def get_naive_big_encoding()

in amzn-smt-prediction/scripts/clean_eager_lazy_data.py [0:0]

• 11 lines of code
• 7 McCabe index (conditional complexity)

def get_naive_big_encoding(param_sets):
    f_dict = {}

    f_dict['constraints'] = sum(p['constraints'] for p in param_sets)
    f_dict['vars']        = sum(p['vars']        for p in param_sets)
    f_dict['a_max']       = max(p['a_max']       for p in param_sets)
    f_dict['b_max']       = max(p['b_max']       for p in param_sets)
    f_dict['k']           = sum(p['k']           for p in param_sets)
    f_dict['w']           = max(p['w']           for p in param_sets)

    f_dict['bound'] = compute_bound(f_dict)
    f_dict['benchmark'] = param_sets[0]['benchmark'] 
    # All dicts in param_sets *should* have the same benchmark
    # (They are multiple parameter sets from the same file)

    return f_dict