Author: bugman
Date: Thu Aug 14 11:31:10 2008
New Revision: 7192
URL: http://svn.gna.org/viewcvs/relax?rev=7192&view=rev
Log:
Bug fixes and expansion of the __linear_constraints() method.
The bug fix is that the b array was not being scaled! This is essential when
diagonal scaling is
active.
The added feature is that the pN weight of state N, which is not a parameter
of the model, is now
constrained to be between 0 and 1. This is because the value is equal to 1
minus the sum of all
other probabilities, hence linear constraints can be created.
Modified:
branches/rdc_analysis/specific_fns/n_state_model.py
Modified: branches/rdc_analysis/specific_fns/n_state_model.py
URL:
http://svn.gna.org/viewcvs/relax/branches/rdc_analysis/specific_fns/n_state_model.py?rev=7192&r1=7191&r2=7192&view=diff
==============================================================================
--- branches/rdc_analysis/specific_fns/n_state_model.py (original)
+++ branches/rdc_analysis/specific_fns/n_state_model.py Thu Aug 14 11:31:10
2008
@@ -26,7 +26,7 @@
# Python module imports.
from math import acos, cos, pi
from minfx.generic import generic_minimise
-from numpy import array, dot, float64, identity, zeros
+from numpy import array, dot, float64, identity, ones, zeros
from numpy.linalg import inv, norm
from re import search
from warnings import warn
@@ -277,7 +277,7 @@
Standard notation
=================
- The N-state model constraints are:
+ The N-state model constraints are::
0 <= pc <= 1,
@@ -288,24 +288,44 @@
===============
In the notation A.x >= b, where A is an matrix of coefficients, x is
an array of parameter
- values, and b is a vector of scalars, these inequality constraints
are:
+ values, and b is a vector of scalars, these inequality constraints
are::
| 1 0 0 | | 0 |
| | | |
|-1 0 0 | | -1 |
+ | | | |
+ | 0 1 0 | | 0 |
| | | p0 | | |
- | 0 1 0 | | | | 0 |
+ | 0 -1 0 | | | | -1 |
| | . | p1 | >= | |
- | 0 -1 0 | | | | -1 |
+ | 0 0 1 | | | | 0 |
| | | p2 | | |
- | 0 0 1 | | 0 |
+ | 0 0 -1 | | -1 |
| | | |
- | 0 0 -1 | | -1 |
+ |-1 -1 -1 | | -1 |
+ | | | |
+ | 1 1 1 | | 0 |
This example is for a 4-state model, the last probability pn is not
included as this
parameter does not exist (because the sum of pc is equal to 1). The
Euler angle parameters
have been excluded here but will be included in the returned A and b
objects. These
- parameters simply add columns of zero to the A matrix and have no
effect on b.
+ parameters simply add columns of zero to the A matrix and have no
effect on b. The last two
+ rows correspond to the inequality::
+
+ 0 <= pN <= 1.
+
+ As::
+ N-1
+ \
+ pN = 1 - > pc,
+ /__
+ c=1
+
+ then::
+
+ -p1 - p2 - ... - p(N-1) >= -1,
+
+ p1 + p2 + ... + p(N-1) >= 0.
@keyword data_types: The base data types used in the
optimisation. This list can
@@ -341,17 +361,26 @@
A[j][i] = 1.0
A[j+1][i] = -1.0
b.append(0.0)
- b.append(-1.0)
+ b.append(-1.0 / scaling_matrix[i, i])
j = j + 2
# Increment i.
i = i + 1
+
+ # Add the inequalities for pN.
+ A.append(zero_array * 0.0)
+ A.append(zero_array * 0.0)
+ for i in xrange(pop_start, self.param_num()):
+ A[-2][i] = -1.0
+ A[-1][i] = 1.0
+ b.append(-1.0 / scaling_matrix[i, i])
+ b.append(0.0)
# Convert to numpy data structures.
A = array(A, float64)
b = array(b, float64)
- # Return the contraint objects.
+ # Return the constraint objects.
return A, b
r7192 - /branches/rdc_analysis/specific_fns/n_state_model.py