Author: bugman
Date: Tue Aug 26 08:58:39 2014
New Revision: 25259
URL: http://svn.gna.org/viewcvs/relax?rev=25259&view=rev
Log:
Implemented the chi-squared gradient as a C module for the target functions.
This simply translates the Python code into C to allow any target function C
modules to build its
own gradient function.
Modified:
trunk/target_functions/c_chi2.c
trunk/target_functions/c_chi2.h
Modified: trunk/target_functions/c_chi2.c
URL:
http://svn.gna.org/viewcvs/relax/trunk/target_functions/c_chi2.c?rev=25259&r1=25258&r2=25259&view=diff
==============================================================================
--- trunk/target_functions/c_chi2.c (original)
+++ trunk/target_functions/c_chi2.c Tue Aug 26 08:58:39 2014
@@ -19,7 +19,9 @@
#include <stdio.h>
#include <math.h>
+#include "c_chi2.h"
+/* Define the function for calculating the square of a number. */
#define square(x) (x)*(x)
@@ -27,17 +29,22 @@
/* Function to calculate the chi-squared value.
The chi-sqared equation
- ~~~~~~~~~~~~~~~~~~~~~~~
- _n_
- \ (yi - yi()) ** 2
- Chi2() = > ----------------
- /__ sigma_i ** 2
- i=1
+ =======================
+
+ The equation is::
+
+ _n_
+ \ (yi - yi(theta)) ** 2
+ chi^2(theta) = > ---------------------
+ /__ sigma_i ** 2
+ i=1
where:
- yi are the values of the measured data set.
- yi() are the values of the back calculated data set.
- sigma_i are the values of the error set.
+ - 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.
The chi-squared value is returned.
*/
@@ -52,3 +59,54 @@
return chi2;
}
+
+
+void dchi2(double dchi2[], double data[], double back_calc_vals[], double
back_calc_grad[][MAX_DATA], double errors[], int num_times, int M) {
+ /* Calculate the full chi-squared gradient.
+
+ The chi-squared gradient
+ ========================
+
+ The equation is::
+
+ _n_
+ dchi^2(theta) \ / yi - yi(theta) dyi(theta) \
+ ------------- = -2 > | -------------- . ---------- |
+ dthetaj /__ \ sigma_i**2 dthetaj /
+ i=1
+
+ where
+ - i is the index over data sets.
+ - j is the parameter index of the gradient.
+ - 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.
+ - dyi(theta)/dthetaj are the values of the back calculated gradient
for parameter j.
+ - sigma_i are the values of the error set.
+
+
+ @param dchi2: The chi-squared gradient data structure to place
the gradient elements
+ into.
+ @type dchi2: numpy rank-1 size M array
+ @param data: The vector of yi values.
+ @type data: numpy rank-1 size N array
+ @param back_calc_vals: The vector of yi(theta) values.
+ @type back_calc_vals: numpy rank-1 size N array
+ @param back_calc_grad: The matrix of dyi(theta)/dtheta values.
+ @type back_calc_grad: numpy rank-2 size MxN array
+ @param errors: The vector of sigma_i values.
+ @type errors: numpy rank-1 size N array
+ @param M: The dimensions of the gradient.
+ @type M: int
+ */
+
+ /* Declarations. */
+ int i, j;
+
+ /* Calculate the chi-squared gradient. */
+ for (j = 0; j < M; ++j) {
+ for (i = 0; i < num_times; ++i) {
+ dchi2[j] += -2.0 / (errors[i]*errors[i]) * (data[i] -
back_calc_vals[i]) * back_calc_grad[j][i];
+ }
+ }
+}
Modified: trunk/target_functions/c_chi2.h
URL:
http://svn.gna.org/viewcvs/relax/trunk/target_functions/c_chi2.h?rev=25259&r1=25258&r2=25259&view=diff
==============================================================================
--- trunk/target_functions/c_chi2.h (original)
+++ trunk/target_functions/c_chi2.h Tue Aug 26 08:58:39 2014
@@ -21,6 +21,10 @@
#ifndef RELAX_C_CHI2
#define RELAX_C_CHI2
+/* The maximum number of data points */
+#define MAX_DATA 50
+
double chi2(double *values, double *sd, double *back_calc, int num_times);
+void dchi2(double dchi2[], double data[], double back_calc_vals[], double
back_calc_grad[][MAX_DATA], double errors[], int num_times, int M);
#endif
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