The exponential curve-fitting Hessian from the target_functions.relax_fit.d2func() function is bug free and fully functional.
Regards, Edward On 26 August 2014 19:24, Edward d'Auvergne <[email protected]> wrote: > Oh well, I had some time between frame order calculations and decided > to practice my C coding a little. The full exponential curve Hessian > is now implemented. It may still need debugging. But you can now use > the full Newton algorithm in your timing tests and comparisons to > Scipy. > > Regards, > > Edward > > On 26 August 2014 13:32, Edward d'Auvergne <[email protected]> wrote: >> Test it and see ;) And if you are interested in the speed up in this >> part of the analysis, you can take it one more step and implement the >> Hessian in the C modules and then time the Newton algorithm! >> >> Regards, >> >> Edward >> >> On 26 August 2014 13:26, Troels Emtekær Linnet <[email protected]> wrote: >>> Should this be used instead then in R2eff minimisation? >>> >>> Is it faster than simplex? >>> >>> Best >>> Troels >>> >>> 2014-08-26 13:04 GMT+02:00 Edward d'Auvergne <[email protected]>: >>>> Oh, I forgot to mention, but I also converted the >>>> Relax_fit.test_curve_fitting_height and >>>> Relax_fit.test_curve_fitting_volume system tests to use BFGS >>>> optimisation. This is one of the best optimisation techniques when >>>> only the function and gradient are present, as it tries to numerically >>>> approximate the Hessian matrix, updating it as the algorithm moves >>>> along. It is fast and performs incredibly well, so it is a widely >>>> used algorithm. The system tests using BFGS demonstrate that the >>>> gradient works very well for optimisation. It isn't as fast as Newton >>>> optimisation however. >>>> >>>> Regards, >>>> >>>> Edward >>>> >>>> >>>> >>>> >>>> On 26 August 2014 13:00, Edward d'Auvergne <[email protected]> wrote: >>>>> Hi Troels, >>>>> >>>>> I've now implemented the exponential curve-fitting dfunc() function >>>>> for calculating the gradient. This includes: >>>>> >>>>> - The Python wrapper function >>>>> specific_analyses.relax_fit.optimisation.dfunc_wrapper(), >>>>> - The target_functions/c_chi2.c function dchi2(), >>>>> - The target_functions/exponential.c functions exponential_dI0() and >>>>> exponential_dR(), >>>>> - The target_functions.relax_fit C module dfunc() Python function. >>>>> >>>>> I have tested the gradient using the numerical integration in the >>>>> test_suite/shared_data/curve_fitting/numeric_gradient/integrate.py >>>>> file to determine what the chi-squared gradient should be at different >>>>> parameter combinations. And this has been converted into a few unit >>>>> tests. As this works, that means that the jacobian() function of the >>>>> C module should also be correct and bug-free, hence you should be able >>>>> to use it to obtain the covariance matrix. >>>>> >>>>> This is all I will do for now. All that is left is to do for the >>>>> target_functions.relax_fit C module is simply the same thing, but for >>>>> the Hessian. Feel free to give this a go if you are interested. If I >>>>> have time in the future, I might add this too. >>>>> >>>>> Regards, >>>>> >>>>> Edward >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> On 24 August 2014 17:56, Troels E. Linnet >>>>> <[email protected]> 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. >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> _______________________________________________________ >>>>>> >>>>>> Reply to this item at: >>>>>> >>>>>> <http://gna.org/task/?7822> >>>>>> >>>>>> _______________________________________________ >>>>>> Message sent via/by Gna! >>>>>> http://gna.org/ >>>>>> >>>>>> >>>>>> _______________________________________________ >>>>>> relax (http://www.nmr-relax.com) >>>>>> >>>>>> This is the relax-devel mailing list >>>>>> [email protected] >>>>>> >>>>>> To unsubscribe from this list, get a password >>>>>> reminder, or change your subscription options, >>>>>> visit the list information page at >>>>>> https://mail.gna.org/listinfo/relax-devel _______________________________________________ relax (http://www.nmr-relax.com) This is the relax-devel mailing list [email protected] To unsubscribe from this list, get a password reminder, or change your subscription options, visit the list information page at https://mail.gna.org/listinfo/relax-devel
