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AccelerationConsortium
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Hartmann-4-Source-Data

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train · 100 rows

Unnamed: 0 int64 0 99	x1 float64 0.03 1	x2 float64 0.03 0.98	x3 float64 0.02 1	x4 float64 0.01 0.99	Target float64 -0.66 4.42
0	0.710878	0.312552	0.56084	0.47709	1.515504
1	0.994973	0.653594	0.373074	0.269167	-0.094808
2	0.176687	0.754632	0.444048	0.339134	1.718351
3	0.676859	0.654327	0.031384	0.137092	0.154643
4	0.100842	0.486377	0.72523	0.446163	3.252221
5	0.397967	0.564109	0.988932	0.784126	1.844571
6	0.639818	0.096968	0.017788	0.695478	0.290557
7	0.949773	0.853657	0.422621	0.859823	-0.466309
8	0.507647	0.878471	0.674828	0.398342	2.677061
9	0.1719	0.660694	0.065723	0.591949	1.37814
10	0.739076	0.83093	0.141713	0.755642	0.045379
11	0.327121	0.069581	0.463108	0.229524	4.417969
12	0.307738	0.931921	0.513254	0.077792	0.089763
13	0.561276	0.740555	0.211167	0.061523	0.308769
14	0.612408	0.558861	0.659928	0.308541	2.396844
15	0.511017	0.969269	0.833307	0.583252	3.093791
16	0.53976	0.188807	0.45179	0.442362	2.21277
17	0.271116	0.488797	0.502488	0.05577	2.209404
18	0.541476	0.294652	0.888138	0.808448	1.117531
19	0.445029	0.386932	0.333839	0.539518	1.611
20	0.074457	0.220206	0.372902	0.735788	0.002745
21	0.605763	0.381591	0.819943	0.446017	2.70142
22	0.868371	0.741953	0.569036	0.408415	-0.214694
23	0.195803	0.860349	0.789462	0.930357	-0.016374
24	0.390926	0.067844	0.952286	0.903237	-0.00027
25	0.345701	0.34485	0.816659	0.655835	2.445295
26	0.364129	0.942017	0.12397	0.584393	4.161736
27	0.701292	0.73805	0.286319	0.404289	0.765366
28	0.781586	0.958024	0.202801	0.700916	-0.404567
29	0.949378	0.610127	0.940244	0.568622	0.125068
30	0.674882	0.323062	0.282305	0.335645	1.118523
31	0.547502	0.425907	0.987561	0.903148	1.0951
32	0.047665	0.832342	0.798164	0.698491	0.296577
33	0.195605	0.74261	0.742618	0.033438	1.190078
34	0.248218	0.690867	0.312666	0.158769	1.813864
35	0.092403	0.947405	0.170689	0.314198	-0.076504
36	0.618529	0.26726	0.185171	0.015958	0.460042
37	0.675887	0.739787	0.047954	0.822985	0.061697
38	0.266294	0.66966	0.29979	0.741236	1.593333
39	0.996301	0.338096	0.420883	0.098879	-0.01956
40	0.198543	0.772444	0.432354	0.146401	0.942245
41	0.98797	0.670369	0.796832	0.654998	0.136941
42	0.851055	0.621744	0.771746	0.04903	0.555331
43	0.656416	0.154749	0.863322	0.666711	0.595528
44	0.130554	0.523061	0.738098	0.409587	3.258319
45	0.56977	0.26415	0.552177	0.458332	2.158404
46	0.171266	0.321995	0.792292	0.992241	1.042313
47	0.890956	0.535112	0.843802	0.162465	1.250926
48	0.418726	0.659492	0.705586	0.174152	1.946065
49	0.028549	0.698026	0.830313	0.732199	0.525667
50	0.162757	0.706447	0.442051	0.826844	0.634414
51	0.370831	0.635414	0.532736	0.495922	3.826129
52	0.041814	0.179127	0.730048	0.166121	3.804167
53	0.840821	0.97666	0.064521	0.870707	-0.662493
54	0.559806	0.122173	0.42478	0.427977	2.337973
55	0.182759	0.24328	0.961855	0.584819	1.3043
56	0.122465	0.402211	0.688667	0.00614	2.9022
57	0.767588	0.229995	0.981386	0.549103	0.403058
58	0.429719	0.789967	0.799476	0.848012	1.987739
59	0.527696	0.537364	0.395154	0.268001	1.946706
60	0.627348	0.550595	0.426406	0.337401	1.615088
61	0.874456	0.938092	0.752905	0.504701	-0.543997
62	0.603522	0.257413	0.05371	0.277301	2.026659
63	0.831825	0.461953	0.675119	0.305118	1.500742
64	0.891202	0.628902	0.173001	0.574771	-0.295242
65	0.206205	0.181326	0.849311	0.980949	0.361774
66	0.085146	0.821179	0.71167	0.198184	0.705083
67	0.192288	0.554789	0.279294	0.060684	1.339932
68	0.276135	0.696708	0.352057	0.302761	2.536102
69	0.525959	0.805621	0.786638	0.856357	1.263523
70	0.463642	0.871933	0.784995	0.981325	0.621531
71	0.982073	0.30862	0.040508	0.941612	-0.535563
72	0.891514	0.593615	0.658964	0.154204	0.693404
73	0.251113	0.322292	0.632507	0.597753	2.24919
74	0.835983	0.585227	0.861358	0.202361	1.20688
75	0.587611	0.129606	0.463095	0.175797	2.20484
76	0.492845	0.774614	0.788039	0.439428	3.703872
77	0.469039	0.308349	0.221011	0.605836	1.390761
78	0.485689	0.876681	0.430202	0.474379	3.493717
79	0.962685	0.264412	0.081172	0.955746	-0.625821
80	0.250123	0.376439	0.29527	0.635389	1.050335
81	0.779614	0.105944	0.330848	0.269391	1.136053
82	0.035642	0.409575	0.164219	0.934437	-0.403402
83	0.217492	0.073956	0.882629	0.254824	2.940732
84	0.49246	0.301548	0.52958	0.817371	0.263871
85	0.939652	0.0859	0.022918	0.405227	0.101757
86	0.939476	0.028139	0.636047	0.8443	-0.407852
87	0.566867	0.394247	0.420778	0.61272	1.024407
88	0.466693	0.178794	0.876052	0.771612	0.735504
89	0.680777	0.574909	0.99714	0.015119	0.27692
90	0.063738	0.838269	0.673817	0.843736	0.022698
91	0.092766	0.684531	0.56365	0.269386	1.573503
92	0.308721	0.345561	0.042725	0.634292	0.811175
93	0.615505	0.176496	0.042809	0.437352	1.094664
94	0.89538	0.810666	0.215467	0.941252	-0.484335
95	0.074323	0.405346	0.729614	0.439732	3.647117
96	0.189024	0.129029	0.839344	0.107861	2.838709
97	0.263062	0.711579	0.859694	0.601217	3.413759
98	0.715637	0.692236	0.411102	0.500907	0.262107
99	0.553983	0.222254	0.553537	0.510268	1.45902

This file contains some data points that can be used for the 4-dimensional transfer learning task.

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9.95 kB

Size of the auto-converted Parquet files:

8.1 kB

Number of rows:

100