Hi Troels, Actually, a better name might be chi2_rankN(), as this is for multiple rank rather than multiple dimensions. The rank of A would be len(A.shape), whereas the dimensions for each rank would be A.shape[0], A.shape[1], etc. What do you think? Would you have an idea for a better name?
Regards, Edward On 10 June 2014 18:54, <[email protected]> wrote: > Author: tlinnet > Date: Tue Jun 10 18:54:28 2014 > New Revision: 23799 > > URL: http://svn.gna.org/viewcvs/relax?rev=23799&view=rev > Log: > Added a multi-dimensional numpy array chi2 value calculation function. > > Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion > models for Clustered analysis. > > Modified: > branches/disp_spin_speed/target_functions/chi2.py > > Modified: branches/disp_spin_speed/target_functions/chi2.py > URL: > http://svn.gna.org/viewcvs/relax/branches/disp_spin_speed/target_functions/chi2.py?rev=23799&r1=23798&r2=23799&view=diff > ============================================================================== > --- branches/disp_spin_speed/target_functions/chi2.py (original) > +++ branches/disp_spin_speed/target_functions/chi2.py Tue Jun 10 18:54:28 > 2014 > @@ -63,6 +63,46 @@ > > # Calculate the chi-squared statistic. > return sum((1.0 / errors * (data - back_calc_vals))**2, axis=0) > + > + > +# Chi-squared value for multi-dimensional axis. > +#################### > + > + > +def chi2_ND(data, back_calc_vals, errors): > + """Function to calculate the chi-squared value for multiple numpy array > axis. > + > + The chi-squared equation > + ======================== > + > + The equation is:: > + > + _n_ > + \ (yi - yi(theta)) ** 2 > + chi^2(theta) = > --------------------- > + /__ sigma_i ** 2 > + i=1 > + > + where > + - i is the index over data sets. > + - theta is the parameter vector. > + - yi are the values of the measured data set. > + - yi(theta) are the values of the back calculated data set. > + - sigma_i are the values of the error set. > + > + > + @param data: The multi dimensional vectors of yi values. > + @type data: numpy multi dimensional array > + @param back_calc_vals: The multi dimensional vectors of yi(theta) > values. > + @type back_calc_vals: numpy multi dimensional array > + @param errors: The multi dimensional vectors of sigma_i values. > + @type errors: numpy multi dimensional array > + @return: The chi-squared value. > + @rtype: float > + """ > + > + # Calculate the chi-squared statistic. > + return sum((1.0 / errors * (data - back_calc_vals))**2) > > > # Chi-squared gradient. > > > _______________________________________________ > relax (http://www.nmr-relax.com) > > This is the relax-commits 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-commits _______________________________________________ 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
