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, <tlinnet@xxxxxxxxxxxxx> 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.
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Re: r23799 - /branches/disp_spin_speed/target_functions/chi2.py