Author: bugman
Date: Thu Jul 24 13:59:45 2008
New Revision: 6946
URL: http://svn.gna.org/viewcvs/relax?rev=6946&view=rev
Log:
Improvements to the chi-squared value, gradient, and Hessian docstrings.
Modified:
1.3/maths_fns/chi2.py
Modified: 1.3/maths_fns/chi2.py
URL:
http://svn.gna.org/viewcvs/relax/1.3/maths_fns/chi2.py?rev=6946&r1=6945&r2=6946&view=diff
==============================================================================
--- 1.3/maths_fns/chi2.py (original)
+++ 1.3/maths_fns/chi2.py Thu Jul 24 13:59:45 2008
@@ -45,6 +45,7 @@
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.
@@ -52,11 +53,11 @@
@param data: The vector of yi values.
- @type data: numpy array
+ @type data: numpy rank-1 size N array
@param back_calc_vals: The vector of yi(theta) values.
- @type back_calc_vals: numpy array
+ @type back_calc_vals: numpy rank-1 size N array
@param errors: The vector of sigma_i values.
- @type errors: numpy array
+ @type errors: numpy rank-1 size N array
@return: The chi-squared value.
@rtype: float
"""
@@ -84,22 +85,25 @@
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.
+ - dyi(theta)/dthetaj are the values of the back calculated gradient
for parameter j.
- sigma_i are the values of the error set.
+
@param data: The vector of yi values.
- @type data: numpy array
+ @type data: numpy rank-1 size N array
@param back_calc_vals: The vector of yi(theta) values.
- @type back_calc_vals: numpy array
- @param back_calc_grad: The matrix of dyi(theta)/dthetaj values.
- @type back_calc_grad: numpy matrix
+ @type back_calc_vals: numpy rank-1 size N array
+ @param back_calc_grad: The vector of dyi(theta)/dthetaj values for
parameter j.
+ @type back_calc_grad: numpy rank-1 size N array
@param errors: The vector of sigma_i values.
- @type errors: numpy array
- @return: The chi-squared gradient.
- @rtype: numpy array
+ @type errors: numpy rank-1 size N array
+ @return: The chi-squared gradient element j.
+ @rtype: float
"""
# Calculate the chi-squared gradient.
@@ -125,28 +129,32 @@
i=1
where
+ - i is the index over data sets.
+ - j is the parameter index for the first dimension of the Hessian.
+ - k is the parameter index for the second dimension of the Hessian.
- theta is the parameter vector.
- - yi are the values of the measured relaxation data set.
- - yi(theta) are the values of the back calculated relaxation data
set.
- - dyi(theta)/dthetaj are the values of the back calculated gradient.
- - d2yi(theta)/dthetaj.dthetak are the values of the back calculated
Hessian.
+ - 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.
+ - d2yi(theta)/dthetaj.dthetak are the values of the back calculated
Hessian for the
+ parameters j and k.
- sigma_i are the values of the error set.
@param data: The vector of yi values.
- @type data: numpy array
+ @type data: numpy rank-1 size N array
@param back_calc_vals: The vector of yi(theta) values.
- @type back_calc_vals: numpy array
- @param back_calc_grad_j: The matrix of dyi(theta)/dthetaj values.
- @type back_calc_grad_j: numpy matrix
- @param back_calc_grad_k: The matrix of dyi(theta)/dthetak values.
- @type back_calc_grad_k: numpy matrix
- @param back_calc_hess: The 3rd rank tensor of
d2yi(theta)/dthetaj.dthetak values.
- @type back_calc_hess: numpy matrix
+ @type back_calc_vals: numpy rank-1 size N array
+ @param back_calc_grad_j: The vector of dyi(theta)/dthetaj values for
parameter j.
+ @type back_calc_grad_j: numpy rank-1 size N array
+ @param back_calc_grad_k: The vector of dyi(theta)/dthetak values for
parameter k.
+ @type back_calc_grad_k: numpy rank-1 size N array
+ @param back_calc_hess: The vector of d2yi(theta)/dthetaj.dthetak
values at {j, k}.
+ @type back_calc_hess: numpy rank-1 size N array
@param errors: The vector of sigma_i values.
- @type errors: numpy array
- @return: The chi-squared Hessian.
- @rtype: numpy 3rd rank tensor
+ @type errors: numpy rank-1 size N array
+ @return: The chi-squared Hessian element {j,k}.
+ @rtype: float
"""
# Calculate the chi-squared Hessian.
r6946 - /1.3/maths_fns/chi2.py