommend that parameter
scaling is turned off as that might make the covariance matrix
calculations more difficult. Once the covariance matrix diagonal
matches the Monte Carlo simulations (well the square root of the
diagonal elements to convert from variances to standard deviations),
then turn on the parameter scaling and see if the errors are still
correct.
Regards,
Edward
On 24 August 2014 17:56, Troels E. Linnet
<NO-REPLY.INVALID-ADDRESS@xxxxxxx> wrote:
URL:
<http://gna.org/task/?7822>
Summary: Implement user function to estimate R2eff and
associated errors for exponential curve fitting.
Project: relax
Submitted by: tlinnet
Submitted on: Sun 24 Aug 2014 03:56:36 PM UTC
Should Start On: Sun 24 Aug 2014 12:00:00 AM UTC
Should be Finished on: Sun 24 Aug 2014 12:00:00 AM UTC
Category: relax's source code
Priority: 5 - Normal
Status: In Progress
Percent Complete: 0%
Assigned to: tlinnet
Open/Closed: Open
Discussion Lock: Any
Effort: 0.00
_______________________________________________________
Details:
A verification script, showed that using scipy.optimize.leastsq reaches the
exact same parameters as minfx for exponential curve fitting.
The verification script is in:
test_suite/shared_data/curve_fitting/profiling/profiling_relax_fit.py
test_suite/shared_data/curve_fitting/profiling/verify_error.py
The profiling script shows that a 10 X increase in speed can be reached by
removing
the linear constraints when using minfx.
The profiling also shows that scipy.optimize.leastsq is 10X as fast as using
minfx, even without linear constraints.
scipy.optimize.leastsq is a wrapper around wrapper around MINPACK's lmdif
and
lmder algorithms.
MINPACK is a FORTRAN90 library which solves systems of nonlinear equations,
or
carries out the least squares minimization of the residual of a set of
linear
or nonlinear equations.
The verification script also shows, that a very heavy and time consuming
monte carlo simulation of 2000 steps, reaches the same errors as the errors
reported by scipy.optimize.leastsq.
The return from scipy.optimize.leastsq, gives the estimated co-variance.
Taking the square root of the co-variance corresponds with 2X error reported
by minfx after 2000 Monte-Carlo simulations.
This could be an extremely time saving step, when performing model fitting
in
R1rho, where the errors of the R2eff values, are estimated by Monte-Carlo
simulations.
The following setup illustrates the problem.
This was analysed on a: MacBook Pro, 13-inch, Late 2011.
With no multi-core setup.
Script running is:
test_suite/shared_data/dispersion/Kjaergaard_et_al_2013/2_pre_run_r2eff.py
This script analyses just the R2eff values for 15 residues.
It estimates the errors of R2eff based on 2000 Monte Carlo simulations.
For each residues, there is 14 exponential graphs.
The script was broken after 35 simulations.
This was measured to 20 minutes.
So 500 simulations would take about 4.8 Hours.
The R2eff values and errors can by scipy.optimize.leastsq can instead be
calculated in: 15 residues * 0.02 seconds = 0.3 seconds.
_______________________________________________________
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<http://gna.org/task/?7822>
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Re: [task #7822] Implement user function to estimate R2eff and associated errors for exponential curve fitting.