Author: bugman
Date: Wed Jan 16 12:05:48 2008
New Revision: 4781
URL: http://svn.gna.org/viewcvs/relax?rev=4781&view=rev
Log:
Merged revisions 4759-4780 via svnmerge from
svn+ssh://bugman@xxxxxxxxxxx/svn/relax/1.3
........
r4759 | semor | 2008-01-15 19:11:09 +0100 (Tue, 15 Jan 2008) | 8 lines
Changed the precision for gX in the mfpar file for use of ModelFree with
the palmer.py script.
The change was from a three decimal precision (-2.713) to a four decimal
precision (-2.7126).
This was discussed in thread starting at:
https://mail.gna.org/public/relax-devel/2008-01/msg00040.html
........
r4778 | bugman | 2008-01-16 11:30:55 +0100 (Wed, 16 Jan 2008) | 3 lines
Converted the maths_fns.chi2 module from Numeric to numpy.
........
r4779 | bugman | 2008-01-16 12:00:28 +0100 (Wed, 16 Jan 2008) | 5 lines
Updated the docstrings of the chi-squared value, gradient, and Hessian
functions.
They are now in epydoc format.
........
r4780 | bugman | 2008-01-16 12:04:34 +0100 (Wed, 16 Jan 2008) | 3 lines
Added a module docstring to the chi2 module.
........
Modified:
branches/N_state_model/ (props changed)
branches/N_state_model/generic_fns/palmer.py
branches/N_state_model/maths_fns/chi2.py
Propchange: branches/N_state_model/
------------------------------------------------------------------------------
Binary property 'svnmerge-integrated' - no diff available.
Modified: branches/N_state_model/generic_fns/palmer.py
URL:
http://svn.gna.org/viewcvs/relax/branches/N_state_model/generic_fns/palmer.py?rev=4781&r1=4780&r2=4781&view=diff
==============================================================================
--- branches/N_state_model/generic_fns/palmer.py (original)
+++ branches/N_state_model/generic_fns/palmer.py Wed Jan 16 12:05:48 2008
@@ -388,7 +388,7 @@
file.write('%-14s' % "constants")
file.write('%-6i' % relax_data_store.res[self.run][i].num)
file.write('%-7s' % self.nucleus)
- file.write('%-8.3f' % (relax_data_store.gx / 1e7))
+ file.write('%-8.4f' % (relax_data_store.gx / 1e7))
file.write('%-8.3f' % (relax_data_store.res[self.run][i].r * 1e10))
file.write('%-8.3f\n' % (relax_data_store.res[self.run][i].csa *
1e6))
Modified: branches/N_state_model/maths_fns/chi2.py
URL:
http://svn.gna.org/viewcvs/relax/branches/N_state_model/maths_fns/chi2.py?rev=4781&r1=4780&r2=4781&view=diff
==============================================================================
--- branches/N_state_model/maths_fns/chi2.py (original)
+++ branches/N_state_model/maths_fns/chi2.py Wed Jan 16 12:05:48 2008
@@ -1,6 +1,6 @@
###############################################################################
#
#
-# Copyright (C) 2003, 2004 Edward d'Auvergne
#
+# Copyright (C) 2003, 2004, 2008 Edward d'Auvergne
#
#
#
# This file is part of the program relax.
#
#
#
@@ -20,9 +20,10 @@
#
#
###############################################################################
+"""Module containing functions for calculating the chi-squared value,
gradient, and Hessian."""
# Python module imports.
-from Numeric import Float64, sum, transpose, zeros
+from numpy import sum
# Chi-squared value.
@@ -33,19 +34,29 @@
"""Function to calculate the chi-squared value.
The chi-sqared equation
- ~~~~~~~~~~~~~~~~~~~~~~~
- _n_
- \ (yi - yi()) ** 2
- Chi2 = > ----------------
- /__ sigma_i ** 2
- i=1
+ =======================
+
+ _n_
+ \ (yi - yi(theta)) ** 2
+ chi^2(theta) = > ---------------------
+ /__ sigma_i ** 2
+ i=1
where:
+ theta is the parameter vector.
yi are the values of the measured data set.
- yi() are the values of the back calculated 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.
+
+ @param data: The vector of yi values.
+ @type data: numpy array
+ @param back_calc_vals: The vector of yi(theta) values.
+ @type back_calc_vals: numpy array
+ @param errors: The vector of sigma_i values.
+ @type errors: numpy array
+ @return: The chi-squared value.
+ @rtype: float
"""
# Calculate the chi-squared statistic.
@@ -60,19 +71,31 @@
"""Function to create the chi-squared gradient.
The chi-sqared gradient
- ~~~~~~~~~~~~~~~~~~~~~~~
- _n_
- dChi2 \ / yi - yi() dyi() \
- ------- = -2 > | ---------- . ------- |
- dthetaj /__ \ sigma_i**2 dthetaj /
- i=1
+ =======================
+
+ _n_
+ dchi^2(theta) \ / yi - yi(theta) dyi(theta) \
+ ------------- = -2 > | -------------- . ---------- |
+ dthetaj /__ \ sigma_i**2 dthetaj /
+ i=1
where:
+ theta is the parameter vector.
yi are the values of the measured data set.
- yi() are the values of the back calculated data set.
+ yi(theta) are the values of the back calculated data set.
+ dyi(theta)/dthetaj are the values of the back calculated gradient.
sigma_i are the values of the error set.
- The chi-squared gradient vector is returned.
+ @param data: The vector of yi values.
+ @type data: numpy 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
+ @param errors: The vector of sigma_i values.
+ @type errors: numpy array
+ @return: The chi-squared gradient.
+ @rtype: numpy array
"""
# Calculate the chi-squared gradient.
@@ -87,17 +110,37 @@
"""Function to create the chi-squared Hessian.
The chi-squared Hessian
- ~~~~~~~~~~~~~~~~~~~~~~~
+ =======================
+
_n_
- d2chi2 \ 1 / dyi() dyi()
d2yi() \
- --------------- = 2 > ---------- | ------- . ------- - (yi - yi())
. --------------- |
- dthetaj.dthetak /__ sigma_i**2 \ dthetaj dthetak
dthetaj.dthetak /
+ d2chi^2(theta) \ 1 / dyi(theta) dyi(theta)
d2yi(theta) \
+ --------------- = 2 > ---------- | ---------- . ---------- -
(yi-yi(theta)) . --------------- |
+ dthetaj.dthetak /__ sigma_i**2 \ dthetaj dthetak
dthetaj.dthetak /
i=1
where:
+ theta is the parameter vector.
yi are the values of the measured relaxation data set.
- yi() are the values of the back calculated 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.
sigma_i are the values of the error set.
+
+
+ @param data: The vector of yi values.
+ @type data: numpy 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
+ @param errors: The vector of sigma_i values.
+ @type errors: numpy array
+ @return: The chi-squared Hessian.
+ @rtype: numpy 3rd rank tensor
"""
# Calculate the chi-squared Hessian.
r4781 - in /branches/N_state_model: ./ generic_fns/palmer.py maths_fns/chi2.py