Author: bugman
Date: Tue Sep 22 14:52:46 2009
New Revision: 9558
URL: http://svn.gna.org/viewcvs/relax?rev=9558&view=rev
Log:
Redesigned the N-state model grid search setup around the new minfx interface.
Modified:
1.3/specific_fns/n_state_model.py
Modified: 1.3/specific_fns/n_state_model.py
URL:
http://svn.gna.org/viewcvs/relax/1.3/specific_fns/n_state_model.py?rev=9558&r1=9557&r2=9558&view=diff
==============================================================================
--- 1.3/specific_fns/n_state_model.py (original)
+++ 1.3/specific_fns/n_state_model.py Tue Sep 22 14:52:46 2009
@@ -26,6 +26,7 @@
# Python module imports.
from math import acos, cos, pi, sqrt
from minfx.generic import generic_minimise
+from minfx.grid import grid
from numpy import array, dot, float64, identity, ones, zeros
from numpy.linalg import inv, norm
from re import search
@@ -1267,45 +1268,37 @@
# If inc is a single int, convert it into an array of that value.
if isinstance(inc, int):
- temp = []
- for j in xrange(n):
- temp.append(inc)
- inc = temp
-
- # Initialise the grid_ops structure.
- grid_ops = []
- """This structure is a list of lists. The first dimension
corresponds to the model
- parameter. The second dimension has the elements: 0, the number of
increments in that
- dimension; 1, the lower limit of the grid; 2, the upper limit of the
grid."""
-
- # Set the grid search options.
- for i in xrange(n):
- # i is in the parameter array.
- if i < len(cdp.params):
- # Probabilities (default values).
- if search('^p', cdp.params[i]):
- grid_ops.append([inc[i], 0.0, 1.0])
-
- # Angles (default values).
- if search('^alpha', cdp.params[i]) or search('^gamma',
cdp.params[i]):
- grid_ops.append([inc[i], 0.0, 2*pi])
- elif search('^beta', cdp.params[i]):
- grid_ops.append([inc[i], 0.0, pi])
-
- # Otherwise this must be an alignment tensor component.
- else:
- grid_ops.append([inc[i], -1e-3, 1e-3])
-
- # Lower bound (if supplied).
- if lower:
- grid_ops[i][1] = lower[i]
-
- # Upper bound (if supplied).
- if upper:
- grid_ops[i][1] = upper[i]
+ inc = [inc]*n
+
+ # Setup the default bounds.
+ if not lower:
+ # Init.
+ lower = []
+ upper = []
+
+ for i in range(n):
+ # i is in the parameter array.
+ if i < len(cdp.params):
+ # Probabilities (default values).
+ if search('^p', cdp.params[i]):
+ lower.append(0.0)
+ upper.append(1.0)
+
+ # Angles (default values).
+ if search('^alpha', cdp.params[i]) or search('^gamma',
cdp.params[i]):
+ lower.append(0.0)
+ upper.append(2*pi)
+ elif search('^beta', cdp.params[i]):
+ lower.append(0.0)
+ upper.append(pi)
+
+ # Otherwise this must be an alignment tensor component.
+ else:
+ lower.append(-1e-3)
+ upper.append(1e-3)
# Minimisation.
- self.minimise(min_algor='grid', min_options=grid_ops,
constraints=constraints, verbosity=verbosity, sim_index=sim_index)
+ self.minimise(min_algor='grid', min_options=[inc, lower, upper],
constraints=constraints, verbosity=verbosity, sim_index=sim_index)
def is_spin_param(self, name):
@@ -1393,6 +1386,8 @@
# Linear constraints.
if constraints:
A, b = self.__linear_constraints(data_types=data_types,
scaling_matrix=scaling_matrix)
+ else:
+ A, b = None, None
# Get the data structures for optimisation using the tensors as base
data sets.
full_tensors, red_tensor_elem, red_tensor_err, full_in_ref_frame =
None, None, None, None
@@ -1412,16 +1407,26 @@
# Set up the class instance containing the target function.
model = N_state_opt(model=cdp.model, N=cdp.N,
init_params=param_vector, full_tensors=full_tensors,
red_data=red_tensor_elem, red_errors=red_tensor_err,
full_in_ref_frame=full_in_ref_frame, pcs=pcs, rdcs=rdcs, pcs_errors=pcs_err,
rdc_errors=rdc_err, pcs_vect=pcs_vect, xh_vect=xh_vect, pcs_const=pcs_dj,
dip_const=rdc_dj, scaling_matrix=scaling_matrix)
+ # Grid search.
+ if search('^[Gg]rid', min_algor):
+ print min_options
+ results = grid(func=model.func, args=(),
num_incs=min_options[0], lower=min_options[1], upper=min_options[2], A=A,
b=b, verbosity=verbosity)
+
+ # Unpack the results.
+ param_vector, func, iter_count = results
+ f_count = iter_count
+ g_count = 0.0
+ h_count = 0.0
+ warning = None
+
# Minimisation.
- if constraints:
+ else:
results = generic_minimise(func=model.func, dfunc=model.dfunc,
d2func=model.d2func, args=(), x0=param_vector, min_algor=min_algor,
min_options=min_options, func_tol=func_tol, grad_tol=grad_tol,
maxiter=max_iterations, A=A, b=b, full_output=1, print_flag=verbosity)
- else:
- results = generic_minimise(func=model.func, dfunc=model.dfunc,
d2func=model.d2func, args=(), x0=param_vector, min_algor=min_algor,
min_options=min_options, func_tol=func_tol, grad_tol=grad_tol,
maxiter=max_iterations, full_output=1, print_flag=verbosity)
- if results == None:
- return
-
- # Disassemble the results.
- param_vector, func, iter_count, f_count, g_count, h_count, warning =
results
+
+ # Unpack the results.
+ if results == None:
+ return
+ param_vector, func, iter_count, f_count, g_count, h_count,
warning = results
# Catch infinite chi-squared values.
if isInf(func):
r9558 - /1.3/specific_fns/n_state_model.py