def _read_yaml_object_values_correlated(obj, constraints):
list_ = yaml.safe_load(obj)
for parameter_group in list_:
parameter_names = []
central_values = []
errors = []
for dict_list in parameter_group['values']:
parameter_name, value = list(dict_list.items())[0]
Parameter[parameter_name] # this will raise an error if the parameter doesn't exist!
parameter_names.append(parameter_name)
error_dict = errors_from_string(value)
central_values.append(error_dict['central_value'])
squared_error = 0.
for sym_err in error_dict['symmetric_errors']:
squared_error += sym_err**2
for asym_err in error_dict['asymmetric_errors']:
squared_error += asym_err[0]*asym_err[1]
errors.append(sqrt(squared_error))
correlation = _fix_correlation_matrix(parameter_group['correlation'], len(parameter_names))
covariance = np.outer(np.asarray(errors), np.asarray(errors))*correlation
if not np.all(np.linalg.eigvals(covariance) > 0):
# if the covariance matrix is not positive definite, try a dirty trick:
# multiply all the correlations by 0.99.
n_dim = len(correlation)
correlation = (correlation - np.eye(n_dim))*0.99 + np.eye(n_dim)
covariance = np.outer(np.asarray(errors), np.asarray(errors))*correlation
# if it still isn't positive definite, give up.
assert np.all(np.linalg.eigvals(covariance) > 0), "The covariance matrix is not positive definite!" + str(covariance)
constraints.add_constraint(parameter_names, MultivariateNormalDistribution(central_values, covariance))
def _read_yaml_object_values_correlated(obj, constraints):
list_ = yaml.load(obj)
for parameter_group in list_:
parameter_names = []
central_values = []
errors = []
for dict_list in parameter_group['values']:
parameter_name, value = list(dict_list.items())[0]
Parameter.get_instance(parameter_name) # this will raise an error if the parameter doesn't exist!
parameter_names.append(parameter_name)
error_dict = errors_from_string(value)
central_values.append(error_dict['central_value'])
squared_error = 0.
for sym_err in error_dict['symmetric_errors']:
squared_error += sym_err**2
for asym_err in error_dict['asymmetric_errors']:
squared_error += asym_err[0]*asym_err[1]
errors.append(sqrt(squared_error))
correlation = _fix_correlation_matrix(parameter_group['correlation'], len(parameter_names))
covariance = np.outer(np.asarray(errors), np.asarray(errors))*correlation
if not np.all(np.linalg.eigvals(covariance) > 0):
# if the covariance matrix is not positive definite, try a dirty trick:
# multiply all the correlations by 0.99.
n_dim = len(correlation)
correlation = (correlation - np.eye(n_dim))*0.99 + np.eye(n_dim)
covariance = np.outer(np.asarray(errors), np.asarray(errors))*correlation
# if it still isn't positive definite, give up.
assert np.all(np.linalg.eigvals(covariance) > 0), "The covariance matrix is not positive definite!" + str(covariance)
constraints.add_constraint(parameter_names, MultivariateNormalDistribution(central_values, covariance))