Author: bugman
Date: Thu Jan 17 18:39:56 2013
New Revision: 18209
URL: http://svn.gna.org/viewcvs/relax?rev=18209&view=rev
Log:
The N-state model grid search optimises each tensor separately when only
tensors are optimised.
This massively collapses the grid search size from inc**(5*N) to N*inc**5,
where inc are the number
of increments per dimension and N is the number of alignments, making a grid
search possible.
Modified:
trunk/specific_fns/n_state_model.py
Modified: trunk/specific_fns/n_state_model.py
URL:
http://svn.gna.org/viewcvs/relax/trunk/specific_fns/n_state_model.py?rev=18209&r1=18208&r2=18209&view=diff
==============================================================================
--- trunk/specific_fns/n_state_model.py (original)
+++ trunk/specific_fns/n_state_model.py Thu Jan 17 18:39:56 2013
@@ -1,6 +1,6 @@
###############################################################################
#
#
-# Copyright (C) 2007-2012 Edward d'Auvergne
#
+# Copyright (C) 2007-2013 Edward d'Auvergne
#
#
#
# This file is part of the program relax (http://www.nmr-relax.com).
#
#
#
@@ -1852,21 +1852,15 @@
def grid_search(self, lower=None, upper=None, inc=None,
constraints=False, verbosity=0, sim_index=None):
"""The grid search function.
- @param lower: The lower bounds of the grid search which must
be equal to the number of
- parameters in the model.
+ @param lower: The lower bounds of the grid search which must
be equal to the number of parameters in the model.
@type lower: array of numbers
- @param upper: The upper bounds of the grid search which must
be equal to the number of
- parameters in the model.
+ @param upper: The upper bounds of the grid search which must
be equal to the number of parameters in the model.
@type upper: array of numbers
- @param inc: The increments for each dimension of the space
for the grid search. The
- number of elements in the array must equal to
the number of parameters
- in the model.
+ @param inc: The increments for each dimension of the space
for the grid search. The number of elements in the array must equal to the
number of parameters in the model.
@type inc: array of int
- @param constraints: If True, constraints are applied during the grid
search (elinating parts
- of the grid). If False, no constraints are used.
+ @param constraints: If True, constraints are applied during the grid
search (elinating parts of the grid). If False, no constraints are used.
@type constraints: bool
- @param verbosity: A flag specifying the amount of information to
print. The higher the
- value, the greater the verbosity.
+ @param verbosity: A flag specifying the amount of information to
print. The higher the value, the greater the verbosity.
@type verbosity: int
"""
@@ -1922,8 +1916,63 @@
lower.append(-1e-3)
upper.append(1e-3)
- # Minimisation.
- self.minimise(min_algor='grid', lower=lower, upper=upper, inc=inc,
constraints=constraints, verbosity=verbosity, sim_index=sim_index)
+ # Determine the data type.
+ data_types = self._base_data_types()
+
+ # The number of tensors to optimise.
+ tensor_num = align_tensor.num_tensors(skip_fixed=True)
+
+ # Custom sub-grid search for when only tensors are optimised (as
each tensor is independent, the number of points collapses from inc**(5*N) to
N*inc**5).
+ if cdp.model == 'fixed' and tensor_num > 1 and ('rdc' in data_types
or 'pcs' in data_types) and not align_tensor.all_tensors_fixed() and
hasattr(cdp, 'paramag_centre_fixed') and cdp.paramag_centre_fixed:
+ # Print out.
+ print("Optimising each alignment tensor separately.")
+
+ # Store the alignment tensor fixed flags.
+ fixed_flags = []
+ for i in range(len(cdp.align_ids)):
+ # Get the tensor object.
+ tensor = align_tensor.return_tensor(index=i,
skip_fixed=False)
+
+ # Store the flag.
+ fixed_flags.append(tensor.fixed)
+
+ # Fix the tensor.
+ tensor.set('fixed', True)
+
+ # Loop over each sub-grid.
+ for i in range(len(cdp.align_ids)):
+ # Skip the tensor if originally fixed.
+ if fixed_flags[i]:
+ continue
+
+ # Get the tensor object.
+ tensor = align_tensor.return_tensor(index=i,
skip_fixed=False)
+
+ # Unfix the current tensor.
+ tensor.set('fixed', False)
+
+ # Grid search parameter subsets.
+ lower_sub = lower[i*5:i*5+5]
+ upper_sub = upper[i*5:i*5+5]
+ inc_sub = inc[i*5:i*5+5]
+
+ # Minimisation of the sub-grid.
+ self.minimise(min_algor='grid', lower=lower_sub,
upper=upper_sub, inc=inc_sub, constraints=constraints, verbosity=verbosity,
sim_index=sim_index)
+
+ # Fix the tensor again.
+ tensor.set('fixed', True)
+
+ # Reset the state of the tensors.
+ for i in range(len(cdp.align_ids)):
+ # Get the tensor object.
+ tensor = align_tensor.return_tensor(index=i,
skip_fixed=False)
+
+ # Fix the tensor.
+ tensor.set('fixed', fixed_flags[i])
+
+ # All other minimisation.
+ else:
+ self.minimise(min_algor='grid', lower=lower, upper=upper,
inc=inc, constraints=constraints, verbosity=verbosity, sim_index=sim_index)
def is_spin_param(self, name):
r18209 - /trunk/specific_fns/n_state_model.py