Author: bugman Date: Wed Jan 9 10:20:19 2008 New Revision: 4519 URL: http://svn.gna.org/viewcvs/relax?rev=4519&view=rev Log: Added code to fetch the initial parameter vector. Modified: branches/N_state_model/specific_fns/n_state_model.py Modified: branches/N_state_model/specific_fns/n_state_model.py URL: http://svn.gna.org/viewcvs/relax/branches/N_state_model/specific_fns/n_state_model.py?rev=4519&r1=4518&r2=4519&view=diff ============================================================================== --- branches/N_state_model/specific_fns/n_state_model.py (original) +++ branches/N_state_model/specific_fns/n_state_model.py Wed Jan 9 10:20:19 2008 @@ -97,6 +97,9 @@ @type sim_index: None or int """ + # Create the initial parameter vector. + param_vector = self.assemble_param_vector(sim_index=sim_index) + # Set up the class instance containing the target function. model = N_state_opt() @@ -108,12 +111,12 @@ # Minimisation. if constraints: - results = generic_minimise(func=model.func, args=(), x0=self.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=print_flag) + results = generic_minimise(func=model.func, 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=print_flag) else: - results = generic_minimise(func=model.func, args=(), x0=self.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=print_flag) + results = generic_minimise(func=model.func, 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=print_flag) if results == None: return - self.param_vector, self.func, self.iter_count, self.f_count, self.g_count, self.h_count, self.warning = results + param_vector, func, iter_count, f_count, g_count, h_count, warning = results def return_data_name(self, name):