Author: bugman
Date: Mon Jul 28 14:34:21 2008
New Revision: 6986
URL: http://svn.gna.org/viewcvs/relax?rev=6986&view=rev
Log:
Changes to the linear_constraints() method.
The method is now private and accepts new arguments for the different types
of N-state model.
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=6986&r1=6985&r2=6986&view=diff
==============================================================================
--- branches/rdc_analysis/specific_fns/n_state_model.py (original)
+++ branches/rdc_analysis/specific_fns/n_state_model.py Mon Jul 28 14:34:21
2008
@@ -172,6 +172,85 @@
raise RelaxError, "Neither RDC nor alignment tensor data is
present."
+ def __linear_constraints(self, data_type=None, scaling_matrix=None):
+ """Function for setting up the linear constraint matrices A and b.
+
+ Standard notation
+ =================
+
+ The N-state model constraints are:
+
+ 0 <= pc <= 1,
+
+ where p is the probability and c corresponds to state c.
+
+
+ Matrix notation
+ ===============
+
+ 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:
+
+ | 1 0 0 | | 0 |
+ | | | |
+ |-1 0 0 | | -1 |
+ | | | p0 | | |
+ | 0 1 0 | | | | 0 |
+ | | . | p1 | >= | |
+ | 0 -1 0 | | | | -1 |
+ | | | p2 | | |
+ | 0 0 1 | | 0 |
+ | | | |
+ | 0 0 -1 | | -1 |
+
+ 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.
+
+
+ @keyword data_type: The type of data used in the
optimisation - either 'rdc' or
+ 'tensor'.
+ @type data_type: str
+ @keyword scaling_matrix: The diagonal scaling matrix.
+ @type scaling_matrx: numpy rank-2 square matrix
+ @return: The matrices A and b.
+ @rtype: tuple of len 2 of a numpy rank-2, size
NxM matrix and numpy
+ rank-1, size N array
+ """
+
+ # Alias the current data pipe.
+ cdp = ds[ds.current_pipe]
+
+ # Initialisation (0..j..m).
+ A = []
+ b = []
+ zero_array = zeros(self.param_num(), float64)
+ i = 0
+ j = 0
+
+ # Loop over the prob parameters (N - 1, because the sum of pc is 1).
+ for k in xrange(cdp.N - 1):
+ # 0 <= pc <= 1.
+ A.append(zero_array * 0.0)
+ A.append(zero_array * 0.0)
+ A[j][i] = 1.0
+ A[j+1][i] = -1.0
+ b.append(0.0)
+ b.append(-1.0)
+ j = j + 2
+
+ # Increment i.
+ i = i + 1
+
+ # Convert to numpy data structures.
+ A = array(A, float64)
+ b = array(b, float64)
+
+ # Return the contraint objects.
+ return A, b
+
+
def assemble_param_vector(self, sim_index=None):
"""Assemble all the parameters of the model into a single array.
@@ -612,79 +691,6 @@
# All other parameters are global.
return False
-
-
- def linear_constraints(self):
- """Function for setting up the linear constraint matrices A and b.
-
- Standard notation
- =================
-
- The N-state model constraints are:
-
- 0 <= pc <= 1,
-
- where p is the probability and c corresponds to state c.
-
-
- Matrix notation
- ===============
-
- 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:
-
- | 1 0 0 | | 0 |
- | | | |
- |-1 0 0 | | -1 |
- | | | p0 | | |
- | 0 1 0 | | | | 0 |
- | | . | p1 | >= | |
- | 0 -1 0 | | | | -1 |
- | | | p2 | | |
- | 0 0 1 | | 0 |
- | | | |
- | 0 0 -1 | | -1 |
-
- 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.
-
-
- @return: The matrices A and b.
- @rtype: tuple of len 2 of a numpy matrix and numpy
array
- """
-
- # Alias the current data pipe.
- cdp = ds[ds.current_pipe]
-
- # Initialisation (0..j..m).
- A = []
- b = []
- zero_array = zeros(self.param_num(), float64)
- i = 0
- j = 0
-
- # Loop over the prob parameters (N - 1, because the sum of pc is 1).
- for k in xrange(cdp.N - 1):
- # 0 <= pc <= 1.
- A.append(zero_array * 0.0)
- A.append(zero_array * 0.0)
- A[j][i] = 1.0
- A[j+1][i] = -1.0
- b.append(0.0)
- b.append(-1.0)
- j = j + 2
-
- # Increment i.
- i = i + 1
-
- # Convert to numpy data structures.
- A = array(A, float64)
- b = array(b, float64)
-
- # Return the contraint objects.
- return A, b
def minimise(self, min_algor=None, min_options=None, func_tol=None,
grad_tol=None, max_iterations=None, constraints=False, scaling=True,
verbosity=0, sim_index=None):
@@ -747,7 +753,7 @@
# Linear constraints.
if constraints:
- A, b = self.linear_constraints(data_type=data_type,
scaling_matrix=scaling_matrix)
+ A, b = self.__linear_constraints(data_type=data_type,
scaling_matrix=scaling_matrix)
# Set up minimisation using alignment tensors.
if data_type == 'tensor':
r6986 - /branches/rdc_analysis/specific_fns/n_state_model.py