def compute_optimised_trajectories(problem, input_sample, N, k_choices, local_optimization = False):
    '''
    Calls the procedure to compute the optimum k_choices of trajectories
    from the input_sample.
    If there are groups, then this procedure allocates the groups to the
    correct call here.
    '''
    num_params = problem['num_vars']
    groups = compute_groups_matrix(problem['groups'], num_params)
    
    if np.any((input_sample < 0) | (1 > input_sample)):
        raise ValueError("Input sample must be scaled between 0 and 1")
    
    if _has_gurobi == True and local_optimization == False:
        maximum_combo = return_max_combo(input_sample,
                                         N,
                                         num_params,
                                         k_choices,
                                         groups)
        
    else:
        maximum_combo = find_optimum_combination(input_sample,
                                                 N,
                                                 num_params,
                                                 k_choices,
                                                 groups,
                                                 local_optimization)

    num_groups = None
    if groups is not None:
        num_groups = groups[0].shape[1]

    output = compile_output(input_sample,
                            N,
                            num_params,
                            maximum_combo,
                            num_groups)
    
    return output
dataset/ETHPy150Open SALib/SALib/SALib/sample/morris.py/compute_optimised_trajectories/swapped operands of `>`