diff --git a/Programing_project/Instruction_Programming_project.pdf b/Programing_project/Instruction_Programming_project.pdf
index 5170cb8..fa8e9c7 100644
Binary files a/Programing_project/Instruction_Programming_project.pdf and b/Programing_project/Instruction_Programming_project.pdf differ
diff --git a/Programing_project/data/raw_data.txt b/Programing_project/data/raw_data.txt
index ac63486..5159caa 100644
--- a/Programing_project/data/raw_data.txt
+++ b/Programing_project/data/raw_data.txt
@@ -1 +1,2001 @@
-subjID	trial	delay_later	amount_later	delay_sooner	amount_sooner	choice(1=choosingLL)
1	1	6	10.5	0	10	1
1	2	170	38.3	0	10	1
1	3	28	13.4	0	10	1
1	4	28	31.4	0	10	1
1	5	85	30.9	0	10	1
1	6	28	21.1	0	10	1
1	7	28	13	0	10	1
1	8	1	21.3	0	10	1
1	9	28	21.1	0	10	1
1	10	15	30.1	0	10	1
1	11	1	10.7	0	10	1
1	12	85	36.1	0	10	1
1	13	15	10.5	0	10	1
1	14	6	16.7	0	10	1
1	15	1	11	0	10	1
1	16	15	14.2	0	10	1
1	17	15	12.5	0	10	1
1	18	15	20.7	0	10	1
1	19	6	11	0	10	0
1	20	28	16.9	0	10	1
1	21	15	30.1	0	10	1
1	22	85	24.4	0	10	1
1	23	170	41.3	0	10	1
1	24	15	14.2	0	10	1
1	25	6	10.5	0	10	1
1	26	170	24.4	0	10	1
1	27	15	49	0	10	1
1	28	170	29.7	0	10	1
1	29	1	11.8	0	10	0
1	30	6	13.2	0	10	0
1	31	85	30.9	0	10	1
1	32	6	44	0	10	1
1	33	6	35.1	0	10	1
1	34	28	15.5	0	10	1
1	35	170	43.3	0	10	1
1	36	170	33.9	0	10	1
1	37	1	11	0	10	1
1	38	1	21.3	0	10	1
1	39	85	45	0	10	1
1	40	15	39.6	0	10	1
1	41	85	10.5	0	10	0
1	42	170	15	0	10	1
1	43	170	49.8	0	10	1
1	44	170	24.4	0	10	1
1	45	28	13.4	0	10	1
1	46	1	31.6	0	10	1
1	47	170	35.6	0	10	1
1	48	1	41.9	0	10	NA
1	49	6	17.4	0	10	1
1	50	85	18.4	0	10	1
1	51	85	27.3	0	10	1
1	52	85	26	0	10	1
1	53	170	38.3	0	10	1
1	54	28	21.7	0	10	1
1	55	1	10.7	0	10	1
1	56	170	49.8	0	10	1
1	57	1	11.2	0	10	1
1	58	15	20.7	0	10	1
1	59	6	44	0	10	1
1	60	28	41.1	0	10	1
1	61	28	16.9	0	10	1
1	62	6	14	0	10	1
1	63	1	31.6	0	10	1
1	64	15	18.6	0	10	1
1	65	28	12	0	10	1
1	66	6	13.2	0	10	1
1	67	170	43.3	0	10	1
1	68	28	31.4	0	10	1
1	69	85	19.5	0	10	1
1	70	170	35.6	0	10	1
1	71	85	18.4	0	10	1
1	72	1	12.5	0	10	1
1	73	170	41.3	0	10	1
1	74	170	15	0	10	0
1	75	28	12	0	10	0
1	76	85	36.1	0	10	1
1	77	1	18	0	10	1
1	78	85	10.5	0	10	0
1	79	170	33.9	0	10	1
1	80	6	26.3	0	10	1
1	81	85	45	0	10	1
1	82	28	21.7	0	10	1
1	83	28	13	0	10	0
1	84	85	27.3	0	10	1
1	85	15	18.6	0	10	NA
1	86	15	12.5	0	10	1
1	87	6	26.3	0	10	1
1	88	6	11	0	10	1
1	89	15	10.7	0	10	0
1	90	6	16.7	0	10	1
1	91	28	41.1	0	10	1
1	92	85	26	0	10	1
1	93	85	24.4	0	10	1
1	94	1	12.5	0	10	1
1	95	6	17.4	0	10	1
1	96	6	35.1	0	10	1
1	97	6	14	0	10	1
1	98	15	10.5	0	10	0
1	99	1	11.8	0	10	1
1	100	15	10.7	0	10	1
1	101	15	39.6	0	10	1
1	102	85	19.5	0	10	1
1	103	1	11.2	0	10	1
1	104	170	29.7	0	10	1
1	105	15	49	0	10	1
1	106	1	41.9	0	10	1
1	107	1	18	0	10	1
1	108	28	15.5	0	10	NA
2	1	1	11.8	0	10	0
2	2	170	35.6	0	10	0
2	3	85	10.5	0	10	0
2	4	28	21.1	0	10	1
2	5	28	13	0	10	0
2	6	6	10.5	0	10	0
2	7	15	10.5	0	10	0
2	8	6	17.4	0	10	1
2	9	85	26	0	10	1
2	10	6	35.1	0	10	1
2	11	28	21.7	0	10	1
2	12	6	14	0	10	1
2	13	15	14.2	0	10	0
2	14	1	12.5	0	10	1
2	15	170	38.3	0	10	0
2	16	1	18	0	10	1
2	17	15	39.6	0	10	1
2	18	85	18.4	0	10	0
2	19	28	21.1	0	10	0
2	20	85	19.5	0	10	0
2	21	6	11	0	10	1
2	22	85	30.9	0	10	1
2	23	1	10.7	0	10	1
2	24	28	16.9	0	10	0
2	25	170	29.7	0	10	1
2	26	170	43.3	0	10	0
2	27	6	14	0	10	1
2	28	6	11	0	10	1
2	29	28	41.1	0	10	1
2	30	1	31.6	0	10	1
2	31	15	18.6	0	10	1
2	32	15	14.2	0	10	1
2	33	28	12	0	10	0
2	34	1	21.3	0	10	1
2	35	85	36.1	0	10	1
2	36	85	26	0	10	1
2	37	15	49	0	10	1
2	38	1	41.9	0	10	1
2	39	1	21.3	0	10	NA
2	40	170	41.3	0	10	0
2	41	170	43.3	0	10	0
2	42	15	18.6	0	10	1
2	43	15	49	0	10	1
2	44	170	15	0	10	0
2	45	85	24.4	0	10	1
2	46	15	30.1	0	10	1
2	47	85	18.4	0	10	0
2	48	170	41.3	0	10	0
2	49	28	41.1	0	10	1
2	50	28	31.4	0	10	NA
2	51	6	35.1	0	10	1
2	52	1	11.2	0	10	0
2	53	170	33.9	0	10	0
2	54	28	15.5	0	10	0
2	55	1	18	0	10	1
2	56	15	10.7	0	10	0
2	57	85	45	0	10	1
2	58	85	19.5	0	10	0
2	59	6	44	0	10	1
2	60	85	30.9	0	10	0
2	61	1	11	0	10	0
2	62	170	35.6	0	10	NA
2	63	170	29.7	0	10	0
2	64	6	16.7	0	10	1
2	65	28	15.5	0	10	0
2	66	6	44	0	10	1
2	67	85	10.5	0	10	0
2	68	85	45	0	10	1
2	69	1	11	0	10	0
2	70	15	10.5	0	10	0
2	71	170	49.8	0	10	0
2	72	15	20.7	0	10	0
2	73	6	13.2	0	10	1
2	74	15	12.5	0	10	1
2	75	28	13	0	10	1
2	76	1	10.7	0	10	1
2	77	28	13.4	0	10	NA
2	78	15	39.6	0	10	1
2	79	15	20.7	0	10	1
2	80	1	11.2	0	10	1
2	81	85	24.4	0	10	1
2	82	1	12.5	0	10	0
2	83	170	49.8	0	10	1
2	84	170	33.9	0	10	1
2	85	85	27.3	0	10	NA
2	86	170	24.4	0	10	0
2	87	15	10.7	0	10	0
2	88	6	16.7	0	10	1
2	89	1	11.8	0	10	0
2	90	6	10.5	0	10	0
2	91	28	12	0	10	0
2	92	6	17.4	0	10	1
2	93	28	16.9	0	10	0
2	94	28	13.4	0	10	1
2	95	1	31.6	0	10	1
2	96	85	36.1	0	10	1
2	97	15	30.1	0	10	0
2	98	170	15	0	10	0
2	99	85	27.3	0	10	0
2	100	170	38.3	0	10	0
2	101	15	12.5	0	10	0
2	102	6	26.3	0	10	1
2	103	1	41.9	0	10	1
2	104	6	13.2	0	10	1
2	105	28	21.7	0	10	0
2	106	170	24.4	0	10	0
2	107	28	31.4	0	10	0
2	108	6	26.3	0	10	1
3	1	28	16.9	0	10	0
3	2	1	21.3	0	10	1
3	3	6	44	0	10	1
3	4	170	49.8	0	10	1
3	5	28	13.4	0	10	1
3	6	28	21.1	0	10	1
3	7	15	14.2	0	10	NA
3	8	6	26.3	0	10	1
3	9	85	24.4	0	10	0
3	10	170	41.3	0	10	1
3	11	28	12	0	10	0
3	12	15	39.6	0	10	1
3	13	85	30.9	0	10	1
3	14	28	31.4	0	10	1
3	15	85	10.5	0	10	1
3	16	1	10.7	0	10	1
3	17	28	31.4	0	10	1
3	18	6	26.3	0	10	1
3	19	1	41.9	0	10	1
3	20	6	13.2	0	10	0
3	21	28	41.1	0	10	1
3	22	15	12.5	0	10	1
3	23	15	39.6	0	10	1
3	24	85	10.5	0	10	0
3	25	28	12	0	10	1
3	26	170	38.3	0	10	1
3	27	85	36.1	0	10	1
3	28	1	41.9	0	10	1
3	29	15	10.5	0	10	0
3	30	85	19.5	0	10	0
3	31	85	26	0	10	1
3	32	85	45	0	10	1
3	33	1	12.5	0	10	1
3	34	6	13.2	0	10	1
3	35	15	10.7	0	10	1
3	36	1	11	0	10	1
3	37	15	30.1	0	10	0
3	38	15	20.7	0	10	1
3	39	6	17.4	0	10	1
3	40	6	10.5	0	10	0
3	41	170	15	0	10	1
3	42	15	12.5	0	10	1
3	43	1	31.6	0	10	1
3	44	15	10.5	0	10	0
3	45	170	41.3	0	10	1
3	46	170	15	0	10	0
3	47	15	18.6	0	10	1
3	48	6	17.4	0	10	1
3	49	85	18.4	0	10	0
3	50	170	43.3	0	10	1
3	51	28	21.7	0	10	0
3	52	6	16.7	0	10	0
3	53	170	33.9	0	10	0
3	54	1	18	0	10	1
3	55	1	18	0	10	1
3	56	15	30.1	0	10	1
3	57	1	10.7	0	10	0
3	58	85	27.3	0	10	1
3	59	6	35.1	0	10	1
3	60	85	30.9	0	10	1
3	61	85	24.4	0	10	1
3	62	85	19.5	0	10	0
3	63	170	33.9	0	10	1
3	64	6	10.5	0	10	0
3	65	85	27.3	0	10	1
3	66	28	16.9	0	10	0
3	67	6	35.1	0	10	1
3	68	15	49	0	10	1
3	69	85	26	0	10	1
3	70	85	45	0	10	1
3	71	1	11.8	0	10	1
3	72	170	35.6	0	10	1
3	73	1	31.6	0	10	1
3	74	28	13	0	10	0
3	75	28	21.1	0	10	1
3	76	15	20.7	0	10	1
3	77	15	10.7	0	10	0
3	78	28	15.5	0	10	0
3	79	1	21.3	0	10	1
3	80	6	14	0	10	1
3	81	170	49.8	0	10	1
3	82	85	36.1	0	10	1
3	83	1	11.2	0	10	0
3	84	28	15.5	0	10	0
3	85	170	29.7	0	10	1
3	86	170	24.4	0	10	1
3	87	170	24.4	0	10	1
3	88	28	13.4	0	10	0
3	89	15	18.6	0	10	1
3	90	28	21.7	0	10	1
3	91	85	18.4	0	10	1
3	92	6	16.7	0	10	1
3	93	6	11	0	10	1
3	94	28	41.1	0	10	1
3	95	170	43.3	0	10	1
3	96	6	44	0	10	1
3	97	1	11.2	0	10	1
3	98	6	11	0	10	1
3	99	170	35.6	0	10	1
3	100	15	49	0	10	1
3	101	170	38.3	0	10	1
3	102	28	13	0	10	0
3	103	170	29.7	0	10	1
3	104	1	12.5	0	10	1
3	105	1	11	0	10	1
3	106	1	11.8	0	10	1
3	107	6	14	0	10	0
3	108	15	14.2	0	10	1
4	1	170	41.3	0	10	1
4	2	170	38.3	0	10	1
4	3	28	21.1	0	10	1
4	4	15	20.7	0	10	1
4	5	85	45	0	10	1
4	6	85	45	0	10	1
4	7	28	21.7	0	10	1
4	8	1	11.2	0	10	1
4	9	170	49.8	0	10	1
4	10	6	14	0	10	0
4	11	28	21.7	0	10	1
4	12	1	11.2	0	10	1
4	13	1	31.6	0	10	1
4	14	6	10.5	0	10	1
4	15	1	21.3	0	10	1
4	16	170	43.3	0	10	1
4	17	1	18	0	10	1
4	18	15	10.5	0	10	0
4	19	15	20.7	0	10	1
4	20	15	39.6	0	10	1
4	21	170	33.9	0	10	1
4	22	1	21.3	0	10	1
4	23	85	30.9	0	10	1
4	24	15	18.6	0	10	1
4	25	28	13.4	0	10	1
4	26	170	15	0	10	1
4	27	170	41.3	0	10	1
4	28	85	27.3	0	10	0
4	29	1	11.8	0	10	0
4	30	85	24.4	0	10	1
4	31	15	49	0	10	1
4	32	6	17.4	0	10	1
4	33	6	35.1	0	10	1
4	34	170	15	0	10	0
4	35	6	26.3	0	10	1
4	36	170	35.6	0	10	0
4	37	6	13.2	0	10	1
4	38	28	15.5	0	10	1
4	39	1	11	0	10	1
4	40	15	12.5	0	10	1
4	41	6	13.2	0	10	0
4	42	1	10.7	0	10	1
4	43	6	17.4	0	10	1
4	44	85	10.5	0	10	0
4	45	28	13.4	0	10	1
4	46	1	41.9	0	10	1
4	47	28	13	0	10	1
4	48	28	16.9	0	10	0
4	49	85	36.1	0	10	1
4	50	15	18.6	0	10	1
4	51	85	27.3	0	10	1
4	52	15	49	0	10	1
4	53	15	30.1	0	10	1
4	54	170	29.7	0	10	1
4	55	6	14	0	10	1
4	56	28	41.1	0	10	1
4	57	15	30.1	0	10	1
4	58	15	12.5	0	10	1
4	59	85	30.9	0	10	1
4	60	28	21.1	0	10	1
4	61	6	44	0	10	1
4	62	28	16.9	0	10	1
4	63	6	11	0	10	0
4	64	170	38.3	0	10	1
4	65	85	18.4	0	10	1
4	66	85	19.5	0	10	1
4	67	170	33.9	0	10	0
4	68	170	35.6	0	10	1
4	69	15	14.2	0	10	1
4	70	28	13	0	10	0
4	71	28	31.4	0	10	1
4	72	1	11.8	0	10	0
4	73	1	12.5	0	10	0
4	74	28	31.4	0	10	1
4	75	1	12.5	0	10	1
4	76	28	41.1	0	10	1
4	77	1	10.7	0	10	1
4	78	170	24.4	0	10	1
4	79	6	16.7	0	10	1
4	80	170	24.4	0	10	1
4	81	6	35.1	0	10	1
4	82	1	11	0	10	0
4	83	28	12	0	10	0
4	84	15	10.5	0	10	0
4	85	15	10.7	0	10	0
4	86	28	12	0	10	1
4	87	85	19.5	0	10	1
4	88	6	16.7	0	10	1
4	89	6	11	0	10	0
4	90	15	39.6	0	10	1
4	91	85	24.4	0	10	0
4	92	6	26.3	0	10	1
4	93	85	18.4	0	10	1
4	94	15	14.2	0	10	0
4	95	6	10.5	0	10	0
4	96	1	41.9	0	10	1
4	97	85	36.1	0	10	1
4	98	85	26	0	10	1
4	99	28	15.5	0	10	0
4	100	1	31.6	0	10	1
4	101	6	44	0	10	1
4	102	85	26	0	10	1
4	103	170	29.7	0	10	1
4	104	170	43.3	0	10	1
4	105	170	49.8	0	10	1
4	106	85	10.5	0	10	0
4	107	1	18	0	10	1
4	108	15	10.7	0	10	1
5	1	170	41.3	0	10	1
5	2	85	18.4	0	10	1
5	3	28	21.7	0	10	1
5	4	85	10.5	0	10	0
5	5	15	14.2	0	10	1
5	6	28	21.7	0	10	1
5	7	85	30.9	0	10	1
5	8	85	26	0	10	1
5	9	1	10.7	0	10	1
5	10	28	13	0	10	0
5	11	170	33.9	0	10	1
5	12	85	36.1	0	10	0
5	13	15	30.1	0	10	1
5	14	1	31.6	0	10	1
5	15	6	13.2	0	10	1
5	16	1	11	0	10	1
5	17	85	24.4	0	10	1
5	18	1	41.9	0	10	1
5	19	15	14.2	0	10	0
5	20	15	20.7	0	10	1
5	21	15	10.5	0	10	0
5	22	6	10.5	0	10	1
5	23	85	45	0	10	1
5	24	28	16.9	0	10	1
5	25	1	21.3	0	10	1
5	26	6	14	0	10	1
5	27	28	13.4	0	10	0
5	28	6	17.4	0	10	1
5	29	170	33.9	0	10	0
5	30	15	18.6	0	10	1
5	31	85	45	0	10	1
5	32	28	13.4	0	10	0
5	33	15	10.5	0	10	0
5	34	15	49	0	10	1
5	35	170	43.3	0	10	1
5	36	15	39.6	0	10	1
5	37	85	18.4	0	10	0
5	38	170	49.8	0	10	1
5	39	15	10.7	0	10	0
5	40	170	24.4	0	10	0
5	41	15	39.6	0	10	1
5	42	28	41.1	0	10	1
5	43	85	27.3	0	10	1
5	44	1	18	0	10	1
5	45	1	12.5	0	10	1
5	46	1	11.8	0	10	0
5	47	28	15.5	0	10	0
5	48	170	15	0	10	0
5	49	28	21.1	0	10	1
5	50	6	11	0	10	0
5	51	28	31.4	0	10	1
5	52	1	31.6	0	10	1
5	53	15	20.7	0	10	1
5	54	28	31.4	0	10	1
5	55	1	11.2	0	10	1
5	56	6	11	0	10	1
5	57	6	10.5	0	10	1
5	58	15	10.7	0	10	1
5	59	28	13	0	10	0
5	60	85	26	0	10	1
5	61	6	35.1	0	10	1
5	62	170	35.6	0	10	1
5	63	85	27.3	0	10	1
5	64	85	30.9	0	10	1
5	65	1	41.9	0	10	1
5	66	170	35.6	0	10	1
5	67	28	15.5	0	10	1
5	68	1	11.2	0	10	1
5	69	170	49.8	0	10	1
5	70	15	12.5	0	10	0
5	71	85	19.5	0	10	1
5	72	6	16.7	0	10	1
5	73	1	10.7	0	10	1
5	74	6	44	0	10	1
5	75	170	29.7	0	10	1
5	76	6	17.4	0	10	1
5	77	1	21.3	0	10	1
5	78	170	38.3	0	10	0
5	79	170	24.4	0	10	0
5	80	6	35.1	0	10	1
5	81	1	12.5	0	10	1
5	82	1	11.8	0	10	1
5	83	28	12	0	10	1
5	84	28	12	0	10	1
5	85	85	36.1	0	10	1
5	86	170	29.7	0	10	1
5	87	170	43.3	0	10	1
5	88	1	11	0	10	0
5	89	85	24.4	0	10	0
5	90	15	30.1	0	10	1
5	91	6	14	0	10	0
5	92	170	38.3	0	10	1
5	93	6	44	0	10	1
5	94	6	16.7	0	10	1
5	95	6	26.3	0	10	1
5	96	28	16.9	0	10	0
5	97	85	10.5	0	10	0
5	98	15	18.6	0	10	1
5	99	28	21.1	0	10	1
5	100	170	15	0	10	0
5	101	15	49	0	10	1
5	102	170	41.3	0	10	1
5	103	6	13.2	0	10	1
5	104	85	19.5	0	10	1
5	105	6	26.3	0	10	1
5	106	28	41.1	0	10	1
5	107	1	18	0	10	1
5	108	15	12.5	0	10	0
6	1	15	18.6	0	10	1
6	2	1	10.7	0	10	1
6	3	1	11.2	0	10	1
6	4	15	18.6	0	10	1
6	5	28	16.9	0	10	1
6	6	85	27.3	0	10	1
6	7	28	13	0	10	1
6	8	15	10.7	0	10	0
6	9	170	33.9	0	10	1
6	10	15	14.2	0	10	1
6	11	15	10.5	0	10	1
6	12	170	33.9	0	10	1
6	13	15	39.6	0	10	1
6	14	1	11.8	0	10	1
6	15	15	10.7	0	10	0
6	16	28	21.1	0	10	1
6	17	85	18.4	0	10	0
6	18	1	18	0	10	1
6	19	1	11	0	10	1
6	20	15	12.5	0	10	1
6	21	170	38.3	0	10	1
6	22	1	11	0	10	0
6	23	6	16.7	0	10	0
6	24	28	16.9	0	10	1
6	25	6	17.4	0	10	1
6	26	1	12.5	0	10	1
6	27	85	18.4	0	10	0
6	28	28	31.4	0	10	1
6	29	6	26.3	0	10	1
6	30	85	45	0	10	1
6	31	85	24.4	0	10	1
6	32	6	16.7	0	10	1
6	33	85	10.5	0	10	0
6	34	6	44	0	10	1
6	35	1	12.5	0	10	1
6	36	170	15	0	10	0
6	37	170	15	0	10	0
6	38	15	39.6	0	10	1
6	39	85	19.5	0	10	1
6	40	15	10.5	0	10	1
6	41	85	27.3	0	10	1
6	42	170	29.7	0	10	1
6	43	170	24.4	0	10	1
6	44	15	14.2	0	10	0
6	45	6	11	0	10	1
6	46	1	41.9	0	10	1
6	47	1	31.6	0	10	1
6	48	28	13.4	0	10	1
6	49	15	30.1	0	10	1
6	50	28	41.1	0	10	1
6	51	28	13	0	10	1
6	52	85	19.5	0	10	1
6	53	170	43.3	0	10	1
6	54	28	41.1	0	10	1
6	55	6	17.4	0	10	1
6	56	15	20.7	0	10	1
6	57	15	30.1	0	10	1
6	58	170	49.8	0	10	1
6	59	85	36.1	0	10	1
6	60	85	30.9	0	10	1
6	61	170	35.6	0	10	1
6	62	15	20.7	0	10	1
6	63	1	11.2	0	10	0
6	64	170	24.4	0	10	1
6	65	28	21.7	0	10	1
6	66	1	10.7	0	10	1
6	67	85	45	0	10	1
6	68	6	10.5	0	10	1
6	69	15	12.5	0	10	1
6	70	28	31.4	0	10	1
6	71	170	38.3	0	10	1
6	72	1	18	0	10	1
6	73	1	21.3	0	10	1
6	74	6	35.1	0	10	1
6	75	28	13.4	0	10	0
6	76	85	10.5	0	10	0
6	77	28	12	0	10	1
6	78	6	10.5	0	10	1
6	79	1	11.8	0	10	1
6	80	6	13.2	0	10	1
6	81	1	41.9	0	10	1
6	82	85	36.1	0	10	1
6	83	28	15.5	0	10	1
6	84	85	30.9	0	10	1
6	85	170	43.3	0	10	1
6	86	85	26	0	10	1
6	87	28	21.1	0	10	1
6	88	28	15.5	0	10	0
6	89	6	11	0	10	1
6	90	1	31.6	0	10	1
6	91	170	49.8	0	10	1
6	92	1	21.3	0	10	1
6	93	28	21.7	0	10	1
6	94	170	41.3	0	10	1
6	95	15	49	0	10	1
6	96	6	35.1	0	10	1
6	97	15	49	0	10	1
6	98	6	26.3	0	10	1
6	99	28	12	0	10	1
6	100	6	14	0	10	1
6	101	6	44	0	10	1
6	102	170	29.7	0	10	1
6	103	6	14	0	10	1
6	104	170	35.6	0	10	1
6	105	85	26	0	10	1
6	106	6	13.2	0	10	1
6	107	170	41.3	0	10	1
6	108	85	24.4	0	10	1
7	1	28	13	0	10	1
7	2	28	41.1	0	10	1
7	3	170	29.7	0	10	0
7	4	1	10.7	0	10	1
7	5	6	17.4	0	10	1
7	6	15	12.5	0	10	NA
7	7	15	18.6	0	10	1
7	8	170	24.4	0	10	0
7	9	1	11	0	10	1
7	10	28	16.9	0	10	1
7	11	170	41.3	0	10	1
7	12	15	10.5	0	10	0
7	13	6	10.5	0	10	1
7	14	28	12	0	10	1
7	15	170	24.4	0	10	1
7	16	1	10.7	0	10	0
7	17	6	35.1	0	10	1
7	18	85	19.5	0	10	0
7	19	6	26.3	0	10	1
7	20	85	26	0	10	1
7	21	1	11.2	0	10	1
7	22	6	16.7	0	10	1
7	23	28	31.4	0	10	1
7	24	170	35.6	0	10	0
7	25	1	21.3	0	10	1
7	26	15	20.7	0	10	1
7	27	15	14.2	0	10	1
7	28	85	24.4	0	10	1
7	29	1	11	0	10	1
7	30	85	27.3	0	10	1
7	31	15	18.6	0	10	1
7	32	6	16.7	0	10	1
7	33	28	21.1	0	10	1
7	34	15	39.6	0	10	1
7	35	28	31.4	0	10	1
7	36	1	11.8	0	10	1
7	37	170	38.3	0	10	1
7	38	1	12.5	0	10	1
7	39	1	11.8	0	10	1
7	40	28	21.7	0	10	1
7	41	28	21.1	0	10	1
7	42	170	33.9	0	10	0
7	43	6	14	0	10	1
7	44	15	12.5	0	10	1
7	45	15	10.7	0	10	1
7	46	1	41.9	0	10	NA
7	47	1	18	0	10	1
7	48	15	14.2	0	10	1
7	49	6	11	0	10	0
7	50	85	30.9	0	10	1
7	51	170	49.8	0	10	1
7	52	6	44	0	10	1
7	53	85	45	0	10	1
7	54	170	49.8	0	10	1
7	55	85	10.5	0	10	0
7	56	15	49	0	10	1
7	57	170	15	0	10	0
7	58	6	13.2	0	10	1
7	59	170	35.6	0	10	1
7	60	170	29.7	0	10	0
7	61	170	15	0	10	0
7	62	28	15.5	0	10	1
7	63	28	21.7	0	10	1
7	64	85	45	0	10	1
7	65	28	13.4	0	10	0
7	66	6	44	0	10	1
7	67	6	10.5	0	10	1
7	68	85	36.1	0	10	1
7	69	6	14	0	10	1
7	70	170	43.3	0	10	1
7	71	28	12	0	10	0
7	72	85	24.4	0	10	1
7	73	85	18.4	0	10	0
7	74	15	10.7	0	10	0
7	75	6	35.1	0	10	1
7	76	15	49	0	10	1
7	77	85	19.5	0	10	0
7	78	1	12.5	0	10	1
7	79	1	18	0	10	1
7	80	28	13	0	10	0
7	81	6	17.4	0	10	1
7	82	1	21.3	0	10	1
7	83	15	30.1	0	10	1
7	84	85	26	0	10	0
7	85	85	30.9	0	10	1
7	86	170	33.9	0	10	0
7	87	15	39.6	0	10	1
7	88	1	41.9	0	10	1
7	89	170	43.3	0	10	1
7	90	28	16.9	0	10	0
7	91	85	10.5	0	10	0
7	92	1	31.6	0	10	1
7	93	6	26.3	0	10	1
7	94	15	30.1	0	10	1
7	95	1	31.6	0	10	1
7	96	6	13.2	0	10	1
7	97	170	38.3	0	10	1
7	98	85	36.1	0	10	1
7	99	170	41.3	0	10	1
7	100	28	13.4	0	10	1
7	101	28	15.5	0	10	0
7	102	15	10.5	0	10	0
7	103	6	11	0	10	0
7	104	15	20.7	0	10	1
7	105	85	27.3	0	10	0
7	106	28	41.1	0	10	1
7	107	85	18.4	0	10	1
7	108	1	11.2	0	10	0
8	1	85	19.5	0	10	0
8	2	85	19.5	0	10	0
8	3	28	21.1	0	10	0
8	4	1	11.2	0	10	0
8	5	170	33.9	0	10	0
8	6	85	18.4	0	10	1
8	7	15	20.7	0	10	1
8	8	1	21.3	0	10	1
8	9	15	14.2	0	10	0
8	10	85	30.9	0	10	0
8	11	1	11	0	10	1
8	12	170	49.8	0	10	1
8	13	1	41.9	0	10	1
8	14	6	44	0	10	1
8	15	170	38.3	0	10	1
8	16	28	12	0	10	0
8	17	6	10.5	0	10	0
8	18	28	13	0	10	0
8	19	6	14	0	10	1
8	20	170	43.3	0	10	0
8	21	6	17.4	0	10	1
8	22	1	18	0	10	1
8	23	85	36.1	0	10	0
8	24	15	10.5	0	10	0
8	25	85	24.4	0	10	1
8	26	170	29.7	0	10	0
8	27	6	14	0	10	1
8	28	15	12.5	0	10	0
8	29	28	15.5	0	10	0
8	30	85	45	0	10	1
8	31	28	13.4	0	10	0
8	32	6	16.7	0	10	1
8	33	170	49.8	0	10	0
8	34	6	17.4	0	10	1
8	35	85	26	0	10	1
8	36	1	10.7	0	10	0
8	37	6	11	0	10	1
8	38	1	11.8	0	10	1
8	39	1	12.5	0	10	0
8	40	85	26	0	10	0
8	41	15	10.7	0	10	0
8	42	170	35.6	0	10	1
8	43	85	27.3	0	10	1
8	44	170	43.3	0	10	0
8	45	28	13.4	0	10	0
8	46	28	12	0	10	0
8	47	1	31.6	0	10	1
8	48	6	13.2	0	10	1
8	49	85	36.1	0	10	1
8	50	28	21.7	0	10	1
8	51	15	18.6	0	10	0
8	52	85	27.3	0	10	0
8	53	6	26.3	0	10	1
8	54	1	41.9	0	10	1
8	55	15	30.1	0	10	1
8	56	1	10.7	0	10	0
8	57	170	15	0	10	0
8	58	6	10.5	0	10	0
8	59	28	31.4	0	10	1
8	60	28	41.1	0	10	1
8	61	170	29.7	0	10	0
8	62	1	11.8	0	10	0
8	63	15	18.6	0	10	NA
8	64	1	11	0	10	0
8	65	170	41.3	0	10	1
8	66	15	39.6	0	10	1
8	67	28	31.4	0	10	0
8	68	6	16.7	0	10	1
8	69	15	49	0	10	1
8	70	85	45	0	10	1
8	71	170	24.4	0	10	1
8	72	85	24.4	0	10	1
8	73	1	18	0	10	1
8	74	85	10.5	0	10	0
8	75	28	21.7	0	10	1
8	76	28	16.9	0	10	0
8	77	6	44	0	10	1
8	78	170	33.9	0	10	1
8	79	6	11	0	10	1
8	80	28	13	0	10	1
8	81	28	41.1	0	10	1
8	82	6	13.2	0	10	1
8	83	28	15.5	0	10	0
8	84	15	49	0	10	1
8	85	15	14.2	0	10	1
8	86	170	41.3	0	10	1
8	87	15	12.5	0	10	0
8	88	85	18.4	0	10	1
8	89	1	12.5	0	10	1
8	90	15	20.7	0	10	0
8	91	6	26.3	0	10	1
8	92	170	24.4	0	10	0
8	93	28	21.1	0	10	1
8	94	15	10.5	0	10	0
8	95	6	35.1	0	10	1
8	96	85	30.9	0	10	1
8	97	1	21.3	0	10	1
8	98	15	39.6	0	10	1
8	99	170	35.6	0	10	1
8	100	15	10.7	0	10	1
8	101	85	10.5	0	10	0
8	102	28	16.9	0	10	0
8	103	170	15	0	10	0
8	104	170	38.3	0	10	0
8	105	6	35.1	0	10	1
8	106	1	31.6	0	10	1
8	107	15	30.1	0	10	1
8	108	1	11.2	0	10	1
9	1	1	11.2	0	10	1
9	2	6	10.5	0	10	0
9	3	28	31.4	0	10	1
9	4	15	49	0	10	1
9	5	15	12.5	0	10	1
9	6	170	33.9	0	10	1
9	7	170	35.6	0	10	0
9	8	6	17.4	0	10	1
9	9	1	21.3	0	10	1
9	10	1	10.7	0	10	0
9	11	1	11.8	0	10	1
9	12	1	31.6	0	10	1
9	13	6	16.7	0	10	0
9	14	1	10.7	0	10	1
9	15	170	15	0	10	0
9	16	170	43.3	0	10	1
9	17	85	27.3	0	10	0
9	18	28	21.7	0	10	1
9	19	1	11	0	10	0
9	20	1	11.8	0	10	1
9	21	1	12.5	0	10	1
9	22	6	16.7	0	10	1
9	23	170	35.6	0	10	1
9	24	6	11	0	10	1
9	25	85	30.9	0	10	0
9	26	28	13	0	10	0
9	27	28	41.1	0	10	1
9	28	85	10.5	0	10	0
9	29	1	11.2	0	10	1
9	30	85	36.1	0	10	1
9	31	1	12.5	0	10	1
9	32	6	26.3	0	10	1
9	33	170	33.9	0	10	1
9	34	170	43.3	0	10	0
9	35	85	10.5	0	10	0
9	36	170	49.8	0	10	0
9	37	15	18.6	0	10	1
9	38	6	14	0	10	1
9	39	6	11	0	10	0
9	40	15	39.6	0	10	1
9	41	85	19.5	0	10	0
9	42	15	10.7	0	10	0
9	43	85	36.1	0	10	1
9	44	1	18	0	10	0
9	45	170	49.8	0	10	1
9	46	15	20.7	0	10	1
9	47	1	11	0	10	1
9	48	28	13.4	0	10	1
9	49	15	20.7	0	10	1
9	50	1	18	0	10	1
9	51	85	18.4	0	10	1
9	52	85	18.4	0	10	0
9	53	85	26	0	10	1
9	54	28	31.4	0	10	1
9	55	6	44	0	10	1
9	56	6	13.2	0	10	0
9	57	6	10.5	0	10	0
9	58	28	12	0	10	0
9	59	15	10.5	0	10	0
9	60	6	17.4	0	10	1
9	61	170	24.4	0	10	0
9	62	15	30.1	0	10	1
9	63	6	35.1	0	10	1
9	64	15	10.7	0	10	1
9	65	15	14.2	0	10	1
9	66	170	41.3	0	10	1
9	67	28	21.1	0	10	1
9	68	6	26.3	0	10	1
9	69	15	14.2	0	10	1
9	70	85	24.4	0	10	0
9	71	85	27.3	0	10	0
9	72	28	13.4	0	10	1
9	73	170	29.7	0	10	0
9	74	28	15.5	0	10	0
9	75	85	45	0	10	1
9	76	170	38.3	0	10	0
9	77	28	16.9	0	10	1
9	78	6	35.1	0	10	1
9	79	85	19.5	0	10	0
9	80	15	18.6	0	10	1
9	81	15	12.5	0	10	1
9	82	85	30.9	0	10	0
9	83	28	12	0	10	1
9	84	28	21.7	0	10	1
9	85	28	13	0	10	0
9	86	1	41.9	0	10	1
9	87	15	39.6	0	10	1
9	88	6	13.2	0	10	0
9	89	1	21.3	0	10	1
9	90	170	15	0	10	0
9	91	15	30.1	0	10	1
9	92	85	26	0	10	0
9	93	15	49	0	10	1
9	94	85	45	0	10	1
9	95	6	14	0	10	0
9	96	170	38.3	0	10	1
9	97	170	29.7	0	10	0
9	98	28	16.9	0	10	0
9	99	6	44	0	10	1
9	100	1	31.6	0	10	1
9	101	15	10.5	0	10	0
9	102	28	41.1	0	10	1
9	103	85	24.4	0	10	0
9	104	28	15.5	0	10	0
9	105	28	21.1	0	10	1
9	106	1	41.9	0	10	1
9	107	170	41.3	0	10	1
9	108	170	24.4	0	10	0
10	1	170	41.3	0	10	0
10	2	6	10.5	0	10	0
10	3	170	15	0	10	0
10	4	85	27.3	0	10	0
10	5	170	15	0	10	0
10	6	28	13	0	10	0
10	7	6	35.1	0	10	1
10	8	15	14.2	0	10	1
10	9	85	19.5	0	10	0
10	10	170	43.3	0	10	1
10	11	85	45	0	10	1
10	12	1	41.9	0	10	1
10	13	15	30.1	0	10	1
10	14	85	26	0	10	1
10	15	28	12	0	10	1
10	16	1	11.8	0	10	1
10	17	15	10.7	0	10	0
10	18	6	44	0	10	1
10	19	1	18	0	10	1
10	20	28	12	0	10	0
10	21	15	20.7	0	10	1
10	22	28	41.1	0	10	1
10	23	15	39.6	0	10	1
10	24	85	26	0	10	0
10	25	6	26.3	0	10	1
10	26	6	35.1	0	10	1
10	27	6	26.3	0	10	1
10	28	15	10.5	0	10	0
10	29	1	31.6	0	10	1
10	30	170	41.3	0	10	0
10	31	6	10.5	0	10	1
10	32	1	11.2	0	10	0
10	33	170	29.7	0	10	0
10	34	85	45	0	10	0
10	35	15	12.5	0	10	0
10	36	170	38.3	0	10	0
10	37	85	19.5	0	10	0
10	38	28	13.4	0	10	0
10	39	28	13.4	0	10	0
10	40	15	30.1	0	10	1
10	41	28	41.1	0	10	1
10	42	15	10.5	0	10	0
10	43	170	33.9	0	10	0
10	44	6	14	0	10	1
10	45	170	35.6	0	10	1
10	46	85	10.5	0	10	0
10	47	85	30.9	0	10	1
10	48	28	15.5	0	10	0
10	49	15	39.6	0	10	1
10	50	6	13.2	0	10	1
10	51	1	10.7	0	10	1
10	52	15	14.2	0	10	1
10	53	6	11	0	10	0
10	54	6	17.4	0	10	1
10	55	170	24.4	0	10	1
10	56	85	18.4	0	10	1
10	57	28	31.4	0	10	1
10	58	28	21.7	0	10	1
10	59	15	18.6	0	10	1
10	60	85	10.5	0	10	0
10	61	6	16.7	0	10	1
10	62	85	18.4	0	10	0
10	63	6	44	0	10	1
10	64	1	18	0	10	1
10	65	28	16.9	0	10	0
10	66	15	10.7	0	10	0
10	67	1	10.7	0	10	1
10	68	15	49	0	10	1
10	69	170	38.3	0	10	1
10	70	28	15.5	0	10	0
10	71	28	31.4	0	10	1
10	72	6	14	0	10	1
10	73	170	35.6	0	10	0
10	74	1	12.5	0	10	1
10	75	15	18.6	0	10	0
10	76	1	31.6	0	10	1
10	77	28	16.9	0	10	1
10	78	1	21.3	0	10	1
10	79	15	12.5	0	10	0
10	80	170	49.8	0	10	0
10	81	85	27.3	0	10	0
10	82	6	16.7	0	10	1
10	83	85	36.1	0	10	0
10	84	85	36.1	0	10	1
10	85	6	17.4	0	10	1
10	86	1	11	0	10	0
10	87	6	13.2	0	10	0
10	88	170	29.7	0	10	0
10	89	1	11.2	0	10	0
10	90	1	41.9	0	10	1
10	91	170	33.9	0	10	0
10	92	1	11.8	0	10	0
10	93	15	49	0	10	1
10	94	1	21.3	0	10	0
10	95	85	30.9	0	10	0
10	96	15	20.7	0	10	1
10	97	28	21.1	0	10	0
10	98	170	24.4	0	10	0
10	99	85	24.4	0	10	0
10	100	85	24.4	0	10	0
10	101	28	21.1	0	10	0
10	102	28	21.7	0	10	1
10	103	170	49.8	0	10	1
10	104	6	11	0	10	1
10	105	1	12.5	0	10	1
10	106	28	13	0	10	0
10	107	170	43.3	0	10	0
10	108	1	11	0	10	0
11	1	6	10.5	0	10	0
11	2	85	36.1	0	10	1
11	3	85	27.3	0	10	0
11	4	6	16.7	0	10	1
11	5	1	31.6	0	10	1
11	6	170	33.9	0	10	0
11	7	15	10.5	0	10	0
11	8	170	35.6	0	10	0
11	9	15	10.7	0	10	0
11	10	15	10.7	0	10	1
11	11	170	15	0	10	0
11	12	85	26	0	10	0
11	13	28	21.1	0	10	1
11	14	170	24.4	0	10	0
11	15	28	13	0	10	0
11	16	15	12.5	0	10	1
11	17	85	19.5	0	10	0
11	18	85	26	0	10	0
11	19	6	11	0	10	0
11	20	6	13.2	0	10	0
11	21	28	15.5	0	10	0
11	22	170	41.3	0	10	0
11	23	6	14	0	10	1
11	24	1	21.3	0	10	1
11	25	85	18.4	0	10	1
11	26	28	12	0	10	1
11	27	15	49	0	10	1
11	28	85	45	0	10	1
11	29	170	41.3	0	10	0
11	30	170	33.9	0	10	0
11	31	28	21.7	0	10	1
11	32	15	18.6	0	10	1
11	33	1	12.5	0	10	0
11	34	1	10.7	0	10	1
11	35	28	21.1	0	10	0
11	36	170	35.6	0	10	0
11	37	1	11.2	0	10	1
11	38	85	19.5	0	10	1
11	39	1	41.9	0	10	1
11	40	28	16.9	0	10	0
11	41	15	30.1	0	10	1
11	42	15	20.7	0	10	0
11	43	15	14.2	0	10	1
11	44	28	13	0	10	1
11	45	15	12.5	0	10	1
11	46	170	43.3	0	10	1
11	47	170	49.8	0	10	1
11	48	6	10.5	0	10	1
11	49	15	30.1	0	10	1
11	50	28	41.1	0	10	1
11	51	28	41.1	0	10	1
11	52	6	26.3	0	10	1
11	53	85	10.5	0	10	0
11	54	6	26.3	0	10	1
11	55	6	44	0	10	1
11	56	85	30.9	0	10	1
11	57	85	24.4	0	10	0
11	58	15	39.6	0	10	1
11	59	1	41.9	0	10	1
11	60	170	49.8	0	10	0
11	61	28	31.4	0	10	1
11	62	28	15.5	0	10	1
11	63	28	12	0	10	0
11	64	6	35.1	0	10	1
11	65	85	24.4	0	10	0
11	66	15	49	0	10	1
11	67	15	39.6	0	10	1
11	68	1	31.6	0	10	1
11	69	85	36.1	0	10	0
11	70	15	14.2	0	10	1
11	71	28	16.9	0	10	0
11	72	6	35.1	0	10	1
11	73	170	15	0	10	0
11	74	1	12.5	0	10	1
11	75	15	20.7	0	10	0
11	76	170	24.4	0	10	0
11	77	85	18.4	0	10	0
11	78	6	17.4	0	10	1
11	79	28	31.4	0	10	1
11	80	1	10.7	0	10	1
11	81	6	11	0	10	1
11	82	1	11.8	0	10	0
11	83	170	43.3	0	10	1
11	84	1	18	0	10	1
11	85	1	11.8	0	10	1
11	86	6	14	0	10	1
11	87	85	10.5	0	10	0
11	88	85	30.9	0	10	0
11	89	85	27.3	0	10	0
11	90	28	13.4	0	10	0
11	91	6	17.4	0	10	1
11	92	170	38.3	0	10	0
11	93	6	16.7	0	10	1
11	94	170	38.3	0	10	0
11	95	1	18	0	10	1
11	96	1	11	0	10	1
11	97	170	29.7	0	10	0
11	98	170	29.7	0	10	0
11	99	15	18.6	0	10	1
11	100	15	10.5	0	10	0
11	101	1	21.3	0	10	1
11	102	1	11.2	0	10	0
11	103	28	13.4	0	10	0
11	104	85	45	0	10	1
11	105	28	21.7	0	10	1
11	106	1	11	0	10	0
11	107	6	13.2	0	10	1
11	108	6	44	0	10	1
12	1	1	11.2	0	10	0
12	2	15	20.7	0	10	1
12	3	6	10.5	0	10	0
12	4	6	35.1	0	10	1
12	5	28	13	0	10	0
12	6	1	21.3	0	10	1
12	7	170	35.6	0	10	0
12	8	1	11	0	10	0
12	9	1	31.6	0	10	1
12	10	85	10.5	0	10	0
12	11	28	13	0	10	0
12	12	170	43.3	0	10	0
12	13	170	29.7	0	10	0
12	14	85	24.4	0	10	0
12	15	85	27.3	0	10	NA
12	16	85	27.3	0	10	1
12	17	28	16.9	0	10	1
12	18	170	41.3	0	10	0
12	19	28	13.4	0	10	0
12	20	170	38.3	0	10	0
12	21	170	43.3	0	10	1
12	22	15	12.5	0	10	0
12	23	15	10.7	0	10	0
12	24	85	45	0	10	1
12	25	170	15	0	10	0
12	26	28	12	0	10	1
12	27	1	41.9	0	10	1
12	28	15	39.6	0	10	1
12	29	6	11	0	10	1
12	30	170	29.7	0	10	0
12	31	170	49.8	0	10	1
12	32	15	10.7	0	10	1
12	33	85	10.5	0	10	1
12	34	170	15	0	10	0
12	35	170	41.3	0	10	1
12	36	6	16.7	0	10	1
12	37	15	18.6	0	10	1
12	38	15	14.2	0	10	1
12	39	6	35.1	0	10	1
12	40	6	13.2	0	10	1
12	41	1	12.5	0	10	1
12	42	6	17.4	0	10	1
12	43	1	18	0	10	1
12	44	1	21.3	0	10	1
12	45	1	11.2	0	10	NA
12	46	1	12.5	0	10	1
12	47	1	41.9	0	10	1
12	48	15	30.1	0	10	1
12	49	6	17.4	0	10	1
12	50	15	10.5	0	10	0
12	51	15	14.2	0	10	0
12	52	28	41.1	0	10	1
12	53	85	45	0	10	1
12	54	15	39.6	0	10	1
12	55	28	15.5	0	10	0
12	56	85	30.9	0	10	0
12	57	85	36.1	0	10	1
12	58	170	35.6	0	10	0
12	59	6	16.7	0	10	1
12	60	6	13.2	0	10	0
12	61	85	30.9	0	10	1
12	62	15	10.5	0	10	0
12	63	28	12	0	10	0
12	64	1	11	0	10	1
12	65	15	18.6	0	10	1
12	66	6	10.5	0	10	1
12	67	6	11	0	10	0
12	68	15	20.7	0	10	1
12	69	28	13.4	0	10	1
12	70	1	10.7	0	10	1
12	71	6	44	0	10	1
12	72	170	38.3	0	10	NA
12	73	28	31.4	0	10	1
12	74	15	12.5	0	10	1
12	75	170	33.9	0	10	0
12	76	15	49	0	10	1
12	77	85	26	0	10	0
12	78	85	18.4	0	10	0
12	79	1	11.8	0	10	0
12	80	85	18.4	0	10	0
12	81	85	24.4	0	10	1
12	82	170	49.8	0	10	0
12	83	28	21.7	0	10	1
12	84	28	16.9	0	10	1
12	85	1	18	0	10	0
12	86	6	26.3	0	10	0
12	87	28	21.7	0	10	NA
12	88	6	26.3	0	10	1
12	89	6	44	0	10	1
12	90	28	21.1	0	10	1
12	91	85	36.1	0	10	1
12	92	85	26	0	10	0
12	93	28	41.1	0	10	1
12	94	28	21.1	0	10	1
12	95	28	31.4	0	10	1
12	96	1	10.7	0	10	0
12	97	15	30.1	0	10	1
12	98	1	31.6	0	10	1
12	99	85	19.5	0	10	0
12	100	170	24.4	0	10	0
12	101	15	49	0	10	1
12	102	6	14	0	10	1
12	103	85	19.5	0	10	1
12	104	28	15.5	0	10	0
12	105	170	24.4	0	10	0
12	106	1	11.8	0	10	0
12	107	6	14	0	10	1
12	108	170	33.9	0	10	0
13	1	170	41.3	0	10	0
13	2	15	10.5	0	10	0
13	3	170	15	0	10	0
13	4	15	12.5	0	10	1
13	5	85	45	0	10	1
13	6	6	44	0	10	1
13	7	1	11.2	0	10	1
13	8	170	29.7	0	10	0
13	9	85	27.3	0	10	1
13	10	1	12.5	0	10	1
13	11	15	20.7	0	10	1
13	12	1	18	0	10	1
13	13	6	16.7	0	10	1
13	14	28	12	0	10	0
13	15	6	35.1	0	10	1
13	16	15	39.6	0	10	1
13	17	28	41.1	0	10	1
13	18	15	18.6	0	10	1
13	19	1	11.2	0	10	1
13	20	85	36.1	0	10	0
13	21	15	10.5	0	10	0
13	22	170	41.3	0	10	1
13	23	28	16.9	0	10	1
13	24	85	26	0	10	0
13	25	28	16.9	0	10	1
13	26	6	35.1	0	10	1
13	27	85	24.4	0	10	1
13	28	85	45	0	10	1
13	29	1	11.8	0	10	1
13	30	170	49.8	0	10	1
13	31	170	33.9	0	10	0
13	32	28	13.4	0	10	1
13	33	1	41.9	0	10	1
13	34	6	26.3	0	10	1
13	35	170	35.6	0	10	1
13	36	6	13.2	0	10	1
13	37	170	29.7	0	10	0
13	38	1	11.8	0	10	0
13	39	85	27.3	0	10	1
13	40	28	21.7	0	10	1
13	41	6	14	0	10	0
13	42	1	11	0	10	1
13	43	6	14	0	10	1
13	44	170	43.3	0	10	1
13	45	15	10.7	0	10	1
13	46	170	24.4	0	10	0
13	47	28	21.1	0	10	1
13	48	6	11	0	10	1
13	49	15	39.6	0	10	1
13	50	6	13.2	0	10	1
13	51	15	10.7	0	10	0
13	52	85	10.5	0	10	0
13	53	85	18.4	0	10	1
13	54	1	12.5	0	10	1
13	55	15	30.1	0	10	1
13	56	85	24.4	0	10	0
13	57	28	12	0	10	0
13	58	15	49	0	10	1
13	59	28	41.1	0	10	1
13	60	170	15	0	10	0
13	61	85	26	0	10	1
13	62	15	18.6	0	10	1
13	63	28	13	0	10	0
13	64	28	15.5	0	10	0
13	65	28	31.4	0	10	1
13	66	85	30.9	0	10	1
13	67	28	13.4	0	10	0
13	68	85	10.5	0	10	0
13	69	1	18	0	10	1
13	70	28	31.4	0	10	1
13	71	170	33.9	0	10	0
13	72	1	31.6	0	10	1
13	73	28	21.1	0	10	1
13	74	6	17.4	0	10	1
13	75	1	21.3	0	10	1
13	76	6	44	0	10	1
13	77	85	36.1	0	10	1
13	78	170	38.3	0	10	0
13	79	85	30.9	0	10	1
13	80	170	24.4	0	10	0
13	81	15	14.2	0	10	0
13	82	85	19.5	0	10	0
13	83	85	19.5	0	10	1
13	84	1	11	0	10	0
13	85	170	49.8	0	10	1
13	86	1	41.9	0	10	1
13	87	6	11	0	10	0
13	88	28	13	0	10	1
13	89	15	14.2	0	10	1
13	90	15	20.7	0	10	1
13	91	170	35.6	0	10	0
13	92	28	21.7	0	10	1
13	93	15	49	0	10	1
13	94	1	10.7	0	10	1
13	95	15	12.5	0	10	1
13	96	28	15.5	0	10	1
13	97	170	43.3	0	10	1
13	98	1	21.3	0	10	1
13	99	6	10.5	0	10	0
13	100	15	30.1	0	10	1
13	101	6	17.4	0	10	1
13	102	6	10.5	0	10	1
13	103	1	10.7	0	10	1
13	104	1	31.6	0	10	1
13	105	6	16.7	0	10	1
13	106	6	26.3	0	10	1
13	107	170	38.3	0	10	0
13	108	85	18.4	0	10	0
14	1	28	21.7	0	10	1
14	2	15	14.2	0	10	0
14	3	6	11	0	10	0
14	4	15	14.2	0	10	0
14	5	15	10.7	0	10	1
14	6	85	30.9	0	10	1
14	7	6	16.7	0	10	0
14	8	1	11.8	0	10	1
14	9	28	13.4	0	10	1
14	10	1	18	0	10	1
14	11	15	39.6	0	10	1
14	12	15	30.1	0	10	1
14	13	1	11	0	10	0
14	14	170	41.3	0	10	0
14	15	6	16.7	0	10	1
14	16	170	43.3	0	10	0
14	17	6	35.1	0	10	1
14	18	15	20.7	0	10	1
14	19	85	26	0	10	1
14	20	28	16.9	0	10	1
14	21	85	19.5	0	10	0
14	22	28	21.1	0	10	1
14	23	1	31.6	0	10	1
14	24	6	26.3	0	10	1
14	25	28	21.7	0	10	1
14	26	6	10.5	0	10	0
14	27	85	24.4	0	10	0
14	28	85	10.5	0	10	0
14	29	15	49	0	10	1
14	30	85	45	0	10	1
14	31	170	29.7	0	10	1
14	32	85	27.3	0	10	1
14	33	170	35.6	0	10	0
14	34	1	11.8	0	10	1
14	35	1	18	0	10	1
14	36	85	27.3	0	10	1
14	37	6	14	0	10	0
14	38	28	15.5	0	10	0
14	39	28	12	0	10	0
14	40	170	38.3	0	10	1
14	41	6	13.2	0	10	1
14	42	85	45	0	10	1
14	43	6	17.4	0	10	1
14	44	85	10.5	0	10	0
14	45	15	10.5	0	10	0
14	46	15	30.1	0	10	1
14	47	170	24.4	0	10	0
14	48	1	12.5	0	10	1
14	49	15	10.5	0	10	0
14	50	170	38.3	0	10	1
14	51	85	18.4	0	10	1
14	52	1	11	0	10	1
14	53	170	24.4	0	10	1
14	54	1	11.2	0	10	1
14	55	6	10.5	0	10	0
14	56	1	10.7	0	10	1
14	57	6	35.1	0	10	1
14	58	28	13	0	10	1
14	59	170	29.7	0	10	0
14	60	28	12	0	10	0
14	61	85	36.1	0	10	1
14	62	15	10.7	0	10	1
14	63	28	21.1	0	10	1
14	64	15	18.6	0	10	1
14	65	170	43.3	0	10	1
14	66	15	18.6	0	10	0
14	67	85	26	0	10	1
14	68	28	13.4	0	10	1
14	69	1	21.3	0	10	1
14	70	6	11	0	10	1
14	71	170	35.6	0	10	1
14	72	170	49.8	0	10	1
14	73	1	41.9	0	10	1
14	74	15	12.5	0	10	0
14	75	1	10.7	0	10	1
14	76	170	49.8	0	10	1
14	77	1	31.6	0	10	1
14	78	85	36.1	0	10	1
14	79	28	15.5	0	10	1
14	80	6	44	0	10	1
14	81	28	13	0	10	0
14	82	6	14	0	10	1
14	83	85	18.4	0	10	0
14	84	15	12.5	0	10	0
14	85	1	11.2	0	10	0
14	86	15	49	0	10	1
14	87	170	33.9	0	10	1
14	88	85	19.5	0	10	0
14	89	6	17.4	0	10	1
14	90	28	41.1	0	10	1
14	91	6	44	0	10	1
14	92	170	15	0	10	0
14	93	28	31.4	0	10	1
14	94	1	12.5	0	10	1
14	95	28	16.9	0	10	1
14	96	85	24.4	0	10	1
14	97	15	39.6	0	10	1
14	98	170	41.3	0	10	1
14	99	1	21.3	0	10	1
14	100	170	15	0	10	0
14	101	170	33.9	0	10	1
14	102	85	30.9	0	10	1
14	103	28	41.1	0	10	1
14	104	6	26.3	0	10	1
14	105	28	31.4	0	10	1
14	106	6	13.2	0	10	1
14	107	15	20.7	0	10	1
14	108	1	41.9	0	10	1
15	1	15	10.7	0	10	0
15	2	28	13.4	0	10	0
15	3	170	33.9	0	10	0
15	4	15	49	0	10	1
15	5	28	21.7	0	10	0
15	6	170	15	0	10	0
15	7	28	41.1	0	10	1
15	8	85	45	0	10	1
15	9	28	13	0	10	1
15	10	170	33.9	0	10	1
15	11	6	11	0	10	0
15	12	85	27.3	0	10	1
15	13	1	11.8	0	10	1
15	14	1	10.7	0	10	1
15	15	28	12	0	10	0
15	16	6	14	0	10	0
15	17	1	11.2	0	10	0
15	18	15	39.6	0	10	1
15	19	15	30.1	0	10	0
15	20	15	20.7	0	10	1
15	21	28	13	0	10	0
15	22	6	44	0	10	1
15	23	170	38.3	0	10	0
15	24	15	18.6	0	10	1
15	25	15	14.2	0	10	1
15	26	15	18.6	0	10	1
15	27	170	41.3	0	10	0
15	28	28	21.1	0	10	1
15	29	6	14	0	10	1
15	30	28	15.5	0	10	0
15	31	170	24.4	0	10	0
15	32	1	31.6	0	10	1
15	33	6	35.1	0	10	1
15	34	15	30.1	0	10	1
15	35	170	49.8	0	10	1
15	36	85	18.4	0	10	0
15	37	15	10.5	0	10	1
15	38	170	38.3	0	10	0
15	39	6	26.3	0	10	1
15	40	170	41.3	0	10	1
15	41	85	10.5	0	10	NA
15	42	1	18	0	10	1
15	43	6	10.5	0	10	1
15	44	85	19.5	0	10	0
15	45	1	21.3	0	10	1
15	46	28	13.4	0	10	1
15	47	15	39.6	0	10	1
15	48	170	15	0	10	0
15	49	85	24.4	0	10	0
15	50	15	12.5	0	10	0
15	51	85	30.9	0	10	0
15	52	28	12	0	10	0
15	53	85	18.4	0	10	0
15	54	28	31.4	0	10	1
15	55	170	35.6	0	10	0
15	56	1	41.9	0	10	1
15	57	15	10.7	0	10	0
15	58	6	44	0	10	1
15	59	85	26	0	10	0
15	60	6	26.3	0	10	1
15	61	170	29.7	0	10	0
15	62	6	17.4	0	10	1
15	63	85	36.1	0	10	NA
15	64	1	11	0	10	1
15	65	1	11.2	0	10	1
15	66	15	20.7	0	10	1
15	67	6	10.5	0	10	0
15	68	28	16.9	0	10	0
15	69	170	43.3	0	10	0
15	70	1	21.3	0	10	0
15	71	1	31.6	0	10	1
15	72	170	24.4	0	10	0
15	73	170	35.6	0	10	0
15	74	1	10.7	0	10	1
15	75	170	29.7	0	10	0
15	76	85	36.1	0	10	0
15	77	6	11	0	10	1
15	78	1	12.5	0	10	1
15	79	15	49	0	10	1
15	80	85	45	0	10	1
15	81	28	41.1	0	10	1
15	82	85	10.5	0	10	0
15	83	1	12.5	0	10	1
15	84	85	30.9	0	10	1
15	85	28	16.9	0	10	0
15	86	85	24.4	0	10	NA
15	87	1	41.9	0	10	1
15	88	6	16.7	0	10	1
15	89	170	43.3	0	10	1
15	90	1	11	0	10	1
15	91	170	49.8	0	10	1
15	92	15	12.5	0	10	0
15	93	1	11.8	0	10	0
15	94	6	17.4	0	10	0
15	95	28	21.1	0	10	1
15	96	28	21.7	0	10	1
15	97	85	27.3	0	10	0
15	98	28	31.4	0	10	1
15	99	6	13.2	0	10	0
15	100	28	15.5	0	10	1
15	101	1	18	0	10	0
15	102	85	19.5	0	10	0
15	103	6	16.7	0	10	1
15	104	15	14.2	0	10	0
15	105	6	13.2	0	10	1
15	106	6	35.1	0	10	1
15	107	15	10.5	0	10	1
15	108	85	26	0	10	0
16	1	85	10.5	0	10	0
16	2	85	36.1	0	10	0
16	3	28	41.1	0	10	1
16	4	15	12.5	0	10	0
16	5	6	17.4	0	10	1
16	6	6	44	0	10	1
16	7	6	14	0	10	0
16	8	28	12	0	10	0
16	9	28	41.1	0	10	1
16	10	15	18.6	0	10	0
16	11	85	27.3	0	10	0
16	12	1	31.6	0	10	1
16	13	85	45	0	10	1
16	14	170	38.3	0	10	0
16	15	28	16.9	0	10	0
16	16	170	29.7	0	10	0
16	17	170	15	0	10	0
16	18	6	14	0	10	1
16	19	85	18.4	0	10	0
16	20	170	43.3	0	10	0
16	21	170	33.9	0	10	0
16	22	85	26	0	10	0
16	23	15	10.7	0	10	0
16	24	15	10.5	0	10	1
16	25	6	13.2	0	10	0
16	26	1	10.7	0	10	1
16	27	28	15.5	0	10	0
16	28	28	13.4	0	10	0
16	29	170	35.6	0	10	0
16	30	170	41.3	0	10	1
16	31	1	31.6	0	10	1
16	32	28	15.5	0	10	0
16	33	85	10.5	0	10	0
16	34	28	21.7	0	10	0
16	35	1	21.3	0	10	1
16	36	170	43.3	0	10	0
16	37	15	49	0	10	1
16	38	85	30.9	0	10	0
16	39	1	11	0	10	0
16	40	170	41.3	0	10	1
16	41	6	13.2	0	10	0
16	42	85	24.4	0	10	0
16	43	170	15	0	10	1
16	44	1	11.8	0	10	0
16	45	85	26	0	10	0
16	46	15	39.6	0	10	1
16	47	15	39.6	0	10	1
16	48	6	26.3	0	10	1
16	49	1	10.7	0	10	0
16	50	85	24.4	0	10	1
16	51	15	20.7	0	10	0
16	52	1	11	0	10	1
16	53	1	12.5	0	10	1
16	54	1	11.2	0	10	1
16	55	28	21.1	0	10	0
16	56	170	49.8	0	10	1
16	57	1	21.3	0	10	1
16	58	28	13.4	0	10	0
16	59	15	10.5	0	10	0
16	60	6	17.4	0	10	1
16	61	28	31.4	0	10	1
16	62	85	19.5	0	10	0
16	63	85	36.1	0	10	1
16	64	15	14.2	0	10	0
16	65	6	35.1	0	10	1
16	66	6	10.5	0	10	1
16	67	15	18.6	0	10	0
16	68	1	41.9	0	10	1
16	69	1	18	0	10	1
16	70	28	21.1	0	10	1
16	71	170	24.4	0	10	1
16	72	15	10.7	0	10	1
16	73	6	16.7	0	10	0
16	74	170	49.8	0	10	0
16	75	15	30.1	0	10	1
16	76	15	14.2	0	10	0
16	77	15	20.7	0	10	1
16	78	28	21.7	0	10	1
16	79	85	27.3	0	10	0
16	80	170	35.6	0	10	0
16	81	28	16.9	0	10	0
16	82	85	18.4	0	10	0
16	83	28	13	0	10	0
16	84	6	11	0	10	0
16	85	6	35.1	0	10	1
16	86	1	41.9	0	10	1
16	87	1	12.5	0	10	1
16	88	6	11	0	10	1
16	89	6	26.3	0	10	1
16	90	170	24.4	0	10	0
16	91	15	30.1	0	10	1
16	92	6	44	0	10	1
16	93	15	12.5	0	10	0
16	94	85	45	0	10	1
16	95	15	49	0	10	1
16	96	170	29.7	0	10	0
16	97	1	11.2	0	10	1
16	98	6	10.5	0	10	1
16	99	170	33.9	0	10	0
16	100	28	13	0	10	0
16	101	85	19.5	0	10	0
16	102	170	38.3	0	10	0
16	103	28	31.4	0	10	1
16	104	1	18	0	10	1
16	105	28	12	0	10	0
16	106	6	16.7	0	10	1
16	107	1	11.8	0	10	0
16	108	85	30.9	0	10	0
17	1	28	21.7	0	10	0
17	2	170	43.3	0	10	0
17	3	28	21.7	0	10	0
17	4	170	15	0	10	0
17	5	170	43.3	0	10	0
17	6	15	18.6	0	10	1
17	7	85	27.3	0	10	0
17	8	6	11	0	10	0
17	9	28	16.9	0	10	0
17	10	15	30.1	0	10	1
17	11	15	20.7	0	10	1
17	12	6	26.3	0	10	1
17	13	28	12	0	10	0
17	14	6	10.5	0	10	1
17	15	1	21.3	0	10	1
17	16	85	36.1	0	10	1
17	17	15	18.6	0	10	1
17	18	28	12	0	10	0
17	19	170	15	0	10	0
17	20	28	41.1	0	10	1
17	21	28	31.4	0	10	1
17	22	85	45	0	10	1
17	23	15	12.5	0	10	0
17	24	6	16.7	0	10	1
17	25	15	20.7	0	10	0
17	26	1	11.2	0	10	1
17	27	15	39.6	0	10	1
17	28	6	35.1	0	10	1
17	29	1	10.7	0	10	1
17	30	15	30.1	0	10	1
17	31	28	13.4	0	10	0
17	32	6	16.7	0	10	1
17	33	170	41.3	0	10	1
17	34	6	10.5	0	10	0
17	35	85	19.5	0	10	0
17	36	6	13.2	0	10	0
17	37	6	26.3	0	10	1
17	38	170	49.8	0	10	0
17	39	1	31.6	0	10	1
17	40	15	10.7	0	10	NA
17	41	170	24.4	0	10	0
17	42	6	11	0	10	0
17	43	15	10.5	0	10	1
17	44	170	29.7	0	10	0
17	45	28	15.5	0	10	0
17	46	85	18.4	0	10	0
17	47	85	18.4	0	10	0
17	48	6	14	0	10	1
17	49	170	38.3	0	10	0
17	50	15	39.6	0	10	1
17	51	1	18	0	10	1
17	52	1	18	0	10	1
17	53	1	11.8	0	10	1
17	54	85	45	0	10	1
17	55	170	33.9	0	10	0
17	56	170	35.6	0	10	0
17	57	1	12.5	0	10	0
17	58	6	44	0	10	1
17	59	1	11	0	10	0
17	60	28	15.5	0	10	0
17	61	15	49	0	10	1
17	62	170	33.9	0	10	0
17	63	85	26	0	10	0
17	64	1	10.7	0	10	1
17	65	28	16.9	0	10	0
17	66	6	14	0	10	1
17	67	15	10.5	0	10	1
17	68	15	49	0	10	1
17	69	85	36.1	0	10	0
17	70	1	31.6	0	10	1
17	71	1	11	0	10	1
17	72	28	21.1	0	10	0
17	73	85	30.9	0	10	0
17	74	6	44	0	10	1
17	75	15	12.5	0	10	1
17	76	170	49.8	0	10	0
17	77	28	13	0	10	1
17	78	85	10.5	0	10	0
17	79	28	13.4	0	10	0
17	80	1	12.5	0	10	1
17	81	28	41.1	0	10	1
17	82	170	38.3	0	10	0
17	83	170	35.6	0	10	0
17	84	28	21.1	0	10	1
17	85	15	10.7	0	10	1
17	86	1	41.9	0	10	1
17	87	28	31.4	0	10	NA
17	88	85	10.5	0	10	0
17	89	1	11.8	0	10	1
17	90	15	14.2	0	10	1
17	91	85	24.4	0	10	0
17	92	6	13.2	0	10	1
17	93	85	19.5	0	10	0
17	94	6	17.4	0	10	1
17	95	85	30.9	0	10	1
17	96	170	24.4	0	10	0
17	97	28	13	0	10	0
17	98	6	17.4	0	10	1
17	99	170	41.3	0	10	0
17	100	85	26	0	10	1
17	101	85	24.4	0	10	0
17	102	1	11.2	0	10	1
17	103	85	27.3	0	10	1
17	104	6	35.1	0	10	1
17	105	170	29.7	0	10	0
17	106	1	41.9	0	10	1
17	107	1	21.3	0	10	1
17	108	15	14.2	0	10	1
18	1	170	43.3	0	10	1
18	2	85	30.9	0	10	1
18	3	6	14	0	10	1
18	4	28	31.4	0	10	1
18	5	170	38.3	0	10	1
18	6	15	14.2	0	10	1
18	7	6	44	0	10	1
18	8	6	11	0	10	1
18	9	85	19.5	0	10	1
18	10	15	20.7	0	10	1
18	11	6	13.2	0	10	1
18	12	170	15	0	10	0
18	13	85	26	0	10	1
18	14	1	18	0	10	1
18	15	15	14.2	0	10	1
18	16	85	36.1	0	10	1
18	17	1	18	0	10	0
18	18	15	49	0	10	1
18	19	170	49.8	0	10	1
18	20	6	35.1	0	10	1
18	21	85	10.5	0	10	0
18	22	28	13.4	0	10	0
18	23	15	20.7	0	10	1
18	24	85	45	0	10	1
18	25	15	39.6	0	10	1
18	26	15	12.5	0	10	1
18	27	1	11.8	0	10	1
18	28	1	21.3	0	10	1
18	29	6	26.3	0	10	1
18	30	15	12.5	0	10	1
18	31	6	17.4	0	10	1
18	32	28	16.9	0	10	1
18	33	170	41.3	0	10	0
18	34	170	24.4	0	10	0
18	35	15	10.7	0	10	0
18	36	1	10.7	0	10	0
18	37	6	35.1	0	10	1
18	38	170	38.3	0	10	1
18	39	6	44	0	10	1
18	40	15	30.1	0	10	1
18	41	28	13	0	10	0
18	42	15	49	0	10	1
18	43	6	11	0	10	0
18	44	15	39.6	0	10	1
18	45	15	10.7	0	10	0
18	46	1	11	0	10	1
18	47	28	21.1	0	10	1
18	48	28	13	0	10	0
18	49	1	11.2	0	10	1
18	50	28	12	0	10	1
18	51	6	16.7	0	10	1
18	52	85	27.3	0	10	1
18	53	170	49.8	0	10	1
18	54	28	21.7	0	10	1
18	55	15	10.5	0	10	0
18	56	170	29.7	0	10	0
18	57	85	10.5	0	10	0
18	58	1	11	0	10	1
18	59	6	14	0	10	1
18	60	170	33.9	0	10	0
18	61	170	35.6	0	10	1
18	62	15	18.6	0	10	1
18	63	6	26.3	0	10	1
18	64	85	18.4	0	10	0
18	65	1	41.9	0	10	1
18	66	28	12	0	10	1
18	67	6	16.7	0	10	1
18	68	170	24.4	0	10	1
18	69	15	18.6	0	10	1
18	70	6	17.4	0	10	1
18	71	85	18.4	0	10	0
18	72	1	21.3	0	10	1
18	73	28	41.1	0	10	1
18	74	85	27.3	0	10	0
18	75	85	36.1	0	10	1
18	76	170	35.6	0	10	0
18	77	28	21.1	0	10	1
18	78	170	43.3	0	10	1
18	79	28	21.7	0	10	1
18	80	85	24.4	0	10	1
18	81	28	31.4	0	10	1
18	82	85	45	0	10	1
18	83	15	10.5	0	10	0
18	84	6	13.2	0	10	1
18	85	1	31.6	0	10	1
18	86	1	31.6	0	10	1
18	87	85	30.9	0	10	1
18	88	85	19.5	0	10	1
18	89	85	24.4	0	10	1
18	90	28	13.4	0	10	0
18	91	170	29.7	0	10	1
18	92	170	33.9	0	10	1
18	93	28	41.1	0	10	1
18	94	170	15	0	10	0
18	95	85	26	0	10	0
18	96	170	41.3	0	10	1
18	97	1	12.5	0	10	1
18	98	1	12.5	0	10	1
18	99	28	15.5	0	10	1
18	100	1	11.2	0	10	0
18	101	6	10.5	0	10	0
18	102	1	10.7	0	10	1
18	103	1	11.8	0	10	1
18	104	28	16.9	0	10	1
18	105	6	10.5	0	10	1
18	106	1	41.9	0	10	1
18	107	28	15.5	0	10	0
18	108	15	30.1	0	10	1
19	1	28	41.1	0	10	1
19	2	170	41.3	0	10	0
19	3	6	10.5	0	10	0
19	4	6	44	0	10	1
19	5	15	12.5	0	10	1
19	6	28	16.9	0	10	1
19	7	6	14	0	10	0
19	8	6	16.7	0	10	1
19	9	1	31.6	0	10	1
19	10	1	10.7	0	10	1
19	11	85	19.5	0	10	1
19	12	28	16.9	0	10	0
19	13	170	24.4	0	10	0
19	14	15	14.2	0	10	1
19	15	85	26	0	10	0
19	16	85	36.1	0	10	1
19	17	15	30.1	0	10	1
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19	21	28	21.7	0	10	0
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19	23	85	10.5	0	10	0
19	24	1	11.8	0	10	0
19	25	1	12.5	0	10	1
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19	27	6	26.3	0	10	1
19	28	6	35.1	0	10	1
19	29	15	18.6	0	10	0
19	30	170	29.7	0	10	0
19	31	85	19.5	0	10	0
19	32	170	43.3	0	10	1
19	33	28	31.4	0	10	1
19	34	28	13.4	0	10	1
19	35	85	26	0	10	0
19	36	85	10.5	0	10	0
19	37	85	45	0	10	1
19	38	28	13	0	10	0
19	39	170	38.3	0	10	0
19	40	1	11	0	10	0
19	41	1	10.7	0	10	0
19	42	170	29.7	0	10	1
19	43	6	26.3	0	10	1
19	44	1	11.2	0	10	0
19	45	28	41.1	0	10	1
19	46	1	12.5	0	10	0
19	47	85	30.9	0	10	1
19	48	170	33.9	0	10	0
19	49	28	13	0	10	0
19	50	170	33.9	0	10	1
19	51	170	49.8	0	10	1
19	52	170	35.6	0	10	0
19	53	15	49	0	10	1
19	54	1	11.2	0	10	1
19	55	6	11	0	10	0
19	56	6	17.4	0	10	1
19	57	15	49	0	10	1
19	58	1	11	0	10	0
19	59	85	18.4	0	10	0
19	60	15	20.7	0	10	1
19	61	170	38.3	0	10	0
19	62	15	39.6	0	10	1
19	63	6	35.1	0	10	1
19	64	28	21.1	0	10	1
19	65	15	39.6	0	10	1
19	66	15	10.7	0	10	0
19	67	1	31.6	0	10	1
19	68	1	41.9	0	10	1
19	69	170	49.8	0	10	0
19	70	170	35.6	0	10	0
19	71	85	36.1	0	10	0
19	72	28	13.4	0	10	1
19	73	1	18	0	10	1
19	74	85	18.4	0	10	1
19	75	85	24.4	0	10	0
19	76	170	43.3	0	10	1
19	77	15	18.6	0	10	1
19	78	6	13.2	0	10	0
19	79	6	44	0	10	1
19	80	15	10.5	0	10	0
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20	24	28	12	0	10	1
20	25	1	18	0	10	1
20	26	170	29.7	0	10	0
20	27	28	21.7	0	10	1
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20	29	170	41.3	0	10	1
20	30	85	19.5	0	10	0
20	31	85	45	0	10	1
20	32	170	33.9	0	10	1
20	33	28	13.4	0	10	0
20	34	85	27.3	0	10	1
20	35	28	13	0	10	0
20	36	15	18.6	0	10	0
20	37	15	12.5	0	10	1
20	38	170	24.4	0	10	0
20	39	6	44	0	10	1
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20	41	6	35.1	0	10	1
20	42	6	26.3	0	10	1
20	43	6	13.2	0	10	1
20	44	15	10.7	0	10	1
20	45	28	21.7	0	10	1
20	46	170	33.9	0	10	1
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20	48	1	10.7	0	10	1
20	49	28	16.9	0	10	1
20	50	1	11.2	0	10	0
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20	54	15	14.2	0	10	1
20	55	85	18.4	0	10	0
20	56	170	29.7	0	10	0
20	57	85	45	0	10	1
20	58	28	31.4	0	10	1
20	59	15	30.1	0	10	1
20	60	1	11.8	0	10	1
20	61	28	31.4	0	10	1
20	62	85	19.5	0	10	0
20	63	6	14	0	10	1
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20	65	1	10.7	0	10	1
20	66	15	49	0	10	1
20	67	1	21.3	0	10	1
20	68	6	35.1	0	10	1
20	69	15	10.5	0	10	NA
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20	71	6	13.2	0	10	1
20	72	170	49.8	0	10	1
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20	79	85	26	0	10	1
20	80	6	17.4	0	10	1
20	81	170	15	0	10	0
20	82	15	12.5	0	10	NA
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diff --git a/Programing_project/docs/marzilliericson2015.pdf b/Programing_project/docs/marzilliericson2015.pdf
deleted file mode 100644
index 17fded0..0000000
Binary files a/Programing_project/docs/marzilliericson2015.pdf and /dev/null differ
diff --git a/Programing_project/scripts/hyperbolic.stan b/Programing_project/scripts/hyperbolic.stan
deleted file mode 100644
index 6d783e1..0000000
--- a/Programing_project/scripts/hyperbolic.stan
+++ /dev/null
@@ -1,75 +0,0 @@
-data {
-    int<lower=1> nSubjects;
-    int<lower=1> nTrials;
-    int<lower=1, upper=nTrials> Tsubj[nSubjects];
-    real<lower=0> delay_later[nSubjects,nTrials];
-    real<lower=0> amount_later[nSubjects,nTrials];
-    real<lower=0> delay_sooner[nSubjects,nTrials];
-    real<lower=0> amount_sooner[nSubjects,nTrials];
-    int<lower=0,upper=1> Choice[nSubjects, nTrials]; # 0 for instant reward, 1 for delayed reward
-}
-
-parameters {
-  # Hyper(group)-parameters  
-  real mu_k_raw;
-  real mu_beta_raw;
-  real sd_k_raw;
-  real sd_beta_raw;
-    
-  # Subject-level raw parameters
-  vector[nSubjects] k_raw;
-  vector[nSubjects] beta_raw;    
-}
-
-transformed parameters {
-  # Transform subject-level raw parameters 
-  vector<lower=0,upper=1>[nSubjects] k;
-  vector<lower=0,upper=5>[nSubjects] beta;
-        
-  k[s]    = Phi_approx( mu_k_raw + sd_k_raw * k_raw[s] )
-  beta[s] = Phi_approx( mu_beta_raw + sd_beta_raw * beta_raw[s] ) * 5;
-}
-
-model {
-  # Hyperparameters
-  sd_k_raw    ~ cauchy(0, 3);
-  sd_beta_raw ~ cauchy(0, 3);
-
-  # individual parameters
-  k_raw    ~ normal(0, 1);
-  beta_raw ~ normal(0, 1);
-    
-  for (s in 1:nSubjects) {
-    # Define values
-    real ev_later;
-    real ev_sooner
-      
-    for (t in 1:(Tsubj[s])) {
-      ev_later   = amount_later[s,t]  / ( 1 + k[s] * delay_later[s,t] );
-      ev_sooner  = amount_sooner[s,t] / ( 1 + k[s] * delay_sooner[s,t] );
-      choice[s,t] ~ bernoulli_logit( beta[ns] * (ev_later - ev_sooner) );
-    }
-  }
-}
-
-generated quantities {
-  real<lower=0,upper=1> mu_k;
-  real<lower=0,upper=5> mu_beta;
-  real log_lik[nSubjects];
-
-  mu_k    = Phi_approx(mu_k_raw);
-  mu_beta = Phi_approx(mu_beta_raw) * 5;
-  
-  { # local section, this saves time and space
-    for (s in 1:nSubjects) {
-      real ev_later;
-      real ev_sooner;
-
-      for (t in 1:(Tsubj[s])) {
-        ev_later   = amount_later[s,t]  / ( 1 + k[s] * delay_later[s,t] );
-        ev_sooner  = amount_sooner[s,t] / ( 1 + k[s] * delay_sooner[s,t] );
-        log_lik[s] += bernoulli_logit_lpmf( choice[s,t] | beta[s] * (ev_later - ev_sooner) );
-      }
-    }
-  }
-}
diff --git a/Programing_project/scripts/delay_discounting_main.R b/Programing_project/scripts/learning_task_main.R
similarity index 69%
rename from Programing_project/scripts/delay_discounting_main.R
rename to Programing_project/scripts/learning_task_main.R
index 0a4f9a4..2e53343 100644
--- a/Programing_project/scripts/delay_discounting_main.R
+++ b/Programing_project/scripts/learning_task_main.R
@@ -1,106 +1,98 @@
-# =============================================================================
-#### Info #### 
-# =============================================================================
-# Hierachical models for delay discounting models
-#
-
-# =============================================================================
-#### Construct Data #### 
-# =============================================================================
-# clear workspace
-rm(list=ls(all=TRUE))
-library(rstan)
-library(loo)
-library(ggplot2)
-
-#### read raw -----------------------------------------------------------------
-rawdata   = # complete this line for reading raw data
-
-#### Preprocess the data ------------------------------------------------------
-subjList  = unique(rawdata[,"subjID"])
-nSubjects = length(subjList) 
-
-Tsubj = as.vector( rep( 0, nSubjects ) ) # number of valid trials per subj
-
-for ( s in 1:nSubjects )  {
-    curSubj  = subjList[ s ]
-    Tsubj[s] = sum( rawdata$subjID == curSubj )
-}
-
-maxTrials = max(Tsubj)
-delay_later   = array(0, c(nSubjects, maxTrials) )
-amount_later  = array(0, c(nSubjects, maxTrials) )
-delay_sooner  = array(0, c(nSubjects, maxTrials) )
-amount_sooner = array(0, c(nSubjects, maxTrials) )
-choice = array(0, c(nSubjects, maxTrials) )
-
-for (s in 1:nSubjects) {
-    curSubj      = subjList[s]
-    useTrials    = Tsubj[s]
-    tmp          = subset(rawdata, rawdata$subjID == curSubj)
-    delay_later[s, 1:useTrials]  = tmp$delay_later
-    amount_later[s, 1:useTrials] = tmp$amount_later
-    delay_sooner[s, 1:useTrials]  = tmp$delay_sooner
-    amount_sooner[s, 1:useTrials] = tmp$amount_sooner
-    choice[s, 1:useTrials] = tmp$choice
-}
-
-dataList = list(
-    nSubjects = nSubjects,
-    nTrials   = maxTrials,
-    Tsubj     = Tsubj,
-    choice    = choice,
-    amount_later   = amount_later,
-    delay_later    = delay_later,
-    amount_sooner  = amount_sooner,
-    delay_sooner   = delay_sooner
-)
-
-# =============================================================================
-#### Running Stan #### 
-# =============================================================================
-rstan_options(auto_write = TRUE)
-options(mc.cores = 2)
-
-nIter     = 2000
-nChains   = 4 
-nWarmup   = floor(nIter/2)
-nThin     = 1
-
-#### run the hyperbolic model ----------------------------------------
-modelFile1 = 'scripts/hyperbolic.stan'
-
-cat("Estimating", modelFile1, "model... \n")
-startTime = Sys.time(); print(startTime)
-cat("Calling", nChains, "simulations in Stan... \n")
-
-fit_hyperbolic = stan() # complete this line for calling Stan
-
-cat("Finishing", modelFile1, "model simulation ... \n")
-endTime = Sys.time(); print(endTime)  
-cat("It took",as.character.Date(endTime - startTime), "\n")
-
-#### run the simple heuristic model ---------------------------------------
-modelFile2 = 'scripts/heuristic.stan'
-
-cat("Estimating", modelFile1, "model... \n")
-startTime = Sys.time(); print(startTime)
-cat("Calling", nChains, "simulations in Stan... \n")
-
-fit_heuristic = stan() # complete this line for calling Stan
-
-cat("Finishing", modelFile2, "model simulation ... \n")
-endTime = Sys.time(); print(endTime)  
-cat("It took",as.character.Date(endTime - startTime), "\n")
-
-# =============================================================================
-#### Model selection #### 
-# =============================================================================
-LL_hyperbolic = # complete this line for extreact log-likelihood
-LL_heuristic  = # complete this line for extreact log-likelihood
-
-waic_hyperbolic = waic(LL1)
-waic_heuristic  = waic(LL2)
-
-
-#### End of file
+# =============================================================================
+#### Info #### 
+# =============================================================================
+# Hierachical models for two-armed bandit learning task
+# (C) Lei Zhang <lei.zhang@univie.ac.at>
+
+# =============================================================================
+#### Construct Data #### 
+# =============================================================================
+# clear workspace
+rm(list=ls(all=TRUE))
+library(rstan)
+library(loo)
+library(ggplot2)
+
+#### read raw -----------------------------------------------------------------
+rawdata   = # complete this line for reading raw data
+    
+# write a line here to remove missing trials
+
+#### Preprocess the data ------------------------------------------------------
+subjList  = unique(rawdata[,"subjID"])
+nSubjects = length(subjList) 
+
+Tsubj = as.vector( rep( 0, nSubjects ) ) # number of valid trials per subj
+
+for ( s in 1:nSubjects )  {
+    curSubj  = subjList[ s ]
+    Tsubj[s] = sum( rawdata$subjID == curSubj )
+}
+
+maxTrials = max(Tsubj)
+choice = array(0, c(nSubjects, maxTrials) )
+reward = array(0, c(nSubjects, maxTrials) )
+
+for (s in 1:nSubjects) {
+    curSubj      = subjList[s]
+    useTrials    = Tsubj[s]
+    tmp          = subset(rawdata, rawdata$subjID == curSubj)
+    choice[s, 1:useTrials] = tmp$choice
+    reward[s, 1:useTrials] = tmp$reward
+}
+
+dataList = list(
+    nSubjects = nSubjects,
+    nTrials   = maxTrials,
+    Tsubj     = Tsubj,
+    choice    = choice,
+    reward    = reward
+)
+
+# =============================================================================
+#### Running Stan #### 
+# =============================================================================
+rstan_options(auto_write = TRUE)
+options(mc.cores = 2) # <-- adjust if you want to run 4 cores in parallel
+
+nIter     = 2000
+nChains   = 4 
+nWarmup   = floor(nIter/2)
+nThin     = 1
+
+#### run the Rescorla-Wagner model ----------------------------------------
+modelFile1 = 'scripts/rw.stan'
+
+cat("Estimating", modelFile1, "model... \n")
+startTime = Sys.time(); print(startTime)
+cat("Calling", nChains, "simulations in Stan... \n")
+
+fit_hyperbolic = stan() # complete this line for calling Stan
+
+cat("Finishing", modelFile1, "model simulation ... \n")
+endTime = Sys.time(); print(endTime)  
+cat("It took",as.character.Date(endTime - startTime), "\n")
+
+#### run the reward-punishment model ---------------------------------------
+modelFile2 = 'scripts/rp.stan'
+
+cat("Estimating", modelFile1, "model... \n")
+startTime = Sys.time(); print(startTime)
+cat("Calling", nChains, "simulations in Stan... \n")
+
+fit_heuristic = stan() # complete this line for calling Stan
+
+cat("Finishing", modelFile2, "model simulation ... \n")
+endTime = Sys.time(); print(endTime)  
+cat("It took",as.character.Date(endTime - startTime), "\n")
+
+# =============================================================================
+#### Model selection #### 
+# =============================================================================
+LL_rw = # complete this line for extreact log-likelihood
+LL_rp  = # complete this line for extreact log-likelihood
+
+waic_rw = waic(LL_rw)
+waic_rp = waic(LL_rp)
+
+#### End of file
diff --git a/Programing_project/scripts/heuristic.stan b/Programing_project/scripts/rp.stan
similarity index 80%
rename from Programing_project/scripts/heuristic.stan
rename to Programing_project/scripts/rp.stan
index e2b73ce..ba936a9 100644
--- a/Programing_project/scripts/heuristic.stan
+++ b/Programing_project/scripts/rp.stan
@@ -1,14 +1,17 @@
-data {
-}
-
-parameters {
-}
-
-transformed parameters {
-}
-
-model {
-}
-
-generated quantities {
-}
+data {
+}
+
+transformed data {
+}
+
+parameters {
+}
+
+transformed parameters {
+}
+
+model {
+}
+
+generated quantities {
+}
diff --git a/Programing_project/scripts/rw.stan b/Programing_project/scripts/rw.stan
new file mode 100644
index 0000000..34cb4c3
--- /dev/null
+++ b/Programing_project/scripts/rw.stan
@@ -0,0 +1,81 @@
+data {
+  int<lower=1> nSubjects;
+  int<lower=1> nTrials;
+  int<lower=1,upper=2> choice;
+  real<lower=-1, upper=1> reward[nSubjects, nTrials]; 
+}
+
+transformed data {
+  vector[2] initV;  // initial values for V
+  initV = rep_vector(0.0, 2);
+}
+
+parameters {
+  // group-level parameters
+  real lr_mu_rae; 
+  real tau_mu_raw;
+  real lr_sd_raw;
+  real tau_sd_raw;
+  
+  // subject-level raw parameters
+  vector[nSubjects] lr_raw;
+  vector[nSubjects] tau_raw;
+}
+
+transformed parameters {
+  vector<lower=0,upper=1>[nSubjects] lr;
+  vector<lower=0,upper=3>[nSubjects] tau;
+  
+  lr  = Phi_approx( lr_mu_raw  + lr_sd_raw * lr_raw[s] )
+  tau = Phi_approx( tau_mu_raw + tau_sd_raw * tau_raw ) * 5;
+}
+
+
+model {
+  // group-level prior
+  lr_mu_raw  ~ normal(0,1);
+  tau_mu_raw ~ normal(0,1);
+  
+  // individual-level prior
+  lr_raw  ~ normal(0,1);
+  tau_raw ~ normal(0,1);
+  
+  for (s in 1:nSubjects) {
+    vector[2] v; 
+    real pe;    
+    v = initV;
+
+    for (t in 1:nTrials) {        
+      Choice[s,t] ~ categorical( tau[s] * v );
+            
+      pe = Reward - v[choice[s,t]];      
+      v[choice[s,t]] = v[choice[s,t]] + lr[s] * pe; 
+    }
+  }    
+}
+
+generated quantities {
+  real<lower=0,upper=1> lr_mu; 
+  real<lower=0,upper=5> tau_mu;
+  
+  real log_lik[nSubjects];
+  
+  lr_mu  = Phi_approx(lr_mu_raw);
+  tau_mu = Phi_approx(tau_mu_raw) * 5;
+
+  { // local section, this saves time and space
+    for (s in 1:nSubjects) {
+      vector[2] v; 
+      real pe;    
+
+      v = initV;
+
+      for (t in 1:nTrials) {    
+        log_lik[s] = log_lik[s] + categorical_logit_lpdf(choice[s,t] | tau[s] * v);    
+              
+        pe = reward[s,t] - v[choice[s,t]];      
+        v[choice[s,t]] = v[choice[s,t]] + lr[s] * pe; 
+      }
+    }    
+  }
+}
diff --git a/Programing_project/short_summary_template.docx b/Programing_project/short_summary.docx
similarity index 70%
rename from Programing_project/short_summary_template.docx
rename to Programing_project/short_summary.docx
index ee85574..37b6fbb 100644
Binary files a/Programing_project/short_summary_template.docx and b/Programing_project/short_summary.docx differ