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3.66356	7.74414	-4.4	4.2	0	18	19	116
3.04452	8.03398	-5.7	4.8	-0.3	69.1	9	506
3.71357	4.70048	-13.5	4.3	0.2	80	3	95
2.94444	7.5251	1.4	3	0.1	177	22	161
4.06044	7.7626	4.1	5.6	1.1	287	7	80
3.68888	7.88683	5.8	2.3	-0.1	200	9	33
3.3322	7.81521	2.7	1.9	0.4	228	7	129
3.3673	7.77779	7.1	8.9	0.2	220	15	155
2.07944	6.89163	4.1	2	0.1	183	9	132
1.94591	7.6774	1.1	5.2	0.1	43.1	10	480
3.3322	5.71373	-9	3.4	0	80	6	98
3.21888	8.33471	1.6	4.4	-0.3	78.1	16	558
1.09861	4.65396	-0.4	5.7	0.8	267	3	121
5.3845	7.38895	-4.8	6	0	81	20	122
3.93183	7.81763	-0.9	1.4	-0.3	236	15	51
3.3673	4.96981	-0.5	0.6	1.7	341.1	4	552
2.83321	7.80384	-1	1.8	0	16	7	150
3.04452	7.62168	-2.3	5.2	-0.1	66.1	12	428
2.99573	5.21494	5.8	2.6	-0.2	68.8	3	582
4.21951	7.64204	5.1	2.2	1.1	199	18	538
3.04452	7.77149	10.8	2.4	-1.5	329.8	13	579
3.7612	8.31385	12.2	4	-2.8	230.4	17	572
3.58352	7.63964	-2.8	2.1	-1.6	210.2	11	512
2.48491	7.05531	11.2	3.3	2.3	302.8	23	604
2.48491	7.38275	13.6	0.6	-0.8	159.8	11	602
3.3322	7.56786	-12.3	3.7	-0.2	70	12	460
3.13549	7.92226	4.2	1.4	0.2	161	7	127
2.07944	7.38771	-2.8	4.5	0.1	53.3	21	417
3.68888	7.4793	3.4	7.2	0.5	209.1	12	477
2.99573	3.97029	-11.7	2.7	1.7	81	4	87
3.98898	8.05102	-2.5	3.2	0.1	83	9	51
3.04452	6.67834	3.9	4.8	-0.6	66.8	8	573
3.55535	7.34601	3	1.4	1	200	21	177
1.79176	7.54062	0	6.1	-0.2	207.5	19	489
3.04452	4.82028	-2.6	2.1	0.7	72.8	4	530
2.56495	7.80057	1	2.2	0	99	7	65
4.36945	7.41878	-0.2	1.1	3.2	138	22	49
3.46574	7.96032	7.7	4.8	-0.2	66	17	196
4.60517	8.23669	10.7	5.3	-0.9	205	17	192
3.09104	8.30721	9.3	7.3	-0.5	206	17	212
4.15888	8.26333	9.5	5	-0.9	208	17	208
1.79176	7.50329	1.6	4.5	0.1	117	19	62
2.48491	6.49527	-3.4	0.5	1.1	217	6	135
3.21888	7.74414	-1.3	4.2	-0.2	59	15	149
3.73767	6.75577	-3.2	2.6	0.6	70.5	21	398
4.2485	6.51915	-1.8	2.3	1.1	81	23	174
3.8712	4.77068	-0.5	0.5	0.7	358	4	402
4.00733	7.88721	7.6	4	-0.6	180	13	208
2.94444	5.42053	3.4	2.9	0.4	74.7	3	567
3.09104	3.80666	7.4	3.2	-0.1	225	3	205
1.94591	4.8752	-3.5	3.8	-0.1	60.5	3	418
3.52636	8.25088	19.8	4.8	-1.9	260	17	206
3.8712	7.85516	16.3	3.4	-0.1	225.3	20	605
2.99573	8.25427	-7.3	5	0	60	8	506
4.07754	7.36201	-0.6	0.8	1.1	88.3	21	422
3.21888	7.59388	-0.4	4.5	-0.2	64.6	13	430
3.49651	8.35208	17.8	5	-2.5	219.6	16	599
3.2581	4.62497	-1.9	2.2	2.2	268	4	172
2.99573	7.39879	-10	3.8	-0.1	78.4	20	449
2.70805	6.48616	-1.3	2.9	0.7	63.3	24	397
3.09104	6.20456	4.7	1.5	1.1	104.4	1	588
4.06044	7.64348	0.3	1.5	-0.3	172	13	130
3.61092	7.59337	8.8	2.2	0	186	21	577
2.56495	6.43455	-9.3	3.6	0.1	71.2	9	450
1.60944	7.00033	-2.4	5.8	0	61	22	418
5.0876	7.49499	2.9	1.2	2.1	271.1	21	543
2.30259	7.80221	2.5	5	0.1	135	7	128
3.2581	7.55276	13.4	3.9	0.3	216.8	19	606
3.66356	6.82979	-15.5	3.1	0.2	70.3	22	460
3.04452	7.49053	-1.6	2.2	-0.9	211.7	12	501
3.4012	7.59035	0.1	2.6	-0.1	73	11	66
3.04452	5.51745	-2.8	0.5	2.2	5	5	155
3.4012	8.18619	-0.9	1.3	0.6	57	8	67
3.55535	5.95842	-10.3	1.2	2.5	86	24	86
3.97029	7.55276	-4.1	0.7	-0.7	264.8	13	401
4.72739	7.77022	-3.8	4.9	-0.2	68	7	166
4.30407	7.99834	8.2	5.6	-0.6	210	15	212
2.56495	7.15305	-4.8	4.7	-0.1	74.6	23	441
3.78419	7.95997	-4.3	6	0.1	18	13	82
4.95583	6.57508	6.2	1.3	1.1	226	24	200
3.66356	7.88231	3.3	5.8	-0.1	46.9	19	557
5.26786	7.43603	-1	1.8	1.4	89	20	44
3.46574	4.95583	-4	1.7	1	65	2	165
2.07944	5.1299	7.8	1.6	1.7	184.3	4	604
3.3673	7.20638	-5.3	1.8	-0.2	80	13	451
3.29584	7.46107	2.2	3.1	0.1	62.3	13	470
2.19722	7.53209	14.8	4.2	-4.5	227.7	11	588
3.43399	8.13359	-4.8	1.5	0	196	17	100
3.2581	6.91771	12.8	1	0.3	103.5	24	602
2.83321	7.12206	-3.2	4.6	-0.1	62.2	12	427
3.63759	8.2845	0.7	0.8	-0.8	86	8	541
2.70805	7.56528	3.5	6.9	0.2	204.4	15	482
2.99573	7.65112	-6.8	2.2	-0.4	78.8	16	517
4.00733	7.67183	8.3	0.7	-1.3	206.2	14	532
3.43399	6.31173	-19	2.3	0.8	60.9	8	461
0.69315	7.0775	7	4	-0.2	54.3	10	578
3.52636	7.34148	-14.6	1.6	-0.4	228.9	14	457
2.63906	7.96346	3.6	3.2	1.2	268	18	42
1.94591	7.35116	3.5	1.9	0.2	215	20	107
3.71357	7.54115	5.2	0.9	-1	280	11	155
2.83321	6.64249	-8.3	2.6	0.2	60	23	85
3.43399	7.56579	0.6	3.8	-0.4	64.6	12	507
3.29584	8.1806	-0.5	0.7	-0.2	248	15	124
3.17805	7.60738	1	2.4	-0.1	192	13	38
4.81218	7.54327	-8	0.6	0.5	88	13	94
4.63473	8.20985	-3	1.5	2.7	89.6	8	470
3.04452	5.76205	5	3.2	0	58	6	195
3.8712	6.91274	-5.1	0.8	1.4	241	23	137
3.68888	8.19781	0.2	2.4	0.5	80	8	72
4.33073	7.6024	12.9	5.6	-0.8	46	13	188
4.61512	7.54327	2.6	2.6	-1.9	243	11	45
4.43082	7.70661	7.8	8	-1.2	33.7	13	556
3.4012	5.54518	-0.7	2	-0.3	254	1	542
2.48491	7.68432	7.7	1.5	-0.1	89	16	209
3.29584	7.90064	1.5	1.3	0.9	334.7	18	422
2.94444	7.76684	15.8	3.2	-0.2	125.5	17	595
3.3673	7.97797	4.3	5.7	-1	232	9	156
2.89037	7.89767	8.1	6.4	-0.5	199.4	7	577
4.90527	8.24065	11.6	6	-1.3	213	16	193
2.63906	7.07581	2.8	3.2	0.3	223	10	69
3.29584	7.7424	-4.4	4.9	-0.1	23	10	82
3.71357	6.97915	-5.1	3.3	1.4	81	23	98
4.39445	8.27741	2	2.5	-0.3	207.2	16	536
3.82864	8.01268	10	4.8	-0.5	83.2	9	600
2.56495	5.78996	1.1	4	-0.1	79	3	70
3.3322	7.45934	6.1	2.5	0	92	22	210
3.13549	6.22851	0.1	1.9	1.1	208.8	6	538
0.69315	6.45362	-2.6	4.9	0.2	38.3	23	405
4.00733	7.7178	7.9	0.4	0.5	276	19	184
3.2581	7.92696	4.8	9.4	0.6	204	14	474
3.2581	6.56244	2.6	2.8	-0.7	224.1	8	545
4.84419	8.17273	-9.6	1.1	1.3	191	16	94
2.63906	5.1299	-1.5	2.5	-0.1	245.1	4	510
1.38629	4.54329	3.8	5.8	0.7	201.9	3	473
3.2581	5.71043	1.8	2.7	0.9	181.9	1	527
2.30259	8.19257	-0.7	4.1	0.6	252	8	121
3.09104	5.00395	3.6	2.7	1.2	344.7	4	533
2.94444	7.39817	3.9	6.2	0.7	206	20	155
3.4012	6.63988	4.7	3.6	0	59	24	196
2.99573	6.29342	-2.5	4.9	-0.2	84	7	167
3.82864	7.4378	-9.9	4.5	-0.1	55	11	89
2.30259	7.7977	1.7	2	0.3	38	15	63
4.29046	7.33629	-12.8	3.5	0	79	19	97
4.41884	7.51589	4.3	2.1	0.6	32.3	20	555
3.3673	7.70841	2.1	3.1	0	79	10	64
2.30259	4.64439	-2.2	4.9	-0.1	77	2	114
3.98898	7.77402	3.3	1.8	-0.3	220	10	155
4.12713	5.05625	5.6	3.5	-0.1	78	4	184
3.68888	5.86363	-0.6	0.6	1.7	142	1	79
2.30259	5.52943	3.4	4.7	0.8	220.1	5	474
3.29584	7.91754	0.2	2.8	0.4	335	15	53
3.13549	6.74052	-6.4	2.8	-0.1	77	22	84
2.07944	6.53669	8	1.9	-0.2	54.6	8	578
5.25227	7.82844	3.6	0.8	1.5	87.7	19	529
3.78419	7.74846	-13.4	4	0.2	74.1	13	448
3.97029	5.71043	-2.1	1.3	1.8	87.8	1	485
2.99573	5.64897	-8.7	4.3	-0.2	74.5	1	450
2.56495	6.42972	1.6	1.3	1	58	8	76
3.21888	6.51323	-3.9	2.8	0.4	71.3	8	398
1.38629	7.93057	1.4	2.5	0.2	210.5	19	525
3.13549	7.59337	3.3	4.2	1.2	235	20	138
4.06044	7.19818	3	1.8	1.1	191	22	192
4.27667	8.23377	7.2	3.8	-0.2	31	15	173
3.49651	8.13212	-1.7	1.5	-0.1	68	8	515
3.21888	7.87284	0.8	5	0.1	76.2	19	514
4.07754	7.53102	0.1	3.5	1.2	66.3	20	397
4.20469	7.49276	4.5	6.6	-0.1	210	12	156
4.18965	5.65599	0.3	1.5	1.7	303.4	1	529
2.48491	7.07327	-0.9	1.6	-0.5	256.5	9	510
1.09861	6.33683	2.2	2.8	0.1	142	1	63
2.48491	7.21229	-0.6	2.1	0.1	60	21	134
2.48491	7.86978	3.2	2.6	1.3	286	20	42
0.69315	7.84031	4.1	2.7	0.2	175	19	56
4.34381	7.98344	-4.8	0.8	1.1	230.1	17	445
2.63906	7.03086	7.1	1.6	0.5	66.4	22	593
3.29584	7.90175	-4.4	3.8	0.1	15	9	82
4.17439	7.72223	7.1	3.9	-0.9	90	10	197
3.71357	8.19451	0	0.8	-0.3	254.2	16	500
4.02535	7.84698	11.6	4.2	-2.7	247	14	200
4.85203	7.58731	3.1	2.2	-3.4	227	11	148
4.64439	6.47235	1.5	1.2	1.3	101	24	191
3.63759	7.74932	15	7.3	-2.2	229.9	14	584
3.4012	8.12711	3.6	4.9	1.2	238	18	138
1.79176	5.68358	5.7	1.9	0.3	49	6	189
4.11087	7.58274	3.4	6.9	-0.1	194.9	11	548
3.3673	8.01434	10.1	2.4	-0.3	44	17	189
4.44265	7.65539	-2.4	2.6	0.7	78.3	18	398
2.77259	7.83716	0.2	4.5	0	80	7	157
3.71357	6.9921	-15	3.3	0.6	66	23	462
5.17048	8.08209	5.2	7.2	-1.3	202.7	14	537
3.09104	7.85554	4.1	3.4	0.1	79	7	170
2.70805	7.68064	6.3	9.4	0.2	248	16	160
4.11087	7.5438	8.7	4	-1.2	216.3	14	538
4.79579	8.30598	2.4	4.2	-0.7	65.1	8	556
2.3979	5.95324	-5.3	1	2.2	60	9	77
2.99573	6.2634	2.5	4.3	-0.1	79	24	69
3.13549	8.25036	9.3	5.4	-0.8	213	16	212
3.43399	7.61776	-5.6	3.1	-0.5	77.4	11	519
3.4012	7.44425	5.6	7	1	248.1	12	471
3.09104	7.87169	-1.7	0.5	0	218	18	51
4.45435	7.64492	-5.6	4.9	-0.1	56.7	7	409
4.18965	7.46851	-3.5	0.7	0.2	165.1	20	520
3.73767	8.18256	-11.2	3.7	0.2	78	16	96
4.20469	5.10595	-11.9	0.6	0.8	286.5	4	489
2.56495	5.79301	2.1	2.6	3.1	241	1	32
2.56495	7.14125	1	4.5	1.3	232	21	145
4.85981	7.69303	8.8	3.6	-2.5	85.4	12	547
5.25227	7.23056	-4.6	6.5	0.1	80	21	122
2.63906	6.0845	-5.8	2	-0.3	107.5	1	518
3.13549	5.72031	1	1.8	0.6	92	3	76
1.94591	6.81892	0.1	1.7	-0.1	214	11	112
3.4012	5.67332	-1.9	0.9	3.7	109.2	5	397
4.72739	8.26178	1	3.4	1.3	248	8	43
3.3673	5.92959	2.1	3.8	0.5	64.2	1	531
3.2581	7.53262	6.1	2.5	-0.1	182.5	21	572
3.73767	8.11761	0.2	0.8	0.9	11	16	113
1.79176	7.98446	1.9	5.8	0.2	9	9	80
4.06044	7.7463	11.8	6	-0.7	212.7	14	582
3.91202	8.19229	-4.4	1.5	0.5	87.9	8	423
0.69315	6.43294	1.9	2.4	0.1	133	24	62
1.60944	5.2832	8.6	1.8	1.5	242	3	34
4.46591	8.22336	9.1	9.4	-0.5	39.4	16	556
3.17805	7.68983	13	0.3	0.4	114	22	602
2.89037	6.70564	1.8	0.8	0.1	269	6	141
4.17439	7.58426	4	3.1	-1.7	103.7	12	554
3.73767	8.04943	-7.8	0.6	-0.1	177	17	95
4.00733	7.72886	8.7	5.5	-0.4	132.3	14	573
3.04452	7.51806	-4.9	4.1	-0.2	73.8	13	436
2.63906	7.58172	-8.8	8.1	-0.1	53	18	90
4.11087	8.28425	2.9	3.3	0.8	22.1	17	397
4.47734	8.01731	0.4	3.9	0.7	67.4	18	507
3.17805	7.67786	-0.7	4.3	-0.4	59	13	151
3.3322	7.8598	4.2	2.7	1.1	230.7	18	526
0.69315	4.79579	2.7	2.8	0.2	187	4	131
4.58497	7.66528	7.4	0.9	-0.2	55	13	184
0.69315	4.36945	0.6	4.6	0.1	350	3	171
3.4012	7.5251	6	3.5	-0.2	78.1	21	571
2.07944	7.73849	1.6	1.6	0.3	211.3	21	525
4.55388	7.62071	3.4	5.9	-0.6	45	13	164
3.21888	5.39363	3.3	1.7	-0.1	187	4	202
2.77259	5.87774	-5.4	5.1	-0.2	72.2	2	441
3.29584	5.8522	4.2	1.7	1	175	5	587
3.3322	8.02027	2	6	-0.1	152	18	61
4.02535	7.23706	-14.2	1.4	-0.1	238.1	18	457
4.38203	7.83281	9.4	2.9	-0.3	248.4	15	540
2.99573	4.60517	1.5	1.1	0.2	233.7	3	480
3.55535	8.11073	0.6	6.8	0.3	10	8	54
3.2581	7.57917	2.5	3	-0.2	83	13	64
4.47734	8.31361	-0.3	3	-0.8	71.8	8	554
4.06044	4.86753	1.4	0.7	1.5	79.4	4	563
3.21888	7.50879	-0.7	4	-0.5	65.1	11	507
4.46591	6.51323	-8.1	0.7	1.2	111	24	143
3.13549	6.6107	-13.1	3.5	0	83	9	97
2.30259	4.70048	6.3	6	0.9	219.2	2	586
3.61092	6.94986	-3.8	0.7	-0.2	258	23	74
3.13549	5.72685	-3.6	0.8	4	302.4	5	470
3.89182	8.16337	-1.1	1.5	-0.2	157.4	16	521
3.21888	7.7012	15.8	3.9	-0.4	119.5	15	595
3.17805	4.45435	-5.1	3.4	0.4	58.4	3	507
3.73767	8.28248	8.5	3.1	-0.7	238	8	205
2.3979	8.05833	-2.1	4.1	-0.2	82	8	114
3.68888	7.90027	1.5	0.5	-0.6	99.2	13	509
4.26268	7.34148	3	0.8	0.6	86	21	32
4.94164	7.90027	-12.6	2.2	0.2	77.8	19	488
3.29584	7.65917	3	3.1	-0.1	221	14	169
2.07944	5.60947	-5.2	2.9	0.1	60	2	90
2.19722	7.15227	3.5	0.9	0.2	265	22	169
2.07944	5.42495	2.8	1.3	0	75.8	2	404
4.54329	7.8071	6.5	4.3	-1.1	44	11	173
2.07944	7.94839	2.5	3.6	-0.1	54.7	16	574
4.36945	8.34069	6.3	4.2	-0.1	76	16	166
3.97029	7.54062	-1.4	1.7	-0.2	86	12	51
3.98898	5.89164	1.7	1.1	2.4	173.1	1	533
2.99573	7.52402	2.4	7.9	-0.1	43.5	21	557
3.52636	6.43294	-0.1	1.6	-0.2	75	22	55
2.99573	7.50219	3.1	1.7	0.3	269	12	69
5.39363	8.04045	12.9	8.3	-1.1	357.5	14	551
3.63759	7.31986	-13.4	3.8	0.1	75	11	448
2.30259	5.43372	0.7	3.9	0.1	182.9	2	491
4.92725	7.94058	2.4	1	0.4	110.7	7	533
2.94444	8.17808	-1.3	5.6	-0.2	46	17	149
2.70805	7.65728	-6.3	4	-0.1	51.6	19	408
2.07944	7.56164	3.5	1.8	0	71.2	21	576
3.4012	7.98752	0	4	-0.3	78.5	9	430
5.05625	7.63143	11.8	2.4	-1.5	232.5	14	535
4.93447	7.63192	5.8	1.9	-3.1	260.8	12	541
2.56495	7.6324	3.1	4.7	-0.1	48	12	576
3.04452	7.9248	1.4	2.1	0	221	17	525
1.38629	4.30407	0.8	3.1	1.6	77	3	50
2.77259	7.67555	6.8	9.9	-0.2	249	15	160
3.91202	7.1025	1.3	8	0	61.6	23	557
4.35671	7.95437	6.8	1.5	-0.6	124.9	7	585
3.4012	7.87474	-6.8	1	0	207	15	84
4.02535	7.85166	10.5	4.4	-0.9	69	15	198
3.73767	8.26049	3.8	5.2	0	222	8	163
2.94444	7.7411	-12.8	3.6	0.1	68.4	14	460
4.09434	5.24702	1.3	0.8	1.1	72.3	5	563
3.04452	5.24175	-4.2	1.4	3.6	230	2	155
3.17805	7.55224	-0.6	2.5	-0.1	75	13	111
2.56495	7.96242	-6.1	5.2	0.4	27	18	82
2.63906	6.85751	5.1	5.5	0.1	230	10	126
3.09104	8.1191	0.8	3	-0.1	78	17	65
3.4012	8.00068	-4.4	3.7	-0.1	78	14	68
2.19722	4.11087	-6.5	2.1	-0.1	68.2	5	451
3.7612	4.81218	-3.7	0.9	-0.1	281.3	4	513
2.77259	6.42811	0.7	2.9	1.4	229.6	24	473
3.4012	4.95583	-5.7	3.3	0.6	72	2	401
3.61092	6.16121	8.4	3.3	0.7	80.5	24	564
3.3673	6.55251	-0.2	2.8	0.7	71	23	72
0.69315	5.1299	-1.9	4.5	0.4	17	2	40
2.94444	5.18739	-7.3	1.9	0.5	85	3	118
4.17439	7.91608	0.1	2.1	2	77	18	49
2.83321	7.33954	-5.9	2.9	0.2	64.6	22	406
3.04452	7.0076	4.7	5.6	0	69	23	211
3.2581	7.87664	4.2	4.4	0.6	189.8	15	472
3.21888	7.23778	2.1	5.4	0.1	159	21	61
4.23411	7.64348	-0.5	2.1	-2	247.8	14	513
4.81218	7.08339	-3.7	1.1	0.1	192	22	101
4.39445	7.95718	6.5	6.2	-0.1	210.8	18	547
2.63906	6.42162	2.1	4.1	0.7	260	24	43
3.2581	7.29029	-3.1	1	-0.2	211	21	102
1.94591	5.42495	-12.8	0.8	1.2	55	6	91
2.94444	7.29029	3.8	4.3	0.5	210	22	105
2.56495	7.52348	3.4	6	0.2	191	14	482
3.21888	5.63479	-4.2	2.1	1.1	91	2	68
2.77259	7.97694	-1.7	5.2	0	76	8	124
4.61512	7.78197	5.4	3.6	-1.1	44	10	173
1.60944	5.743	3.4	4.9	0.7	243	2	140
2.07944	5.743	-0.2	5.6	0.2	16.6	2	553
3.8712	5.62762	2.1	1.5	2	227.8	5	533
3.17805	7.85477	-12.5	3.2	0.2	82.1	15	456
3.58352	7.56941	-4.1	4.3	-0.1	44.7	14	409
4.68213	7.08087	-9.8	0.5	0.2	131	21	83
2.56495	7.43603	-4.9	4.7	-0.1	79.6	21	441
2.30259	6.11368	-5.2	3.5	0	59	1	90
3.55535	7.97488	-1.6	3.5	0.5	34.5	18	480
3.46574	6.69084	6.4	1.2	0	188	6	207
2.48491	7.79523	2	2.8	1.6	242	18	127
4.21951	5.21494	-8.5	1.5	2.8	279	10	93
2.30259	7.65112	3.6	7.6	0.5	199.3	17	482
2.70805	6.12905	0.4	0.8	0.1	297	7	524
2.99573	8.08979	1.2	3.6	0.1	63.9	15	411
2.48491	5.76519	-3.5	3.2	0	240.2	2	504
3.13549	8.06401	3.6	2.3	-0.3	9.4	9	549
4.15888	7.39265	-14.9	1.9	0.8	65	20	91
1.79176	6.43294	1	2.5	-0.2	75	10	70
3.29584	7.62462	-1.7	1.6	-0.1	172	19	51
2.83321	5.22036	3.4	1.1	1.4	89	6	565
3.52636	6.35611	6.9	7	0.8	220	24	34
3.2581	4.47734	-4.8	2.5	0.4	84	3	101
3.98898	6.50279	4.2	3.9	0.6	78	24	197
1.60944	6.88959	1.5	4	0.1	240.4	22	523
3.49651	7.32909	-2.9	1.9	1.7	337.8	19	468
3.17805	7.3601	-8.2	4.7	-0.2	76.5	11	408
4.67283	7.77107	7	3.1	-0.3	73	10	185
3.43399	6.16121	8.1	1.9	-0.1	211.9	1	578
2.07944	7.97281	3.7	4.2	0.2	172	9	128
3.29584	7.90323	0.7	2.1	1.3	207.1	18	473
2.30259	8.16223	0.4	8.6	0.1	34	17	39
2.07944	4.83628	-10.4	1.9	1.1	75	7	86
0.69315	4.91998	6.5	4.5	0.4	206	2	57
3.13549	7.47591	9	4.6	-0.9	208.7	10	587
3.82864	7.52941	10.1	1.1	-3.1	255	11	188
3.8712	7.88796	-0.3	1.7	0.4	26.9	15	416
2.99573	4.47734	-4.4	2.6	0.8	85	4	38
3.2581	7.73281	1.7	1.5	1.1	345	19	158
3.3322	6.93245	-3.2	0.8	-0.1	260	22	68
3.49651	7.65634	-5.9	3.1	-0.1	77	16	85
1.94591	7.0193	3.7	3.6	0.1	195	22	107
4.57471	8.0762	9.9	2.2	0	92	16	177
3.91202	7.90175	7.4	4.3	-0.3	178	18	190
3.80666	6.89669	5.1	2.5	0.7	80	23	197
3.93183	4.30407	3.2	1.5	0.4	75	3	194
2.48491	5.15906	0	4.3	0.1	102.7	3	477
3.13549	7.82764	-5.6	6.1	-0.2	79.5	17	441
2.94444	7.02554	0.7	2.4	-0.2	232.5	23	542
3.4012	7.90175	15.1	2.9	0.2	113.9	19	567
1.94591	5.73334	-2.2	4.3	-0.1	72	5	124
3.17805	5.34711	-3.8	2.7	-0.2	80.5	3	511
3.21888	7.58477	4.2	6.5	0.4	214.3	11	474
2.63906	6.84055	-13.1	4.8	-0.2	52.8	11	462
3.3322	7.55381	-0.2	3.9	-0.2	79.6	12	430
2.70805	4.78749	1.5	3.2	0.1	153	4	61
3.78419	6.86485	0.8	1.5	-0.1	231.5	23	540
3.78419	7.81197	-6.6	2.2	1.3	87	7	122
1.60944	7.89469	2.1	4	-0.1	47.8	14	574
3.3673	7.76132	1.7	2.9	-0.1	81	14	60
3.04452	6.07764	-4.1	5.4	-0.1	66.5	1	427
4.45435	7.80954	18.2	3.7	-2.7	250.7	15	568
1.94591	5.4848	2.2	2	1.9	253.8	5	473
2.63906	5.27811	-7.8	4.6	-0.1	71.5	19	450
3.3322	7.76684	-5.4	3	-0.2	77	14	85
3.43399	5.5835	-4.5	4.1	0.3	59.4	1	507
3.89182	7.86634	8	6.1	0.1	197	19	212
2.19722	8.19644	2.7	3.8	0.2	139	8	128
4.59512	7.59186	-5.6	3.9	-0.2	41	13	116
3.49651	5.54126	4.6	0.9	0.5	108	5	190
2.83321	7.55799	12	4.4	0.2	274.2	20	584
2.19722	6.51471	4.4	2	0.7	205	6	33
2.89037	6.21461	4.5	3.5	0	55	9	196
3.4012	6.63988	0.2	0.5	1.4	84.8	24	483
2.99573	6.02345	-18.6	2.3	0.1	79.4	7	457
3.73767	7.62413	0.1	2.6	1.5	230	19	175
2.94444	5.84064	7.4	4.3	0.3	189.6	1	590
3.58352	8.32579	7.6	4.2	0	224.1	8	583
3.29584	7.32053	8.6	2.6	-0.1	189.2	22	577
4.8752	7.91571	-2.5	3.6	0	64	15	143
4.33073	7.7424	10.6	1.8	-1.6	245	14	177
3.29584	6.91771	-1.6	1.1	0	282	23	51
3.78419	7.78239	5	3.1	-0.4	230	16	545
3.61092	5.18739	5.2	4.4	0.8	206	4	126
2.83321	6.14204	5.3	2.3	1.2	153.1	1	587
3.7612	5.48064	-13.1	1.1	1.3	62.9	5	464
3.58352	5.2933	3.3	2.2	0.5	77.1	2	562
3.17805	5.4848	-0.8	3.5	0	176.8	5	522
3.52636	5.03044	-4	1.5	-0.1	108.2	2	521
4.21951	7.6406	1.7	2.3	-0.3	85	15	167
3.93183	8.29255	6.1	4.2	0.3	23	17	173
2.30259	5.70378	5.8	2.8	0	43	7	189
3.2581	5.4848	5.7	3.3	0.5	228	5	204
2.70805	7.02997	14.9	2.8	-5	255.5	10	594
3.55535	5.31321	-5.7	2.2	0.6	82	5	165
1.94591	6.14419	0.2	3.1	0.1	73	9	56
4.36945	5.14749	-9	1.3	0.4	77	2	88
4.70048	7.66388	16.8	6	-1.1	272	14	205
2.48491	5.8944	2.8	4.7	0	66	2	574
1.60944	7.61085	-0.1	6.6	-0.3	205.2	14	489
3.78419	7.52294	-12.5	0.8	-0.4	232	19	463
1.94591	4.82831	-7.8	3.7	0.1	58.4	4	407
3.09104	7.54908	0.9	1	-0.6	213.1	12	484
3.21888	7.75833	-4.2	4.6	-0.1	78	10	120
2.48491	7.50659	21.9	3.6	-2.5	264.1	19	608
2.19722	6.07764	-10.1	2.8	0	73	1	89
4.93447	7.01571	-9.4	1.2	0	73.8	23	437
4.04305	7.59488	11.2	1.7	-2	236	12	188
4.51086	8.20576	1.8	3.2	0.2	216.6	8	551
1.94591	4.59512	0.5	5.5	0.1	354	4	171
3.09104	8.23297	-2.2	1.6	-0.3	211.7	16	501
4.60517	8.31532	-0.1	5.2	-0.8	79	8	555
3.63759	7.57096	8.4	5.4	-1.8	240	11	163
3.46574	7.61036	0.9	3	-0.7	340	12	142
3.8712	8.21528	-2.9	1.4	-0.2	113.4	8	521
5.15329	8.19146	2.3	2.5	0.6	16	17	44
3.04452	7.67322	-1.4	4.4	-0.1	38.1	14	410
3.49651	7.78655	0.4	1.5	-0.1	110	13	124
2.56495	4.69135	-11.3	2.5	1.2	82	5	87
3.85015	7.629	0.1	3.9	-0.2	70.7	14	506
2.48491	5.49717	-5	4	-0.1	75.4	5	440
2.07944	6.57368	1.6	2	1.2	212.7	6	472
2.99573	7.90618	-2.8	2.3	-0.1	205	20	511
1.38629	6.55251	-3.5	3.9	-0.1	46.3	24	409
3.17805	8.22013	2	3.5	-0.2	82	8	64
2.19722	5.42935	2.4	3.6	0.1	125	5	128
3.55535	7.89096	7.1	3.4	-0.3	82.5	19	571
3.8712	8.02453	-2.7	1.4	1.6	66	9	73
3.4012	7.14283	-1.3	1.2	0.6	66	21	113
3.04452	7.97039	5.5	3.9	-0.5	231	14	131
4.34381	6.65801	5.5	1.1	0.9	138	24	199
2.3979	7.10003	-4	5.8	-0.4	78.4	11	399
2.63906	7.65681	-0.2	2.4	0	41	19	157
2.56495	4.70953	-2.8	1.8	1.4	198.9	3	537
3.3322	5.60212	3.8	3.9	-0.2	85	5	187
2.56495	7.85127	0.2	2.8	0.1	78	17	112
3.68888	7.89655	12.8	6.8	-0.6	227.7	15	583
2.3979	7.20638	-0.8	1.6	1.7	192.6	22	469
2.3979	4.89035	-10.8	1.5	0.2	57	3	460
4.68213	7.56735	-10.2	0.6	2.4	110.8	10	465
3.04452	7.61135	0.8	3.1	-0.1	65.6	19	429
3.04452	6.66823	4.1	1.4	0.2	139.8	24	572
2.19722	5.0689	4.3	4.9	0.1	82	2	212
3.17805	7.46107	-2.8	1.4	-0.1	243	20	102
4.48864	7.68018	-0.4	0.8	2.6	147	21	49
2.30259	7.80344	2.4	3.9	0.2	14.3	20	574
3.29584	8.26256	3.4	0.9	0.3	228	16	109
3.17805	7.6695	12.1	2.8	-3.2	256	13	203
4.06044	5.33272	-2.5	2	1.3	80.7	3	532
4.15888	6.30992	-4.5	2	0.3	90	24	100
3.09104	6.67456	2.2	7.1	0.1	37.9	6	557
4.41884	8.09316	-4.7	1.4	1.2	276.8	18	445
2.89037	6.56667	0.1	2.1	-0.2	247.4	6	543
1.09861	5.83481	-5.8	7.7	0.1	47	7	90
3.43399	7.794	3.3	4.3	0.8	217	17	105
3.52636	7.76089	2	1.7	1.1	234	15	121
3.13549	7.67276	1.3	4.2	0.2	169	10	61
2.19722	7.979	1.4	5	-0.1	40.7	9	480
3.13549	7.78406	6.1	1.9	0.4	171	21	210
2.07944	5.95842	-3.1	4.2	-0.1	52.5	2	426
3.63759	4.54329	-11.5	1.7	3.7	90.4	4	465
3.73767	5.62762	-6.6	1	-0.1	200	2	445
2.48491	5.66643	-3.8	2.1	0.6	73	3	77
1.94591	7.54539	6.5	9.4	-0.9	250	11	160
1.94591	6.23832	-0.4	3.3	0.3	215.1	24	470
2.77259	4.56435	-6.3	2	2.3	223	3	148
2.70805	6.58203	2.2	1.8	0.1	64.2	6	549
1.79176	5.31321	-4.9	4.2	0.3	353	2	117
2.30259	5.61677	-1.3	2.8	-0.1	65.2	1	486
4.11087	7.7111	-5.1	0.7	0.3	60	10	99
3.4012	6.2519	0.1	1	0.2	87	24	111
3.68888	7.85516	6.5	5.2	-0.2	69	19	196
4.17439	8.24512	8.6	1.6	-1	258.8	15	530

library(party)
library(Hmisc)
par1 <- as.numeric(par1)
par3 <- as.numeric(par3)
x <- data.frame(t(y))
is.data.frame(x)
x <- x[!is.na(x[,par1]),]
k <- length(x[1,])
n <- length(x[,1])
colnames(x)[par1]
x[,par1]
if (par2 == 'kmeans') {
cl <- kmeans(x[,par1], par3)
print(cl)
clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
clm <- clm[sort.list(clm[,1]),]
for (i in 1:par3) {
cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')
}
cl$cluster <- as.factor(cl$cluster)
print(cl$cluster)
x[,par1] <- cl$cluster
}
if (par2 == 'quantiles') {
x[,par1] <- cut2(x[,par1],g=par3)
}
if (par2 == 'hclust') {
hc <- hclust(dist(x[,par1])^2, 'cen')
print(hc)
memb <- cutree(hc, k = par3)
dum <- c(mean(x[memb==1,par1]))
for (i in 2:par3) {
dum <- c(dum, mean(x[memb==i,par1]))
}
hcm <- matrix(cbind(dum,1:par3),ncol=2)
hcm <- hcm[sort.list(hcm[,1]),]
for (i in 1:par3) {
memb[memb==hcm[i,2]] <- paste('C',i,sep='')
}
memb <- as.factor(memb)
print(memb)
x[,par1] <- memb
}
if (par2=='equal') {
ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))
x[,par1] <- as.factor(ed)
}
table(x[,par1])
colnames(x)
colnames(x)[par1]
x[,par1]
if (par2 == 'none') {
m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
}
load(file='createtable')
if (par2 != 'none') {
m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
if (par4=='yes') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Prediction (training)',par3+1,TRUE)
a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Actual',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
a<-table.row.end(a)
for (i in 1:10) {
ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
if (i==1) {
m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
m.ct.i.actu <- x[ind==1,par1]
m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
m.ct.x.actu <- x[ind==2,par1]
} else {
m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
}
}
print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
numer <- numer + m.ct.i.tab[i,i]
}
print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
numer <- numer + m.ct.x.tab[i,i]
}
print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'Overall',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.i.cp,4))
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.x.cp,4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
}
}
m
bitmap(file='test1.png')
plot(m)
dev.off()
bitmap(file='test1a.png')
plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
dev.off()
if (par2 == 'none') {
forec <- predict(m)
result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
colnames(result) <- c('Actuals','Forecasts','Residuals')
print(result)
}
if (par2 != 'none') {
print(cbind(as.factor(x[,par1]),predict(m)))
myt <- table(as.factor(x[,par1]),predict(m))
print(myt)
}
bitmap(file='test2.png')
if(par2=='none') {
op <- par(mfrow=c(2,2))
plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
par(op)
}
if(par2!='none') {
plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
}
dev.off()
if (par2 == 'none') {
detcoef <- cor(result$Forecasts,result$Actuals)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goodness of Fit',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.element(a,round(detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'R-squared',1,TRUE)
a<-table.element(a,round(detcoef*detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'RMSE',1,TRUE)
a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Actuals',header=TRUE)
a<-table.element(a,'Forecasts',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(result$Actuals)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,result$Actuals[i])
a<-table.element(a,result$Forecasts[i])
a<-table.element(a,result$Residuals[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
}
if (par2 != 'none') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
for (i in 1:par3) {
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (j in 1:par3) {
a<-table.element(a,myt[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}