self.scores = {}
        self.score(val_data, user_ls)
        vbf_dict = self.scores
        results = {u:evaluate_threshold(self.thresh[u], vbf_dict[u]) 
                   for u in (user_ls if (user_ls != None) 
                             else self.thresh.keys())}
        return results


if __name__=='__main__':
    from CV import CV
    from preprocessor import split_samples, load_data, filter_users_val

    P.np.seterr(all='ignore')

    all_data, pkd = filter_users_val(split_samples(load_data()))

    for u in all_data.keys():
        if all_data[u] == []:
            del all_data[u]
            del pkd[u]


    gbfa = CV(lambda: GammaBFAuth(all_data), 
              all_data, 
              pkd)

    with open('./bf_result.csv', 'rw+') as res_file:
        result_writer = csv.writer(res_file)
        result_writer.writerow(['user',
                                'CV_IPR', 'CV_FRR', 'CV_GT', 'CV_IT'])
if __name__=='__main__':
    """
    test_data = {'a' : [coll.defaultdict(list, {'aa' : range(10,100,10),
                                                'ab' : range(1,10)}),
                        coll.defaultdict(list, {'ac' : range(1,10,2),
                                                'aa' : [2,2,2]}),
                        coll.defaultdict(list, {'ad' : range(10, 20, 3)})
                    ],
                 'b' : [coll.defaultdict(list, {'ba' : range(1,20,3),
                                                'bb' : range(1,15)}),
                        coll.defaultdict(list, {'bc' : range(2,20,4)}),
                        coll.defaultdict(list, {'bd' : range(50,300,150)})
                    ],
    }
    """
    test_data = pp.split_samples(pp.load_data())
    for u in test_data.keys():
        if u not in {'9999999','SERLHOU'}:
            del test_data[u]
    print test_data.keys()
    test_cv = CV(DensityAuth, test_data)
    '''

    for i in test_cv.partition_data('shit', test_data['a'], 1):
        f**k.pprint(i)
    '''

    for i in test_cv.validate():
        pass
    print "DONESKI"