def model_statistics(self, model_info=None, spin_id=None, global_stats=None):
"""Return the k, n, and chi2 model statistics.
k - number of parameters.
n - number of data points.
chi2 - the chi-squared value.
@keyword model_info: The model information from model_loop(). This is unused.
@type model_info: None
@keyword spin_id: The spin identification string. This is ignored in the N-state model.
@type spin_id: None or str
@keyword global_stats: A parameter which determines if global or local statistics are returned. For the N-state model, this argument is ignored.
@type global_stats: None or bool
@return: The optimisation statistics, in tuple format, of the number of parameters (k), the number of data points (n), and the chi-squared value (chi2).
@rtype: tuple of (int, int, float)
"""
# Return the values.
return param_num(), num_data_points(), cdp.chi2
def model_statistics(self,
model_info=None,
spin_id=None,
global_stats=None):
"""Return the k, n, and chi2 model statistics.
k - number of parameters.
n - number of data points.
chi2 - the chi-squared value.
@keyword model_info: The model information from model_loop(). This is unused.
@type model_info: None
@keyword spin_id: The spin identification string. This is ignored in the N-state model.
@type spin_id: None or str
@keyword global_stats: A parameter which determines if global or local statistics are returned. For the N-state model, this argument is ignored.
@type global_stats: None or bool
@return: The optimisation statistics, in tuple format, of the number of parameters (k), the number of data points (n), and the chi-squared value (chi2).
@rtype: tuple of (int, int, float)
"""
# Return the values.
return param_num(), num_data_points(), cdp.chi2
def grid_search(self, lower=None, upper=None, inc=None, scaling_matrix=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.
@type lower: list of lists of floats
@param upper: The upper bounds of the grid search which must be equal to the number of parameters in the model.
@type upper: list of lists of floats
@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: list of lists of int
@keyword scaling_matrix: The per-model list of diagonal and square scaling matrices.
@type scaling_matrix: list of numpy rank-2, float64 array or list of None
@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.
@type verbosity: int
"""
# Test if the N-state model has been set up.
if not hasattr(cdp, 'model'):
raise RelaxNoModelError('N-state')
# The number of parameters.
n = param_num()
# Determine the data type.
data_types = 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[0][i*5:i*5+5]
upper_sub = upper[0][i*5:i*5+5]
inc_sub = inc[0][i*5:i*5+5]
# Minimisation of the sub-grid.
self.minimise(min_algor='grid', lower=[lower_sub], upper=[upper_sub], inc=[inc_sub], scaling_matrix=[None], 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, scaling_matrix=scaling_matrix, constraints=constraints, verbosity=verbosity, sim_index=sim_index)
def grid_search(self,
lower=None,
upper=None,
inc=None,
scaling_matrix=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.
@type lower: list of lists of floats
@param upper: The upper bounds of the grid search which must be equal to the number of parameters in the model.
@type upper: list of lists of floats
@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: list of lists of int
@keyword scaling_matrix: The per-model list of diagonal and square scaling matrices.
@type scaling_matrix: list of numpy rank-2, float64 array or list of None
@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.
@type verbosity: int
"""
# Test if the N-state model has been set up.
if not hasattr(cdp, 'model'):
raise RelaxNoModelError('N-state')
# The number of parameters.
n = param_num()
# Determine the data type.
data_types = 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[0][i * 5:i * 5 + 5]
upper_sub = upper[0][i * 5:i * 5 + 5]
inc_sub = inc[0][i * 5:i * 5 + 5]
# Minimisation of the sub-grid.
self.minimise(min_algor='grid',
lower=[lower_sub],
upper=[upper_sub],
inc=[inc_sub],
scaling_matrix=[None],
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,
scaling_matrix=scaling_matrix,
constraints=constraints,
verbosity=verbosity,
sim_index=sim_index)
