Author: bugman
Date: Fri Jul 16 19:13:13 2010
New Revision: 11314
URL: http://svn.gna.org/viewcvs/relax?rev=11314&view=rev
Log:
Pre-defined probabilities can now be used in the 'fixed' N-state model
optimisation.
Modified:
1.3/maths_fns/n_state_model.py
1.3/specific_fns/n_state_model.py
Modified: 1.3/maths_fns/n_state_model.py
URL:
http://svn.gna.org/viewcvs/relax/1.3/maths_fns/n_state_model.py?rev=11314&r1=11313&r2=11314&view=diff
==============================================================================
--- 1.3/maths_fns/n_state_model.py (original)
+++ 1.3/maths_fns/n_state_model.py Fri Jul 16 19:13:13 2010
@@ -39,7 +39,7 @@
class N_state_opt:
"""Class containing the target function of the optimisation of the
N-state model."""
- def __init__(self, model=None, N=None, init_params=None,
full_tensors=None, red_data=None, red_errors=None, full_in_ref_frame=None,
pcs=None, pcs_errors=None, pcs_weights=None, rdcs=None, rdc_errors=None,
rdc_weights=None, xh_vect=None, temp=None, frq=None, dip_const=None,
atomic_pos=None, paramag_centre=None, scaling_matrix=None, centre_fixed=True):
+ def __init__(self, model=None, N=None, init_params=None, probs=None,
full_tensors=None, red_data=None, red_errors=None, full_in_ref_frame=None,
pcs=None, pcs_errors=None, pcs_weights=None, rdcs=None, rdc_errors=None,
rdc_weights=None, xh_vect=None, temp=None, frq=None, dip_const=None,
atomic_pos=None, paramag_centre=None, scaling_matrix=None, centre_fixed=True):
"""Set up the class instance for optimisation.
The N-state models
@@ -107,6 +107,8 @@
@type N: int
@keyword init_params: The initial parameter values.
Optimisation must start at some point!
@type init_params: numpy float64 array
+ @keyword probs: The probabilities for each state c. The
length of this list should be equal to N.
+ @type probs: list of float
@keyword full_tensors: An array of the {Sxx, Syy, Sxy, Sxz,
Syz} values for all full tensors. The format is [Sxx1, Syy1, Sxy1, Sxz1,
Syz1, Sxx2, Syy2, Sxy2, Sxz2, Syz2, ..., Sxxn, Syyn, Sxyn, Sxzn, Syzn]
@type full_tensors: list of rank-2, 3D numpy arrays
@keyword red_data: An array of the {Sxx, Syy, Sxy, Sxz,
Syz} values for all reduced tensors. The format is the same as for
full_tensors.
@@ -361,9 +363,6 @@
# Set up the paramagnetic info.
self.paramag_info()
- # The probability array (all structures have initial equal
probability).
- self.probs = ones(self.N, float64) / self.N
-
# PCS function, gradient, and Hessian matrices.
self.deltaij_theta = zeros((self.num_align, self.num_spins),
float64)
self.ddeltaij_theta = zeros((self.total_num_params,
self.num_align, self.num_spins), float64)
@@ -388,6 +387,14 @@
# Pure tensor optimisation overrides.
if model == 'fixed' and self.centre_fixed:
+ # The probability array.
+ if probs:
+ self.probs = probs
+
+ # All structures have initial equal probability.
+ else:
+ self.probs = ones(self.N, float64) / self.N
+
# The probs are unpacked by self.func in the population model,
so just override that function.
self.func = self.func_tensor_opt
self.dfunc = self.dfunc_tensor_opt
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=11314&r1=11313&r2=11314&view=diff
==============================================================================
--- 1.3/specific_fns/n_state_model.py (original)
+++ 1.3/specific_fns/n_state_model.py Fri Jul 16 19:13:13 2010
@@ -1416,6 +1416,11 @@
# Determine if alignment tensors or RDCs are to be used.
data_types = self._base_data_types()
+ # The probabilities.
+ probs = None
+ if hasattr(cdp, 'probs'):
+ probs = cdp.probs
+
# Diagonal scaling.
scaling_matrix = None
if len(param_vector):
@@ -1451,7 +1456,7 @@
centre_fixed = cdp.paramag_centre_fixed
# 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_weights=pcs_weight, rdc_weights=rdc_weight,
xh_vect=xh_vect, temp=temp, frq=frq, dip_const=rdc_dj, atomic_pos=atomic_pos,
paramag_centre=paramag_centre, scaling_matrix=scaling_matrix,
centre_fixed=centre_fixed)
+ model = N_state_opt(model=cdp.model, N=cdp.N,
init_params=param_vector, probs=probs, 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_weights=pcs_weight, rdc_weights=rdc_weight,
xh_vect=xh_vect, temp=temp, frq=frq, dip_const=rdc_dj, atomic_pos=atomic_pos,
paramag_centre=paramag_centre, scaling_matrix=scaling_matrix,
centre_fixed=centre_fixed)
# Return the data.
return model, param_vector, data_types, scaling_matrix
r11314 - in /1.3: maths_fns/n_state_model.py specific_fns/n_state_model.py