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cutoff fix

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This commit is contained in:
Natalia Szymczyk 2023-04-20 23:46:06 +02:00
parent 5ec7c9ffcc
commit 7a2929ab94
4 changed files with 598 additions and 3 deletions
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120
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40,Male,0.9,0.3,196,69,48,6.8,3.1,0.8,1
65,Male,1.4,0.6,260,28,24,5.2,2.2,0.7,2
58,Male,1,0.4,182,14,20,6.8,3.4,1,1
32,Male,12.7,8.4,190,28,47,5.4,2.6,0.9,1
26,Male,1,0.3,163,48,71,7.1,3.7,1,2
55,Female,0.8,0.2,225,14,23,6.1,3.3,1.2,2
41,Male,1.2,0.5,246,34,42,6.9,3.4,0.97,1
65,Male,1.9,0.8,170,36,43,3.8,1.4,0.58,2
45,Male,2.2,1.6,320,37,48,6.8,3.4,1,1
45,Male,2.4,1.1,168,33,50,5.1,2.6,1,1
18,Male,0.8,0.2,228,55,54,6.9,4,1.3,1
52,Male,0.6,0.1,178,26,27,6.5,3.6,1.2,2
45,Male,0.8,0.2,140,24,20,6.3,3.2,1,2
58,Female,1.7,0.8,1896,61,83,8,3.9,0.95,1
40,Female,2.1,1,768,74,141,7.8,4.9,1.6,1
24,Male,0.7,0.2,218,47,26,6.6,3.3,1,1
30,Male,0.8,0.2,174,21,47,4.6,2.3,1,1
40,Male,3.5,1.6,298,68,200,7.1,3.4,0.9,1
37,Male,0.8,0.2,125,41,39,6.4,3.4,1.1,1
22,Male,0.6,0.2,202,78,41,8,3.9,0.9,1
36,Female,1.2,0.4,358,160,90,8.3,4.4,1.1,2
45,Female,3.5,1.5,189,63,87,5.6,2.9,1,1
32,Male,15.9,7,280,1350,1600,5.6,2.8,1,1
25,Male,0.6,0.1,183,91,53,5.5,2.3,0.7,2
48,Male,3.2,1.6,257,33,116,5.7,2.2,0.62,1
69,Male,0.9,0.2,215,32,24,6.9,3,0.7,1
52,Male,1.8,0.8,97,85,78,6.4,2.7,0.7,1
54,Male,0.8,0.2,218,20,19,6.3,2.5,0.6,1
60,Male,2.9,1.3,230,32,44,5.6,2,0.5,1
53,Female,0.7,0.1,182,20,33,4.8,1.9,0.6,1
33,Male,1.5,7,505,205,140,7.5,3.9,1,1
37,Male,0.8,0.2,147,27,46,5,2.5,1,1
48,Female,0.9,0.2,175,24,54,5.5,2.7,0.9,2
38,Male,3.7,2.2,216,179,232,7.8,4.5,1.3,1
42,Male,8.9,4.5,272,31,61,5.8,2,0.5,1
42,Male,8.9,4.5,272,31,61,5.8,2,0.5,1
60,Male,22.8,12.6,962,53,41,6.9,3.3,0.9,1
33,Male,0.8,0.2,198,26,23,8,4,1,2
36,Male,1.7,0.5,205,36,34,7.1,3.9,1.2,1
28,Male,0.6,0.1,177,36,29,6.9,4.1,1.4,2
65,Male,0.8,0.2,201,18,22,5.4,2.9,1.1,2
62,Male,0.7,0.2,162,12,17,8.2,3.2,0.6,2
70,Male,1.4,0.6,146,12,24,6.2,3.8,1.58,2
40,Male,1.1,0.3,230,1630,960,4.9,2.8,1.3,1
38,Male,1.7,0.7,859,89,48,6,3,1,1
28,Female,0.8,0.2,309,55,23,6.8,4.1,1.51,1
75,Female,0.8,0.2,188,20,29,4.4,1.8,0.6,1
66,Male,17.3,8.5,388,173,367,7.8,2.6,0.5,1
58,Female,2.8,1.3,670,48,79,4.7,1.6,0.5,1
52,Male,0.9,0.2,156,35,44,4.9,2.9,1.4,1
60,Male,5.8,2.7,599,43,66,5.4,1.8,0.5,1
50,Female,27.7,10.8,380,39,348,7.1,2.3,0.4,1
22,Female,6.7,3.2,850,154,248,6.2,2.8,0.8,1
23,Male,1,0.3,212,41,80,6.2,3.1,1,1
49,Female,0.8,0.2,158,19,15,6.6,3.6,1.2,2
31,Male,0.6,0.1,175,48,34,6,3.7,1.6,1
31,Male,0.6,0.1,175,48,34,6,3.7,1.6,1
60,Male,0.7,0.2,174,32,14,7.8,4.2,1.1,2
38,Male,0.9,0.3,310,15,25,5.5,2.7,1,1
18,Male,0.7,0.1,312,308,405,6.9,3.7,1.1,1
33,Male,2,1.4,2110,48,89,6.2,3,0.9,1
46,Male,18.4,8.5,450,119,230,7.5,3.3,0.7,1
48,Female,1.1,0.7,527,178,250,8,4.2,1.1,1
35,Male,1.8,0.6,275,48,178,6.5,3.2,0.9,2
29,Male,1.2,0.4,160,20,22,6.2,3,0.9,2
62,Male,0.7,0.2,173,46,47,7.3,4.1,1.2,2
55,Male,4.4,2.9,230,14,25,7.1,2.1,0.4,1
34,Male,5.9,2.5,290,45,233,5.6,2.7,0.9,1
50,Male,0.9,0.2,202,20,26,7.2,4.5,1.66,1
33,Male,1.6,0.5,165,15,23,7.3,3.5,0.92,2
46,Male,1.8,0.7,208,19,14,7.6,4.4,1.3,1
46,Male,0.6,0.2,290,26,21,6,3,1,1
72,Male,0.7,0.1,196,20,35,5.8,2,0.5,1
72,Male,0.7,0.1,196,20,35,5.8,2,0.5,1
51,Male,0.8,0.2,367,42,18,5.2,2,0.6,1
21,Male,0.7,0.2,135,27,26,6.4,3.3,1,2
38,Male,1.7,1,180,18,34,7.2,3.6,1,1
26,Male,2,0.9,195,24,65,7.8,4.3,1.2,1
22,Male,0.8,0.2,198,20,26,6.8,3.9,1.3,1
10,Female,0.8,0.1,395,25,75,7.6,3.6,0.9,1
69,Female,0.8,0.2,146,42,70,8.4,4.9,1.4,2
66,Female,4.2,2.1,159,15,30,7.1,2.2,0.4,1
42,Male,0.7,0.2,197,64,33,5.8,2.4,0.7,2
60,Male,11.5,5,1050,99,187,6.2,2.8,0.8,1
67,Male,2.2,1.1,198,42,39,7.2,3,0.7,1
45,Male,2.5,1.2,163,28,22,7.6,4,1.1,1
46,Male,9.4,5.2,268,21,63,6.4,2.8,0.8,1
74,Female,1.1,0.4,214,22,30,8.1,4.1,1,1
17,Male,0.9,0.2,224,36,45,6.9,4.2,1.55,1
75,Male,0.9,0.2,282,25,23,4.4,2.2,1,1
60,Male,3.2,1.8,750,79,145,7.8,3.2,0.69,1
26,Male,1.7,0.6,210,62,56,5.4,2.2,0.6,1
49,Female,0.6,0.1,185,17,26,6.6,2.9,0.7,2
32,Male,0.6,0.1,237,45,31,7.5,4.3,1.34,1
53,Male,19.8,10.4,238,39,221,8.1,2.5,0.4,1
26,Male,7.1,3.3,258,80,113,6.2,2.9,0.8,1
31,Male,0.8,0.2,198,43,31,7.3,4,1.2,1
72,Male,0.7,0.2,185,16,22,7.3,3.7,1,2
74,Male,1,0.3,175,30,32,6.4,3.4,1.1,1
48,Male,2.4,1.1,554,141,73,7.5,3.6,0.9,1
33,Male,0.8,0.2,135,30,29,7.2,4.4,1.5,2
75,Male,10.6,5,562,37,29,5.1,1.8,0.5,1
61,Male,1.5,0.6,196,61,85,6.7,3.8,1.3,2
58,Male,0.8,0.2,298,33,59,6.2,3.1,1,1
70,Male,1.7,0.5,400,56,44,5.7,3.1,1.1,1
58,Male,1,0.5,158,37,43,7.2,3.6,1,1
58,Male,1,0.5,158,37,43,7.2,3.6,1,1
48,Male,0.7,0.2,208,15,30,4.6,2.1,0.8,2
31,Female,1.1,0.3,190,26,15,7.9,3.8,0.9,1
32,Male,12.7,6.2,194,2000,2946,5.7,3.3,1.3,1
21,Male,3.9,1.8,150,36,27,6.8,3.9,1.34,1
61,Male,0.7,0.2,145,53,41,5.8,2.7,0.87,1
70,Male,1.3,0.3,690,93,40,3.6,2.7,0.7,1
13,Male,0.6,0.1,320,28,56,7.2,3.6,1,2
26,Female,0.6,0.2,142,12,32,5.7,2.4,0.75,1
61,Male,0.8,0.2,192,28,35,6.9,3.4,0.9,2
29,Male,0.7,0.2,165,55,87,7.5,4.6,1.58,1
56,Male,17.7,8.8,239,43,185,5.6,2.4,0.7,1
47,Male,0.9,0.2,192,38,24,7.3,4.3,1.4,1
45,Male,2.8,1.7,263,57,65,5.1,2.3,0.8,1
60,Male,0.9,0.3,168,16,24,6.7,3,0.8,1
66,Male,0.7,0.2,239,27,26,6.3,3.7,1.4,1
17,Male,0.9,0.3,202,22,19,7.4,4.1,1.2,2
45,Female,23.3,12.8,1550,425,511,7.7,3.5,0.8,1
50,Male,0.7,0.2,192,18,15,7.4,4.2,1.3,2
65,Male,0.7,0.2,199,19,22,6.3,3.6,1.3,2
55,Male,0.6,0.2,220,24,32,5.1,2.4,0.88,1
65,Male,0.8,0.2,162,30,90,3.8,1.4,0.5,1
33,Male,1.2,0.3,498,28,25,7,3,0.7,1
38,Male,0.7,0.2,110,22,18,6.4,2.5,0.64,1
48,Female,0.8,0.2,142,26,25,6,2.6,0.7,1
42,Male,0.8,0.2,198,29,19,6.6,3,0.8,2
66,Male,0.8,0.2,165,22,32,4.4,2,0.8,1
21,Male,0.7,0.2,211,14,23,7.3,4.1,1.2,2
22,Male,2.4,1,340,25,21,8.3,4.5,1.1,1
4,Male,0.8,0.2,460,152,231,6.5,3.2,0.9,2
40,Male,3.6,1.8,285,50,60,7,2.9,0.7,1
54,Male,0.8,0.2,181,35,20,5.5,2.7,0.96,1
58,Female,2.4,1.1,915,60,142,4.7,1.8,0.6,1
42,Male,1,0.3,154,38,21,6.8,3.9,1.3,2
60,Male,8.6,4,298,412,850,7.4,3,0.6,1
48,Female,0.8,0.2,218,32,28,5.2,2.5,0.9,2
43,Female,0.9,0.3,140,12,29,7.4,3.5,1.8,1
65,Male,0.8,0.1,146,17,29,5.9,3.2,1.18,2
35,Female,0.9,0.3,158,20,16,8,4,1,1
36,Male,5.3,2.3,145,32,92,5.1,2.6,1,2
36,Male,5.3,2.3,145,32,92,5.1,2.6,1,2
12,Male,0.8,0.2,302,47,67,6.7,3.5,1.1,2
52,Male,0.8,0.2,245,48,49,6.4,3.2,1,1
39,Male,0.6,0.2,188,28,43,8.1,3.3,0.6,1
60,Male,5.8,3,257,107,104,6.6,3.5,1.12,1
40,Male,0.6,0.1,98,35,31,6,3.2,1.1,1
47,Female,0.8,0.2,236,10,13,6.7,2.9,0.76,2
68,Female,0.6,0.1,1620,95,127,4.6,2.1,0.8,1
29,Male,0.8,0.2,156,12,15,6.8,3.7,1.1,2
35,Female,1,0.3,805,133,103,7.9,3.3,0.7,1
54,Male,0.9,0.2,290,15,18,6.1,2.8,0.8,1
50,Male,0.6,0.2,137,15,16,4.8,2.6,1.1,1
33,Male,0.7,0.2,256,21,30,8.5,3.9,0.8,1
37,Male,0.8,0.2,195,60,40,8.2,5,1.5,2
26,Male,1.9,0.8,180,22,19,8.2,4.1,1,2
42,Female,0.8,0.2,168,25,18,6.2,3.1,1,1
24,Male,3.3,1.6,174,11,33,7.6,3.9,1,2
75,Male,14.8,9,1020,71,42,5.3,2.2,0.7,1
38,Male,1.5,0.4,298,60,103,6,3,1,2
24,Female,0.7,0.2,188,11,10,5.5,2.3,0.71,2
38,Male,1.1,0.3,198,86,150,6.3,3.5,1.2,1
53,Male,1.6,0.9,178,44,59,6.5,3.9,1.5,2
78,Male,1,0.3,152,28,70,6.3,3.1,0.9,1
41,Female,0.9,0.2,201,31,24,7.6,3.8,1,2
26,Male,2,0.9,157,54,68,6.1,2.7,0.8,1
66,Male,11.3,5.6,1110,1250,4929,7,2.4,0.5,1
63,Male,0.9,0.2,194,52,45,6,3.9,1.85,2
64,Male,1.1,0.5,145,20,24,5.5,3.2,1.39,2
36,Female,0.7,0.2,152,21,25,5.9,3.1,1.1,2
31,Female,0.8,0.2,215,15,21,7.6,4,1.1,1
33,Male,2.1,0.7,205,50,38,6.8,3,0.7,1
48,Male,0.7,0.1,1630,74,149,5.3,2,0.6,1
28,Male,0.6,0.2,159,15,16,7,3.5,1,2
38,Female,2.6,1.2,410,59,57,5.6,3,0.8,2
38,Female,2.6,1.2,410,59,57,5.6,3,0.8,2
40,Male,0.7,0.1,202,37,29,5,2.6,1,1
32,Male,0.9,0.3,462,70,82,6.2,3.1,1,1
40,Male,0.7,0.2,176,28,43,5.3,2.4,0.8,2
55,Male,0.8,0.2,290,139,87,7,3,0.7,1
45,Female,0.6,0.1,270,23,42,5.1,2,0.5,2
66,Male,0.6,0.2,100,17,148,5,3.3,1.9,2
66,Male,16.6,7.6,315,233,384,6.9,2,0.4,1
60,Male,0.8,0.2,286,21,27,7.1,4,1.2,1
57,Male,4.5,2.3,315,120,105,7,4,1.3,1
32,Male,25,13.7,560,41,88,7.9,2.5,2.5,1
55,Male,3.6,1.6,349,40,70,7.2,2.9,0.6,1
40,Male,1.2,0.6,204,23,27,7.6,4,1.1,1
29,Female,0.8,0.2,205,30,23,8.2,4.1,1,1
35,Male,0.8,0.2,279,20,25,7.2,3.2,0.8,1
27,Male,0.6,0.2,161,27,28,3.7,1.6,0.76,2
50,Female,1,0.3,191,22,31,7.8,4,1,2
36,Male,0.8,0.2,182,31,34,6.4,3.8,1.4,2
60,Male,6.3,3.2,314,118,114,6.6,3.7,1.27,1
50,Female,0.7,0.1,192,20,41,7.3,3.3,0.8,1
35,Male,0.8,0.2,198,36,32,7,4,1.3,2
30,Male,0.7,0.2,262,15,18,9.6,4.7,1.2,1
33,Male,0.7,0.1,168,35,33,7,3.7,1.1,1
70,Male,0.6,0.1,862,76,180,6.3,2.7,0.75,1
33,Male,2,1,258,194,152,5.4,3,1.25,1
68,Male,0.7,0.1,145,20,22,5.8,2.9,1,1
13,Female,0.7,0.2,350,17,24,7.4,4,1.1,1
61,Male,0.8,0.1,282,85,231,8.5,4.3,1,1
46,Male,0.6,0.2,115,14,11,6.9,3.4,0.9,1
50,Male,0.9,0.3,194,190,73,7.5,3.9,1,1
75,Female,0.8,0.2,205,27,24,4.4,2,0.8,1
49,Male,0.7,0.1,148,14,12,5.4,2.8,1,2
49,Female,0.8,0.2,198,23,20,7,4.3,1.5,1
26,Male,0.6,0.1,110,15,20,2.8,1.6,1.3,1
37,Male,1.3,0.4,195,41,38,5.3,2.1,0.6,1
42,Female,0.8,0.2,195,18,15,6.7,3,0.8,1
23,Female,2.3,0.8,509,28,44,6.9,2.9,0.7,2
60,Male,8.9,4,950,33,32,6.8,3.1,0.8,1
42,Female,7.4,3.6,298,52,102,4.6,1.9,0.7,1
58,Male,0.8,0.2,123,56,48,6,3,1,1
75,Male,0.9,0.2,162,25,20,6.9,3.7,1.1,1
16,Male,2.6,1.2,236,131,90,5.4,2.6,0.9,1
32,Male,32.6,14.1,219,95,235,5.8,3.1,1.1,1
16,Male,0.7,0.2,418,28,35,7.2,4.1,1.3,2
66,Female,0.7,0.2,162,24,20,6.4,3.2,1,2
27,Male,1,0.3,180,56,111,6.8,3.9,1.85,2
28,Female,0.6,0.1,137,22,16,4.9,1.9,0.6,2
28,Female,1,0.3,90,18,108,6.8,3.1,0.8,2
70,Male,1.3,0.4,358,19,14,6.1,2.8,0.8,1
49,Male,2,0.6,209,48,32,5.7,3,1.1,2
58,Male,0.4,0.1,100,59,126,4.3,2.5,1.4,1
47,Female,3,1.5,292,64,67,5.6,1.8,0.47,1
72,Male,3.9,2,195,27,59,7.3,2.4,0.4,1
42,Female,0.7,0.2,152,35,81,6.2,3.2,1.06,1
37,Male,0.7,0.2,176,28,34,5.6,2.6,0.8,1
48,Female,1.4,0.8,621,110,176,7.2,3.9,1.1,1
55,Male,75,3.6,332,40,66,6.2,2.5,0.6,1
51,Male,4,2.5,275,382,330,7.5,4,1.1,1
85,Female,1,0.3,208,17,15,7,3.6,1,2
64,Female,0.8,0.2,178,17,18,6.3,3.1,0.9,1
51,Male,0.8,0.2,175,48,22,8.1,4.6,1.3,1
42,Female,2.3,1.1,292,29,39,4.1,1.8,0.7,1
6,Male,0.6,0.1,289,38,30,4.8,2,0.7,2
45,Female,0.7,0.2,170,21,14,5.7,2.5,0.7,1
56,Male,1,0.3,195,22,28,5.8,2.6,0.8,2
38,Male,1.8,0.8,342,168,441,7.6,4.4,1.3,1
51,Male,2.9,1.3,482,22,34,7,2.4,0.5,1
54,Female,22.6,11.4,558,30,37,7.8,3.4,0.8,1
27,Male,1.3,0.6,106,25,54,8.5,4.8,,2
51,Male,2.9,1.2,189,80,125,6.2,3.1,1,1
47,Male,0.9,0.2,265,40,28,8,4,1,1
42,Male,0.8,0.2,114,21,23,7,3,0.7,2
38,Male,0.8,0.2,208,25,50,7.1,3.7,1,1
60,Male,2.6,1.2,171,42,37,5.4,2.7,1,1
45,Male,1.3,0.6,166,49,42,5.6,2.5,0.8,2
62,Male,5,2.1,103,18,40,5,2.1,1.72,1
65,Male,7.9,4.3,282,50,72,6,3,1,1
46,Female,1.4,0.4,298,509,623,3.6,1,0.3,1
45,Male,0.6,0.2,245,22,24,7.1,3.4,0.9,1
62,Male,6.8,3,542,116,66,6.4,3.1,0.9,1
29,Male,1,0.3,75,25,26,5.1,2.9,1.3,1
20,Female,0.6,0.2,202,12,13,6.1,3,0.9,2
37,Male,0.7,0.2,235,96,54,9.5,4.9,1,1
64,Male,3,1.4,248,46,40,6.5,3.2,0.9,1
50,Male,1.1,0.3,175,20,19,7.1,4.5,1.7,2
20,Male,1.1,0.5,128,20,30,3.9,1.9,0.95,2
60,Male,2.4,1,1124,30,54,5.2,1.9,0.5,1
46,Female,14.2,7.8,374,38,77,4.3,2,0.8,1
60,Male,2.3,0.6,272,79,51,6.6,3.5,1.1,1
51,Male,0.8,0.2,160,34,20,6.9,3.7,1.1,1
29,Female,0.7,0.1,162,52,41,5.2,2.5,0.9,2
23,Male,1.1,0.5,191,37,41,7.7,4.3,1.2,2
53,Female,0.8,0.2,193,96,57,6.7,3.6,1.16,1
48,Female,1,1.4,144,18,14,8.3,4.2,1,1
30,Female,0.7,0.2,63,31,27,5.8,3.4,1.4,1
45,Male,1.7,0.8,315,12,38,6.3,2.1,0.5,1
45,Female,1,0.3,250,48,44,8.6,4.3,1,1
64,Male,1.1,0.4,201,18,19,6.9,4.1,1.4,1
26,Male,6.8,3.2,140,37,19,3.6,0.9,0.3,1
38,Male,3.1,1.6,253,80,406,6.8,3.9,1.3,1
60,Male,0.5,0.1,500,20,34,5.9,1.6,0.37,2
60,Male,5.7,2.8,214,412,850,7.3,3.2,0.78,1
74,Female,0.9,0.3,234,16,19,7.9,4,1,1
57,Male,0.7,0.2,208,35,97,5.1,2.1,0.7,1
64,Male,1.4,0.5,298,31,83,7.2,2.6,0.5,1
34,Male,6.2,3,240,1680,850,7.2,4,1.2,1
42,Female,0.8,0.2,182,22,20,7.2,3.9,1.1,1
37,Male,1.8,0.8,215,53,58,6.4,3.8,1.4,1
20,Female,16.7,8.4,200,91,101,6.9,3.5,1.02,1
16,Male,7.7,4.1,268,213,168,7.1,4,1.2,1
39,Male,1.9,0.9,180,42,62,7.4,4.3,1.38,1
39,Male,1.9,0.9,180,42,62,7.4,4.3,1.38,1
24,Male,1,0.2,189,52,31,8,4.8,1.5,1
58,Male,1.7,0.8,188,60,84,5.9,3.5,1.4,2
70,Male,3.1,1.6,198,40,28,5.6,2,0.5,1
48,Female,0.9,0.2,173,26,27,6.2,3.1,1,1
61,Male,0.8,0.2,163,18,19,6.3,2.8,0.8,2
46,Female,4.7,2.2,310,62,90,6.4,2.5,0.6,1
60,Male,5.8,2.7,204,220,400,7,3,0.7,1
66,Male,1,0.3,190,30,54,5.3,2.1,0.6,1
65,Female,0.7,0.2,182,23,28,6.8,2.9,0.7,2
45,Female,0.9,0.3,189,23,33,6.6,3.9,,1
58,Female,0.8,0.2,130,24,25,7,4,1.3,1
27,Male,1.2,0.4,179,63,39,6.1,3.3,1.1,2
25,Female,0.7,0.1,140,32,25,7.6,4.3,1.3,2
40,Female,0.9,0.2,285,32,27,7.7,3.5,0.8,1
18,Male,0.8,0.2,282,72,140,5.5,2.5,0.8,1
18,Male,0.8,0.2,282,72,140,5.5,2.5,0.8,1
48,Male,5,2.6,555,284,190,6.5,3.3,1,1
42,Female,0.9,0.2,165,26,29,8.5,4.4,1,2
50,Female,1.7,0.6,430,28,32,6.8,3.5,1,1
21,Male,1,0.3,142,27,21,6.4,3.5,1.2,2
22,Male,0.9,0.3,179,18,21,6.7,3.7,1.2,2
58,Male,0.9,0.2,1100,25,36,7.1,3.5,0.9,1
72,Male,1.7,0.8,200,28,37,6.2,3,0.93,1
50,Male,7.3,3.6,1580,88,64,5.6,2.3,0.6,2
36,Male,2.8,1.5,305,28,76,5.9,2.5,0.7,1
65,Male,0.7,0.1,392,20,30,5.3,2.8,1.1,1
46,Female,0.8,0.2,182,20,40,6,2.9,0.9,1
48,Male,0.7,0.2,326,29,17,8.7,5.5,1.7,1
46,Female,0.8,0.2,185,24,15,7.9,3.7,0.8,1
75,Male,0.9,0.2,206,44,33,6.2,2.9,0.8,1
55,Female,8.2,3.9,1350,52,65,6.7,2.9,0.7,1
38,Male,0.7,0.2,216,349,105,7,3.5,1,1
50,Male,1.6,0.8,218,18,20,5.9,2.9,0.96,1
46,Male,15.8,7.2,227,67,220,6.9,2.6,0.6,1
75,Male,2.8,1.3,250,23,29,2.7,0.9,0.5,1
30,Male,0.8,0.2,182,46,57,7.8,4.3,1.2,2
60,Male,4,1.9,238,119,350,7.1,3.3,0.8,1
46,Male,0.7,0.2,224,40,23,7.1,3,0.7,1
64,Male,0.9,0.3,310,61,58,7,3.4,0.9,2
84,Female,0.7,0.2,188,13,21,6,3.2,1.1,2
28,Male,0.9,0.2,215,50,28,8,4,1,1

18
script.sh
View File

@ -1,7 +1,19 @@
curl -o liver.data 'https://archive.ics.uci.edu/ml/machine-learning-databases/00225/Indian%20Liver%20Patient%20Dataset%20(ILPD).csv'
sort -R liver.data > liver.data.shuf
head -n 120 liver.data.shuf > liver.data.test
head -n 240 liver.data.shuf | tail -n 120 > liver.data.dev
tail -n +241 liver.data.shuf > liver.data.train
# Check if the CUTOFF variable is set
if [ -z "$CUTOFF" ]; then
head -n 120 liver.data.shuf > liver.data.test
head -n 240 liver.data.shuf | tail -n 120 > liver.data.dev
tail -n +241 liver.data.shuf > liver.data.train
else
cut_train=$(( 60 * CUTOFF / 100 ))
cut_test=$(( 80 * CUTOFF / 100 ))
cut_dev=$(( CUTOFF - cut_train - (cut_test - cut_train) ))
head -n $cut_train liver.data.shuf > liver.data.train
head -n $(( cut_train + cut_dev )) liver.data.shuf | tail -n $cut_dev > liver.data.dev
tail -n +$(( cut_train + cut_dev + 1 )) liver.data.shuf | head -n $(( cut_test - cut_dev )) > liver.data.test
fi
rm liver.data liver.data.shuf