niklas/ki_rwu_java
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Crossvalidation added

Browse Source
This commit is contained in:
Niklas Birk 2019-06-30 23:59:49 +02:00
parent 757eb4dd2b
commit 6937e28cdd
12 changed files with 787 additions and 34 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

316
resources/app1.data Normal file
View File

@ -0,0 +1,316 @@
26,1,0,0,1,0,1,0,1,1,0,37.9,38.8,23100,0,1
17,2,0,0,1,0,1,0,1,1,0,36.9,37.4,8100,0,0
28,1,0,0,1,0,0,0,0,0,0,36.7,36.9,9600,0,1
50,1,0,0,1,0,1,0,1,0,0,38.3,38.8,17600,0,1
62,1,0,0,1,0,0,0,1,1,0,36.9,37.3,10200,0,1
6,1,0,0,1,0,1,0,0,0,0,36.6,36.8,9200,0,0
18,2,0,0,1,0,0,0,0,1,0,37.6,37.9,18700,0,1
13,2,0,0,1,0,0,0,1,0,0,35.7,37.3,11200,0,0
12,2,0,0,1,0,1,1,1,1,1,36.5,38.0,16400,0,1
21,2,0,0,1,0,0,0,0,0,0,36.0,37.4,13300,0,0
13,1,0,0,1,0,0,0,0,0,0,37.2,37.5,7000,0,0
19,1,0,0,1,0,0,0,0,0,0,37.0,37.5,16400,0,1
12,2,0,0,1,1,1,1,1,1,1,39.4,39.9,26800,0,1
10,2,0,0,1,0,0,0,1,0,0,36.1,37.5,8200,0,0
47,1,0,0,1,0,1,0,1,0,0,36.8,37.4,10800,0,1
11,2,1,1,1,1,0,0,1,1,0,37.3,38.1,15000,0,1
14,2,0,0,1,1,1,0,1,1,0,37.1,37.1,12100,0,1
17,2,0,0,1,0,1,0,1,0,0,37.9,36.9,7500,0,1
18,2,0,0,1,1,0,0,1,1,0,35.8,37.5,7200,0,0
21,2,0,0,1,0,0,0,1,0,0,37.0,37.5,10100,0,0
9,2,0,0,1,1,1,0,0,0,0,36.6,37.2,11800,0,1
9,2,1,1,1,0,0,0,0,0,0,37.3,37.9,10200,0,0
60,2,0,0,1,0,0,0,0,0,1,37.6,37.4,5100,0,0
30,2,0,0,1,0,0,0,0,0,0,36.2,36.5,12400,0,0
25,2,0,0,1,0,0,0,0,0,0,37.0,37.7,9700,0,0
11,2,0,0,1,0,1,0,0,0,0,37.0,37.7,8700,0,0
26,2,0,0,1,0,0,0,1,1,0,37.3,37.5,17000,0,1
8,2,1,0,1,0,1,0,0,0,0,36.4,37.5,18800,0,1
24,2,0,0,1,0,1,0,0,0,0,38.6,38.9,7800,0,0
21,1,0,0,1,0,1,0,1,1,1,37.3,37.5,15300,0,1
14,1,1,0,1,0,1,0,1,1,0,36.6,37.1,9400,0,1
15,2,0,0,1,0,0,0,0,0,0,36.4,36.8,8200,0,0
18,2,1,1,0,0,0,0,0,0,0,35.8,36.9,8400,0,0
87,2,1,0,1,1,0,0,1,0,0,36.7,37.2,13100,1,0
25,1,0,0,1,0,0,0,0,0,0,36.6,37.0,10400,0,0
19,2,0,0,1,0,0,0,1,1,1,37.7,37.9,17400,0,1
16,1,0,0,1,0,1,0,1,1,0,37.3,37.9,16900,0,1
19,2,0,0,1,0,1,0,1,1,0,36.2,36.7,11000,0,1
5,2,1,1,1,1,1,1,1,1,1,37.6,38.3,20700,0,1
16,2,0,0,1,0,1,1,1,0,0,37.3,38.3,9200,0,0
15,2,0,0,1,0,0,0,1,0,0,36.9,37.0,7000,0,0
12,1,0,0,1,1,0,0,1,0,0,36.1,37.8,12900,0,1
52,1,0,0,0,0,0,0,0,0,0,36.4,36.8,5600,0,0
23,2,0,0,1,0,1,0,1,1,1,36.8,37.2,1100,0,1
9,2,0,0,1,1,1,0,0,0,0,36.1,37.1,5300,0,0
25,1,0,0,1,0,1,0,0,0,0,37.0,37.5,7800,0,0
35,2,1,1,1,1,1,1,1,1,1,36.9,37.8,23500,0,1
16,2,1,0,1,0,0,1,0,1,0,36.8,37.3,14300,0,1
22,1,0,0,1,0,0,0,1,0,0,37.4,37.1,9700,0,1
16,1,0,0,1,1,0,0,1,1,0,35.6,38.1,15400,0,1
46,1,0,0,1,0,1,0,1,1,1,36.9,37.4,16100,0,1
15,2,0,0,1,0,1,0,1,1,0,36.7,37.1,5500,0,0
18,1,1,0,1,0,1,0,0,1,0,37.0,37.7,9400,0,0
58,2,0,0,1,1,0,0,0,0,0,36.5,37.0,8100,0,0
8,2,0,0,1,0,1,0,1,1,0,38.2,38.8,18000,0,1
12,2,0,0,1,0,0,0,0,0,0,36.4,37.4,5100,0,1
79,2,0,0,1,0,1,0,1,0,0,36.5,38.0,8500,0,1
39,1,0,0,1,0,1,0,1,1,0,37.0,37.7,11400,0,1
10,2,0,0,1,1,0,0,0,0,0,36.5,37.7,13100,0,1
21,2,0,0,1,1,1,0,1,1,0,36.9,37.9,6900,0,0
37,1,0,0,1,0,1,0,1,0,0,36.8,38.0,10800,0,1
15,1,0,0,1,0,0,0,1,1,0,36.1,37.3,9100,0,0
12,2,1,0,1,0,1,0,1,1,0,36.9,37.8,6100,0,1
31,1,0,0,1,0,1,0,1,1,0,36.8,37.8,10700,0,1
17,2,0,0,1,0,0,0,0,0,0,36.8,37.3,8500,0,0
18,2,0,0,1,0,1,0,1,1,0,35.2,37.6,13900,0,1
31,1,0,0,1,0,1,0,0,0,0,38.5,38.9,15600,0,1
12,2,0,0,1,0,0,0,0,0,0,36.4,37.3,6100,0,0
17,2,0,0,1,0,0,0,1,0,1,37.8,38.5,18400,0,0
11,2,0,0,1,0,0,0,0,0,0,36.9,37.8,10600,0,1
22,2,0,0,1,0,1,0,0,0,0,37.7,37.9,10000,0,0
21,1,0,0,1,0,0,0,1,0,0,36.4,37.3,11200,0,1
22,1,0,0,1,0,1,0,1,0,1,36.7,37.9,12800,0,0
51,1,1,1,1,1,1,0,1,1,0,37.4,38.2,15400,0,1
16,2,0,0,1,0,0,0,1,0,0,36.0,37.0,10000,0,1
23,2,0,0,1,0,1,1,1,0,0,36.6,37.5,11200,0,0
25,2,0,0,1,0,0,0,0,1,0,36.8,37.6,14200,0,0
19,1,0,0,1,0,1,0,1,1,0,36.4,38.4,4200,0,0
7,2,0,0,1,0,1,1,1,1,1,37.4,38.4,25600,0,1
49,2,0,0,1,0,0,0,1,1,0,37.2,37.8,11000,0,1
36,1,0,0,1,0,1,0,1,1,1,36.8,36.8,7500,0,1
32,2,0,0,1,0,1,0,1,1,0,37.2,37.7,8100,0,1
21,2,0,0,1,0,0,0,0,0,0,36.4,37.4,10000,0,0
16,2,0,0,1,0,0,0,1,1,0,36.8,37.9,22000,0,1
22,2,0,0,1,0,0,0,0,0,0,36.5,37.0,5200,0,0
10,1,0,0,1,0,1,0,1,1,1,38.6,39.3,20400,0,1
21,2,1,0,1,0,0,0,0,1,0,36.5,37.0,7100,0,0
14,1,0,0,1,0,0,0,0,1,0,36.6,37.0,8500,0,1
88,2,0,0,1,1,0,0,1,1,1,37.6,39.0,9600,1,1
11,2,1,0,1,1,1,0,1,1,0,37.3,38.4,18300,0,1
13,1,0,0,1,0,1,0,1,1,0,36.0,37.5,15200,0,1
15,2,0,0,1,1,1,0,1,1,0,37.1,38.0,12900,0,1
66,1,1,1,1,1,1,1,1,1,1,38.2,39.0,20900,1,1
25,2,0,0,1,0,0,0,0,0,0,36.2,36.8,5700,0,0
12,2,0,0,1,0,0,0,1,0,1,35.7,37.7,8100,0,1
68,1,1,1,1,1,1,0,0,1,0,37.5,38.3,11200,0,1
54,2,0,0,1,0,0,0,0,0,0,37.8,38.9,4200,0,0
18,1,1,1,1,1,1,1,1,1,1,38.0,38.4,23400,0,1
54,1,1,0,1,0,0,0,0,0,0,36.8,37.1,8000,0,0
73,2,0,0,1,0,0,0,1,0,0,36.2,37.6,15400,0,1
29,1,0,0,1,0,1,0,1,1,0,37.8,38.4,13800,0,1
23,2,0,0,1,0,0,0,0,0,0,36.2,37.2,6900,0,0
15,1,0,0,1,0,0,0,0,1,1,36.2,36.8,10700,0,1
42,2,0,0,1,0,1,0,0,1,1,36.0,37.3,19300,0,1
15,2,0,0,1,0,0,0,0,0,0,37.0,37.8,7300,0,0
18,1,0,0,1,0,0,0,1,1,0,36.0,36.2,7100,0,0
11,2,0,0,1,0,1,0,1,1,0,38.1,39.3,12200,0,1
32,2,0,0,1,1,1,1,1,1,1,37.2,38.5,10400,0,0
40,2,0,0,1,0,0,0,0,1,0,37.0,37.3,9900,0,0
46,1,1,0,1,1,1,1,1,1,1,37.3,38.1,14700,0,1
20,2,0,0,1,0,1,0,1,0,0,37.0,37.8,11000,0,0
38,2,0,0,1,0,1,0,1,1,0,36.6,37.8,15200,0,1
14,2,0,0,1,0,0,0,1,0,0,36.9,37.8,11200,0,0
16,2,0,0,1,0,0,0,1,1,0,37.3,37.8,15800,0,1
73,2,1,1,1,1,1,0,0,1,0,36.8,37.2,10700,1,1
16,1,0,0,1,1,0,0,0,1,0,36.4,37.7,11200,0,0
14,2,0,0,1,0,1,0,1,0,0,36.2,36.5,6700,0,0
9,1,1,0,1,0,1,1,1,1,0,36.5,38.1,9000,0,1
28,1,0,0,1,0,1,0,1,1,0,37.2,38.4,14200,0,0
18,2,1,0,1,0,0,0,0,1,0,37.1,37.8,4500,0,0
11,1,0,0,1,0,1,0,1,0,0,36.6,36.8,21800,0,1
32,2,0,0,1,0,0,0,0,0,0,36.2,36.8,10000,0,0
26,1,0,0,1,0,0,0,0,1,0,35.9,37.3,8600,0,0
12,1,0,0,1,0,1,0,1,0,0,37.1,37.6,13300,0,1
18,2,0,0,1,0,1,0,1,1,0,36.5,37.6,6500,0,0
22,1,0,0,1,0,1,0,1,0,0,37.7,37.9,17900,0,1
9,2,0,0,1,0,0,0,1,1,0,37.2,37.0,9300,0,0
34,2,0,1,0,0,0,0,0,1,0,36.6,37.4,7900,0,0
26,2,0,0,1,1,1,0,1,1,1,37.2,38.3,23400,0,1
47,2,0,0,1,0,1,0,1,1,0,37.0,37.8,13000,0,1
56,1,1,1,1,1,1,1,1,1,0,36.8,38.1,20300,0,1
25,2,1,0,1,0,1,0,1,1,0,36.8,37.2,13000,0,1
12,1,0,0,1,0,0,0,1,1,1,36.0,36.7,5800,0,1
73,1,0,0,1,0,1,0,1,1,1,37.4,38.0,17600,0,1
24,1,1,0,1,0,1,0,0,0,0,36.6,37.3,9300,0,0
20,2,1,1,1,0,1,0,1,1,0,36.6,37.1,19900,0,1
13,1,0,0,1,0,0,0,0,0,0,37.1,37.6,7700,0,1
11,2,1,0,1,0,1,0,0,0,0,36.4,37.3,8100,0,0
6,2,0,0,1,1,1,0,1,0,0,38.7,39.4,18100,0,0
17,2,0,0,1,1,1,0,1,1,0,37.2,37.8,15100,0,1
65,2,0,0,1,0,0,0,0,0,0,37.5,38.0,7900,0,0
12,2,0,0,1,0,1,0,0,0,0,36.8,37.2,7800,0,1
10,2,0,0,1,1,1,0,1,1,0,36.9,37.3,9600,0,1
62,1,0,0,1,0,1,0,1,1,1,36.7,37.8,12500,0,1
21,2,0,0,1,0,0,0,1,1,0,37.0,37.6,6900,0,0
11,2,1,0,1,1,1,0,1,0,0,36.5,37.5,7100,0,1
13,2,0,0,1,0,1,0,0,0,0,37.5,37.8,13300,0,1
22,2,0,0,1,0,1,0,1,1,0,37.0,37.8,12100,0,0
17,1,0,0,1,0,1,0,1,0,0,36.8,38.2,17800,0,1
69,2,1,1,1,1,0,1,1,1,1,37.2,37.6,21800,1,0
30,2,0,0,1,1,0,0,0,0,0,36.5,37.0,10900,0,0
17,1,0,0,1,0,1,0,1,0,0,36.3,36.8,17200,0,1
15,2,0,0,1,0,0,0,0,0,0,39.0,39.1,10100,0,1
65,1,1,0,1,0,1,0,0,0,0,35.4,37.1,9200,0,1
19,2,0,0,1,0,0,0,1,0,0,36.6,37.1,11100,0,1
84,2,0,0,1,1,0,0,0,0,0,36.3,36.8,11500,1,0
24,2,0,0,1,1,0,0,0,0,0,36.8,37.2,7900,0,0
13,2,0,0,1,0,0,0,0,0,0,37.0,37.2,7400,0,1
27,2,0,0,1,0,0,0,0,0,0,36.3,36.6,11200,0,1
25,1,0,0,1,0,0,0,0,0,0,36.6,36.8,14600,0,0
22,2,0,0,1,0,1,0,1,0,1,39.6,40.2,13700,0,0
30,2,0,0,1,1,0,0,1,0,0,37.6,37.7,15900,0,1
24,2,0,0,1,0,0,0,0,0,0,36.6,37.4,8900,0,0
6,1,0,0,1,0,1,1,1,1,1,37.5,38.2,25700,0,1
29,2,0,0,1,0,1,0,1,1,0,36.4,37.2,10300,0,1
22,2,0,0,1,0,1,0,0,0,0,37.5,37.7,11700,0,0
44,1,0,0,1,0,1,0,1,0,1,36.2,36.9,6700,0,1
20,2,0,0,1,0,0,0,0,0,0,36.6,37.3,9600,0,0
14,2,1,1,1,0,1,0,1,1,1,37.2,38.0,18000,0,1
23,1,0,0,1,0,0,0,0,0,0,36.5,36.9,9300,0,0
27,1,0,0,1,0,1,0,1,1,1,37.1,37.7,15900,0,1
83,2,0,0,1,0,1,0,1,0,0,37.6,38.1,14500,0,1
14,2,0,0,1,1,0,0,1,1,0,36.0,36.9,19600,0,1
42,2,0,0,1,0,1,0,0,1,0,36.2,38.7,14400,0,1
32,2,0,0,1,1,1,0,1,1,0,37.2,37.5,14900,0,1
12,1,0,0,1,0,1,0,1,1,1,37.2,38.2,19600,0,1
12,1,0,0,1,0,1,1,1,1,0,37.4,38.7,19000,0,1
27,2,0,0,1,0,1,0,1,1,0,36.9,37.9,11400,0,1
13,1,1,0,1,0,1,0,1,0,0,38.0,39.2,11000,0,0
32,1,0,0,1,0,0,0,1,1,0,36.8,37.5,10500,0,1
24,2,0,0,1,0,1,0,1,1,0,36.8,37.5,12700,0,1
30,1,0,0,1,1,1,0,1,1,0,35.8,37.2,8300,0,1
19,2,1,1,1,1,1,1,1,1,0,37.2,38.8,12200,0,1
19,2,0,0,1,0,0,0,0,0,0,36.4,37.2,8200,0,0
34,2,0,0,1,0,0,0,1,1,0,37.3,37.4,11200,0,1
28,1,0,0,1,0,1,0,1,1,0,36.7,37.9,12200,0,1
18,2,0,0,1,0,1,1,1,1,1,37.1,38.4,26900,0,1
28,2,0,0,1,0,0,0,0,1,0,36.5,37.0,6400,0,0
33,2,1,0,1,0,0,0,0,0,0,36.0,36.8,7500,0,1
52,2,0,0,1,0,0,0,1,1,0,37.1,36.3,4800,0,0
8,2,0,1,1,0,0,0,1,1,0,36.3,36.8,22000,0,0
11,1,0,0,1,0,1,0,1,1,0,36.5,37.5,16000,0,1
28,2,0,0,1,0,1,0,0,0,0,36.9,37.6,6700,0,1
15,2,0,0,1,0,1,0,1,0,0,36.6,37.3,8800,0,1
14,1,0,0,1,0,1,0,1,0,0,36.6,37.3,16100,0,1
19,1,0,0,1,0,0,0,0,0,0,36.2,37.2,7100,0,0
9,2,0,0,1,0,0,0,0,0,0,37.6,38.5,14600,0,1
38,2,0,0,1,0,0,0,1,1,0,37.7,38.3,14500,0,1
30,1,0,0,1,0,1,0,1,1,1,37.3,37.7,11300,0,1
24,2,0,0,1,0,0,0,0,0,0,36.6,36.8,11100,0,0
34,2,0,0,1,0,0,0,0,0,0,36.3,36.9,8500,0,0
22,2,0,0,1,0,0,0,1,1,0,36.8,37.5,8300,0,0
40,2,0,0,1,0,0,0,0,0,0,37.4,38.6,9000,0,0
25,1,0,0,1,0,0,0,0,0,0,36.7,37.1,8200,0,0
7,1,0,0,1,0,1,0,1,1,1,37.7,38.0,18900,0,1
13,2,0,0,1,0,1,0,0,0,0,38.9,39.3,7900,0,0
17,2,0,0,1,0,1,0,0,1,0,37.5,37.7,13200,0,1
20,2,1,1,1,1,1,1,1,1,1,37.7,36.9,12300,0,1
10,1,0,0,1,0,0,0,0,0,0,35.5,36.1,9000,0,1
32,1,0,0,1,0,1,0,1,0,0,36.1,37.2,9000,0,0
15,1,0,0,1,0,1,0,1,1,1,37.2,37.6,15300,0,1
72,1,0,0,1,1,1,0,1,1,0,36.3,37.6,12000,0,1
15,1,0,0,1,0,0,0,1,1,1,37.2,37.6,16400,0,1
17,1,0,0,1,0,1,0,1,1,1,36.8,37.9,13000,0,1
22,2,0,0,1,0,1,0,1,1,0,36.8,37.0,7400,0,1
23,1,0,0,1,0,1,0,1,1,0,37.0,37.9,12100,0,1
26,1,0,0,1,0,0,0,0,0,0,36.9,37.0,5200,0,0
16,1,1,0,1,0,1,0,1,1,0,36.5,37.2,6900,0,0
21,1,0,0,1,0,1,0,1,0,0,37.9,38.5,18800,0,1
17,1,0,0,1,0,1,0,1,1,1,38.3,38.5,11800,0,1
8,2,0,0,1,0,1,0,1,1,0,36.2,37.2,18100,0,1
12,2,0,0,1,0,1,0,1,1,0,37.1,37.6,6700,0,0
17,2,0,0,1,0,0,0,1,1,0,36.5,37.3,9000,0,0
13,2,0,0,1,0,0,0,1,1,0,36.6,37.0,9200,0,0
20,1,0,0,1,0,1,0,1,0,0,38.0,38.6,11300,0,0
17,1,0,0,1,0,0,0,1,0,0,37.8,38.2,15300,0,1
24,2,0,0,1,1,1,0,1,1,0,37.4,38.3,11700,0,0
33,2,1,1,0,0,0,0,0,1,0,37.2,37.8,5000,0,0
34,2,0,0,1,1,1,0,1,1,0,36.2,36.8,8800,0,0
24,1,0,0,1,0,1,0,1,0,0,37.6,38.1,13600,0,1
24,1,0,0,1,0,1,0,1,1,0,36.9,37.5,13100,0,1
22,1,0,0,1,0,0,0,0,0,0,36.4,36.8,6100,0,0
10,1,0,0,1,0,1,0,0,1,0,37.8,38.5,11900,0,1
22,2,0,0,1,0,1,0,1,1,1,36.5,37.6,13600,0,1
13,1,0,0,1,0,0,0,0,0,0,36.8,37.4,8600,0,1
19,1,0,0,1,0,1,0,0,1,0,37.0,37.5,11900,0,1
16,2,0,0,1,0,1,0,0,0,0,36.8,37.4,15200,0,1
33,1,0,0,1,0,1,0,1,1,1,36.4,38.0,14000,0,1
24,1,1,1,1,1,1,1,1,1,1,39.2,39.7,13300,0,1
18,1,0,0,1,0,0,0,1,0,0,36.9,37.7,10800,0,1
22,2,0,0,1,0,0,0,0,0,0,36.4,37.0,14200,0,1
28,2,0,0,1,0,0,0,0,0,0,37.4,37.4,11200,0,1
38,1,0,0,1,1,0,1,1,1,1,38.6,37.4,19000,0,1
17,2,0,0,1,0,1,0,0,0,0,36.8,37.9,18100,0,1
23,1,0,0,1,0,1,0,1,1,1,37.2,37.7,12900,0,1
16,1,0,0,1,0,1,0,1,1,1,36.8,37.2,19000,0,1
23,1,0,0,1,1,1,0,1,1,0,39.0,39.4,20300,0,1
18,2,0,0,1,0,0,0,0,0,0,36.5,36.8,8100,0,0
23,2,0,0,1,0,1,0,1,1,0,37.3,37.5,12700,0,1
23,2,1,0,1,0,1,0,1,1,0,37.0,37.4,4500,0,0
38,1,0,0,1,0,1,0,1,1,1,36.7,37.8,8700,0,1
14,2,0,0,1,0,1,0,1,0,1,37.0,37.5,7800,0,1
40,1,1,1,1,1,1,0,1,1,1,37.3,38.2,12000,0,0
64,2,0,0,1,1,1,0,0,1,0,36.8,37.7,7200,0,1
5,2,0,0,1,0,1,0,1,0,0,37.2,37.8,13500,0,1
10,1,0,0,1,0,0,0,0,0,0,36.0,37.1,9900,0,0
12,2,0,0,1,0,0,0,0,0,0,37.2,37.6,6300,0,0
24,2,0,0,1,0,1,0,1,0,0,37.7,38.8,12300,0,0
16,1,0,0,1,0,1,0,1,1,0,36.8,37.4,8200,0,1
15,2,0,0,1,0,0,0,0,0,0,36.1,36.9,6600,0,0
12,2,1,0,1,1,1,0,1,1,0,36.0,37.4,9600,0,1
36,2,0,0,1,0,1,0,1,0,0,36.4,37.0,7900,0,0
82,2,0,0,1,1,1,0,1,1,0,36.9,38.2,20900,1,1
11,2,0,1,0,0,0,0,0,0,0,36.2,37.3,9100,0,0
18,2,0,0,1,0,0,0,1,0,0,36.8,35.6,5200,0,0
24,2,0,0,1,0,1,0,1,0,0,36.0,37.1,11800,0,1
19,2,0,0,1,1,0,0,1,0,0,36.1,37.3,11100,0,1
10,1,1,1,1,1,1,0,1,0,0,37.7,38.4,15000,0,0
18,2,0,0,1,0,0,0,0,0,0,36.7,36.9,8900,0,0
31,2,0,0,1,0,1,0,0,0,0,37.6,38.4,13600,0,1
11,2,1,0,1,0,1,0,1,1,0,36.4,36.8,6100,0,1
19,1,0,0,1,0,1,0,1,1,1,36.5,37.2,8600,0,1
37,2,0,0,1,0,1,0,1,1,0,37.5,38.5,11100,0,1
36,2,0,0,1,1,0,0,0,0,0,36.7,36.8,5800,0,0
39,2,0,0,1,0,1,0,0,0,0,36.8,37.4,10900,0,0
10,2,0,0,1,0,0,0,1,0,0,36.0,36.9,5600,0,0
26,2,0,0,1,0,0,0,0,0,0,36.4,37.0,5200,0,1
36,2,1,0,1,0,1,0,0,1,0,37.2,37.7,8500,0,1
33,1,0,0,1,0,1,0,1,1,1,37.9,38.2,18400,0,1
15,2,0,0,1,0,0,0,0,0,0,37.0,37.0,7700,0,0
74,2,1,0,1,0,1,0,1,0,0,36.3,35.3,8100,0,0
25,2,0,0,1,1,0,0,0,0,0,36.3,36.7,4500,0,0
25,2,0,0,1,1,1,0,1,0,0,38.5,38.7,11600,0,1
17,1,0,0,1,0,1,0,1,0,0,38.0,38.5,20400,0,1
33,1,0,0,1,1,1,0,0,0,0,37.4,37.8,8800,0,0
22,2,0,0,1,0,0,0,1,0,0,36.7,37.9,20000,0,1
13,1,1,0,1,0,1,0,0,1,0,36.4,36.6,7500,0,0
33,1,0,0,1,0,0,0,0,1,0,37.0,36.6,6700,0,0
22,2,0,0,1,0,1,0,0,0,0,37.1,38.1,13000,0,0
25,2,0,0,1,0,0,0,0,0,0,36.3,36.8,19200,0,0
19,2,0,0,1,0,0,0,1,0,1,37.0,38.1,11000,0,0
26,1,0,0,1,1,1,0,0,0,0,35.9,36.9,12000,0,1
14,1,0,0,1,0,1,0,1,1,0,36.3,36.9,9400,0,0
19,2,0,0,1,1,0,0,0,0,0,35.9,37.0,6600,0,0
6,2,1,1,1,0,1,0,1,1,1,38.5,39.0,13000,0,1
21,2,0,0,1,0,0,0,1,1,0,36.8,37.7,14000,0,0
21,1,0,0,1,0,0,0,0,0,1,36.6,36.6,5100,0,0
24,2,0,0,1,0,1,0,0,0,0,36.3,36.9,17700,0,0
16,1,0,0,1,0,0,0,0,0,0,35.8,36.4,9000,0,0
18,2,0,0,1,0,1,0,0,0,0,36.7,37.2,10300,0,0
17,2,0,0,1,0,0,0,0,1,0,36.8,37.2,9600,0,0
13,2,0,0,1,0,1,0,1,1,0,36.4,36.8,6300,0,0
15,2,0,0,1,0,1,0,0,0,0,37.0,38.0,12000,0,0
41,2,0,0,1,0,0,0,0,0,0,36.8,36.9,4900,0,0
27,1,0,0,1,0,1,0,1,0,0,36.9,37.6,11000,0,1
11,2,0,0,1,1,0,0,0,0,0,36.4,37.3,13200,0,1
33,2,0,0,1,0,0,0,0,0,0,37.2,37.7,25000,0,1
35,2,0,0,1,0,0,0,1,1,1,37.6,38.0,11700,0,1
20,1,0,0,1,0,0,0,1,0,0,36.3,36.7,5300,0,0
46,1,0,0,1,1,0,0,0,1,0,36.8,36.8,7500,0,0
9,1,0,0,1,0,0,0,1,1,0,36.1,37.0,3000,0,0
45,1,0,0,1,0,1,0,1,1,0,36.6,37.4,14400,0,1
22,2,0,0,1,0,0,0,0,0,0,36.7,37.4,14800,0,0
31,2,1,0,1,1,1,0,1,1,1,36.0,36.8,9100,0,1
37,1,0,0,1,0,1,0,0,0,0,37.4,37.8,25200,0,1
7,1,0,0,1,0,1,0,1,1,0,36.4,38.0,20400,0,1

157
resources/app1.test Normal file
View File

@ -0,0 +1,157 @@
19,2,0,0,1,1,0,0,0,0,0,36.6,37.4,5700,0,0
37,2,0,0,1,0,0,0,0,0,0,36.1,36.9,6000,0,0
10,1,0,0,1,0,1,0,1,1,0,37.2,37.9,17500,0,1
12,1,0,0,1,0,0,0,0,0,0,37.6,37.8,11900,0,1
31,1,0,0,1,0,0,0,0,0,0,36.9,36.5,8400,0,0
19,2,0,0,1,0,1,0,1,1,1,36.4,37.0,7800,0,0
67,1,0,0,1,0,1,0,1,0,0,37.1,37.5,10400,1,1
19,1,1,0,1,0,0,0,0,1,0,37.0,37.5,16000,0,1
28,1,0,0,1,0,1,0,1,1,1,36.3,37.2,12100,0,1
16,2,0,0,1,0,0,0,0,0,0,36.8,37.0,10700,0,1
26,2,0,0,1,0,0,0,0,0,0,36.5,37.0,7300,0,0
11,2,1,0,1,0,1,1,1,1,0,37.9,38.3,23100,0,1
80,2,0,0,1,0,1,1,1,1,0,37.0,38.1,6500,0,1
23,2,0,0,1,0,0,0,0,0,0,36.5,37.0,8600,0,0
54,2,0,0,1,0,0,0,0,0,0,38.0,38.8,4200,0,1
13,1,0,0,1,1,0,0,1,1,0,37.4,36.3,5800,0,1
14,2,0,0,1,0,0,0,0,0,0,36.4,37.3,7900,0,0
11,2,0,0,1,0,1,0,0,0,0,36.0,38.2,14800,0,0
23,1,0,0,1,0,1,0,1,1,0,36.4,37.4,16100,0,1
14,2,0,0,1,0,0,1,1,1,0,36.1,37.5,10700,0,1
33,2,1,0,1,0,1,0,0,0,0,35.5,37.1,23000,0,1
46,2,1,0,1,0,1,0,1,1,0,36.0,37.1,15000,0,1
31,2,0,0,1,0,0,0,0,0,0,36.8,37.2,7800,0,0
17,1,0,0,1,0,1,0,0,0,0,37.0,37.8,8000,0,1
79,2,1,0,1,0,0,0,0,0,1,36.4,37.2,13900,0,1
24,2,0,0,1,0,1,0,0,0,0,37.1,37.7,7600,0,0
33,1,0,0,1,0,1,0,0,0,0,37.0,37.8,10900,0,0
11,2,1,0,1,1,1,0,1,1,1,38.0,38.5,17000,0,1
25,2,0,0,1,0,0,0,1,0,0,36.4,37.0,8800,0,1
15,2,0,0,1,0,1,0,0,0,0,37.3,37.8,7600,0,0
26,2,1,1,1,1,1,0,1,1,0,36.8,37.1,10400,0,1
10,2,0,0,1,0,0,0,1,1,0,38.4,39.1,4500,0,0
11,1,0,0,1,1,0,0,0,0,0,37.3,35.9,10100,0,0
16,2,0,0,1,0,1,0,1,1,0,37.1,37.7,22000,0,1
24,2,0,0,1,1,1,0,1,1,1,37.5,37.6,10600,0,1
15,2,0,0,1,0,1,0,1,0,1,36.3,37.0,13800,0,0
12,2,0,0,1,0,1,0,0,0,0,37.2,37.8,15000,0,1
15,2,0,0,1,0,0,0,1,1,1,36.6,37.3,7500,0,1
58,1,1,0,1,1,1,1,1,1,1,37.0,37.7,7600,0,1
58,2,0,0,1,1,1,0,1,0,0,39.5,40.0,12000,0,1
15,2,0,0,1,0,1,1,1,1,0,37.1,37.8,11300,0,0
13,1,0,0,1,0,1,0,1,0,0,36.8,37.4,6300,0,1
61,2,0,0,1,0,0,1,0,1,0,36.8,37.7,21000,0,0
40,1,1,1,1,1,1,0,1,1,0,38.2,39.0,17900,0,0
13,1,0,0,1,0,1,0,1,1,1,37.9,37.0,8900,0,1
19,1,0,0,1,0,1,0,0,0,1,36.3,38.2,11600,0,0
31,2,0,0,1,1,1,0,0,0,0,37.0,37.2,8000,0,1
76,2,1,0,1,0,1,0,1,0,0,36.4,37.8,7900,0,1
11,2,0,0,1,0,1,0,0,0,0,36.4,37.2,17300,0,1
27,2,0,0,1,0,0,0,0,0,0,36.4,36.8,7600,0,0
21,2,0,0,1,0,0,0,0,0,0,36.6,36.6,5500,0,1
20,1,0,0,1,0,0,0,1,1,0,37.0,37.9,14000,0,1
67,2,0,0,1,0,1,0,1,1,0,38.7,39.0,12300,0,1
29,2,1,0,1,0,0,0,0,0,0,35.7,37.1,11800,0,1
23,2,0,0,1,0,1,1,0,0,0,37.0,37.2,4000,0,0
18,1,0,0,1,0,0,0,0,0,0,37.1,37.4,16500,0,1
11,1,0,0,1,1,1,0,1,1,0,37.6,37.0,10200,0,1
37,2,0,0,1,1,0,0,0,0,0,36.1,36.7,14200,0,0
27,1,0,0,1,0,0,0,1,1,0,36.4,37.4,12400,0,1
22,1,0,0,1,0,1,0,0,0,0,36.2,37.2,28100,0,1
13,1,1,0,1,0,0,0,1,1,0,36.0,36.3,5200,0,0
25,2,0,0,1,0,0,0,0,0,0,37.3,37.1,7800,0,0
54,2,0,0,1,0,0,0,0,0,0,36.2,36.9,7800,0,0
24,2,0,0,1,1,1,0,0,0,0,37.0,37.0,9100,0,0
32,2,0,0,1,0,0,0,0,0,0,36.6,37.8,14700,0,1
17,2,0,0,1,0,1,0,1,1,0,36.0,36.6,6400,0,1
31,1,0,0,1,0,1,0,1,1,0,37.1,37.9,10000,0,1
18,2,0,0,1,0,0,0,0,0,0,36.5,36.8,11700,0,0
22,1,0,0,1,1,0,0,0,0,0,36.8,37.2,8000,0,0
35,1,0,0,1,0,1,0,1,1,0,37.5,38.2,10400,0,1
11,2,0,0,1,0,1,0,1,1,0,36.6,37.2,15100,0,1
12,2,1,0,1,0,1,0,1,1,0,37.1,36.4,11900,0,1
44,2,0,0,1,0,1,0,0,0,0,37.2,37.8,8300,0,0
35,2,0,0,1,0,1,0,1,1,0,36.4,37.3,19100,0,1
49,1,0,0,1,0,0,0,0,0,0,36.2,36.8,13800,0,1
27,1,1,1,1,0,0,0,1,1,0,36.9,37.8,6600,0,0
27,2,0,0,1,0,1,0,0,0,1,36.8,37.4,9200,0,0
10,1,0,0,1,0,1,0,0,0,0,36.1,37.7,9700,0,0
30,1,0,0,1,0,1,0,1,0,0,36.8,37.6,13600,0,1
40,1,1,0,1,0,0,0,0,0,0,36.6,36.8,8700,0,1
10,2,0,0,1,0,0,0,0,1,0,37.6,36.2,13900,0,1
15,2,0,0,1,0,0,0,0,0,0,36.8,37.5,6100,0,1
12,2,0,0,1,0,0,0,1,0,0,36.2,37.1,13700,0,1
16,1,0,0,1,0,1,1,1,1,1,36.8,37.8,18000,0,1
10,1,0,0,1,0,0,0,1,1,0,36.6,36.7,6000,0,1
8,2,0,0,1,0,1,0,1,1,0,36.8,37.6,10000,0,0
21,1,0,0,1,0,0,0,0,0,0,36.3,36.8,7800,0,0
12,1,0,0,1,0,1,0,1,1,1,36.0,37.1,21400,0,1
14,1,0,0,1,1,1,0,1,1,0,37.9,40.0,20100,0,1
13,2,0,0,1,0,0,0,0,0,1,37.8,38.4,11000,0,0
30,1,0,0,1,0,1,0,1,1,0,37.1,37.6,17000,0,1
41,2,0,0,0,0,0,0,0,0,0,36.5,37.0,7900,0,0
92,2,1,0,1,0,1,1,0,1,0,37.9,38.6,16200,0,0
14,2,0,0,1,0,0,0,1,1,1,36.8,37.0,6800,0,0
19,2,0,0,1,0,1,0,0,0,0,37.0,37.6,18700,0,1
15,2,0,0,1,0,1,0,0,0,0,36.6,37.3,7000,0,0
11,2,0,0,1,1,1,0,1,1,0,37.0,39.0,13000,0,1
10,1,0,0,1,0,1,1,1,1,0,37.3,38.2,18100,0,1
20,1,0,0,1,0,0,0,1,1,0,37.0,38.0,17600,0,1
9,2,0,0,1,0,1,0,0,0,1,36.6,36.9,18600,0,1
68,2,0,0,1,1,0,0,0,0,0,36.2,36.8,6100,0,0
13,2,1,0,1,0,0,0,0,0,0,37.5,37.8,8300,0,0
21,2,0,0,1,0,0,0,0,0,0,36.6,37.3,14800,0,0
12,1,1,1,1,1,1,0,0,1,0,36.6,37.3,12000,0,1
33,2,0,0,1,0,0,0,0,0,1,36.4,37.2,9200,0,0
36,2,0,0,1,0,1,0,1,1,0,36.8,37.4,5500,0,0
7,1,0,0,1,0,1,0,1,1,0,37.2,38.1,22500,0,1
18,2,0,0,1,1,1,0,0,1,0,37.0,37.5,9000,0,1
25,1,0,0,1,0,1,0,1,1,0,36.8,37.5,12900,0,1
12,1,0,0,1,0,0,0,1,1,0,37.0,37.9,14900,0,1
17,2,0,0,1,0,0,0,0,1,0,37.6,37.1,16700,0,1
8,1,0,0,1,0,1,0,1,1,1,37.6,38.2,16800,0,1
6,2,1,1,1,1,1,1,1,1,0,37.8,38.7,13700,0,0
10,2,0,0,1,0,1,0,1,1,0,37.2,37.9,7700,0,0
20,2,0,0,1,0,0,0,1,1,0,37.0,37.6,12500,0,1
13,2,1,1,1,1,0,0,1,1,0,37.2,38.0,13100,0,0
45,1,0,0,1,0,1,0,1,1,0,37.5,38.3,18900,0,0
13,2,0,0,1,0,0,0,0,0,0,37.4,37.5,8200,0,0
17,2,0,0,1,0,0,0,1,0,0,36.7,37.4,9000,0,0
11,2,0,0,1,0,0,0,0,0,0,36.8,37.2,5200,0,1
42,1,0,0,1,0,1,0,0,0,1,36.5,37.0,20800,0,1
23,2,0,0,1,0,0,0,1,1,0,37.2,37.9,12900,0,1
58,1,0,0,1,0,1,0,1,0,1,37.1,37.7,14700,0,0
9,1,0,0,1,0,1,0,1,1,0,37.2,38.2,12000,0,1
18,2,0,0,1,0,0,0,0,0,0,36.8,38.4,12000,0,0
75,1,0,0,1,0,1,0,1,0,0,37.9,38.6,14900,0,1
17,2,0,0,1,0,0,0,1,1,0,37.1,37.7,22300,0,1
9,2,0,0,1,0,1,0,1,1,0,37.4,37.4,16000,0,1
17,2,0,0,1,0,1,0,0,0,0,37.2,38.2,16000,0,0
64,2,0,0,1,0,1,0,1,0,0,37.8,37.9,12600,0,1
37,2,0,0,1,0,0,0,0,0,0,37.0,37.5,7800,0,0
16,2,0,0,1,0,1,0,0,0,1,36.8,37.5,3600,1,0
37,2,0,0,1,1,1,0,1,0,1,37.4,38.0,12400,0,0
19,1,0,0,1,0,0,0,0,0,1,36.0,36.5,8900,0,0
10,1,1,1,1,1,0,0,0,1,0,36.6,37.0,5600,0,0
39,1,0,0,1,0,1,0,1,1,1,36.8,38.1,15200,0,1
29,2,0,1,1,1,0,0,0,0,0,36.5,36.9,6600,0,0
24,2,0,0,1,0,1,0,1,1,1,37.0,38.0,8900,0,1
45,2,0,0,1,0,0,0,0,0,0,36.5,37.0,5300,0,0
20,2,0,0,1,0,1,0,1,0,0,37.5,38.2,13200,0,1
10,1,0,0,1,0,1,0,1,0,0,36.8,37.5,23300,0,1
14,2,0,0,1,0,0,0,0,0,0,37.3,37.9,6900,0,0
62,1,0,0,1,0,1,0,0,0,0,36.7,37.5,8500,0,1
9,2,0,0,1,0,1,0,1,1,1,37.8,38.0,19300,0,1
12,1,0,0,1,0,0,0,1,1,0,36.4,36.8,13700,0,0
29,1,1,1,1,0,1,0,1,0,0,35.6,36.7,8200,0,1
57,2,1,0,1,0,0,0,0,0,0,36.7,37.7,12500,0,0
82,2,1,0,0,0,0,0,0,0,0,37.2,37.5,8600,0,0
13,2,0,0,1,0,1,0,1,0,0,36.4,36.7,10300,0,1
56,1,1,0,1,0,1,0,1,0,0,37.3,37.9,12600,0,1
23,2,0,0,1,1,0,0,1,1,0,36.5,37.5,11400,0,1
31,1,0,0,1,0,0,0,0,0,0,35.2,37.0,14400,0,0
9,2,0,0,1,1,0,0,0,0,0,36.5,37.0,7600,0,1
16,2,0,0,1,0,1,0,1,1,0,36.8,37.6,23300,0,1
16,2,0,0,1,0,0,0,0,0,0,36.8,37.3,22100,0,1
32,2,1,0,1,1,1,0,1,1,0,36.6,37.6,12800,0,1
34,1,1,1,1,0,1,0,1,1,0,36.6,37.5,12200,0,1

12
src/machine_learning/DataClass.java Normal file
View File

@ -0,0 +1,12 @@
package machine_learning;
public enum DataClass
{
NEGATIVE,
POSITIVE;
public static DataClass valueOf(int i)
{
return i == 0 ? NEGATIVE : POSITIVE;
}
}

9
src/machine_learning/MachineLearning.java Normal file
View File

@ -0,0 +1,9 @@
package machine_learning;
import java.util.List;
public interface MachineLearning
{
void learn(List<Vector> positives, List<Vector> negatives);
DataClass classify(Vector toClassify);
}

10
src/machine_learning/Vector.java
View File

@ -95,6 +95,16 @@ public class Vector
return this.values.get(index);
}
public Vector decreasedDimension()
{
return new Vector(this.values.subList(0, this.dimension()-1));
}
public Vector normalized()
{
return this.divide(this.euclid());
}
@Override
public boolean equals(Object o)
{

106
src/machine_learning/nearest_neighbour/CrossValidation.java Normal file
View File

@ -0,0 +1,106 @@
package machine_learning.nearest_neighbour;
import machine_learning.DataClass;
import machine_learning.MachineLearning;
import machine_learning.Vector;
import java.util.*;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.concurrent.atomic.AtomicReference;
import java.util.stream.Collectors;
import java.util.stream.IntStream;
public class CrossValidation
{
private int paramMin;
private int paramMax;
public CrossValidation(int paramMin, int paramMax)
{
this.paramMin = paramMin;
this.paramMax = paramMax;
}
public KNearestNeighbour validate(List<Vector> data, int chunkSize)
{
Collections.shuffle(data);
var counter = new AtomicInteger(0);
var chunks = data.stream()
.collect(Collectors.groupingBy(v -> counter.getAndIncrement() / (data.size() / chunkSize)))
.values();
var averageFailRates = new HashMap<Double, Integer>();
IntStream.range(paramMin, paramMax).forEach(i -> {
var failRate = new AtomicReference<>(0d);
chunks.forEach(chunk -> {
var dataWithoutChunk = data.parallelStream()
.filter(v -> !chunk.contains(v))
.collect(Collectors.toList());
var mapOfClasses = splitIntoClasses(dataWithoutChunk);
var negatives = mapOfClasses.get(DataClass.NEGATIVE);
var positives = mapOfClasses.get(DataClass.POSITIVE);
var kNearestNeighbour = new KNearestNeighbour(Vector::distance, i);
kNearestNeighbour.learn(positives, negatives);
var failCount = 0;
for (var vector : chunk)
{
var expectedClass = DataClass.valueOf(Double.valueOf(vector.get(vector.dimension() - 1)).intValue());
var testVector = vector.decreasedDimension().normalized();
var actualClass = kNearestNeighbour.classify(testVector);
if (expectedClass != actualClass)
{
failCount++;
}
}
failRate.set(failRate.get() + failCount * 1d / chunk.size());
});
averageFailRates.put(failRate.get() / chunkSize, i);
});
var optimalParam = averageFailRates.get(averageFailRates.keySet().stream().min(Double::compareTo).get());
var finalKNearestNeighbour = new KNearestNeighbour(Vector::distance, optimalParam);
System.out.println("Optimaler Parameter k = " + optimalParam + " mit Fehlerrate " + averageFailRates.keySet().stream().min(Double::compareTo).get()*100 + " %");
var classes = splitIntoClasses(data);
var negatives = classes.get(DataClass.NEGATIVE);
var positives = classes.get(DataClass.POSITIVE);
finalKNearestNeighbour.learn(positives, negatives);
return finalKNearestNeighbour;
}
private Map<DataClass, List<Vector>> splitIntoClasses(List<Vector> data)
{
var positives = data.parallelStream()
.filter(v -> v.get(v.dimension()-1) == 1)
.collect(Collectors.toList());
var negatives = data.parallelStream()
.filter(v -> v.get(v.dimension()-1) == 0)
.collect(Collectors.toList());
positives = positives.parallelStream()
.map(Vector::decreasedDimension)
.map(Vector::normalized)
.collect(Collectors.toList());
negatives = negatives.parallelStream()
.map(Vector::decreasedDimension)
.map(Vector::normalized)
.collect(Collectors.toList());
return Map.ofEntries(Map.entry(DataClass.NEGATIVE, negatives), Map.entry(DataClass.POSITIVE, positives));
}
}

7
src/machine_learning/nearest_neighbour/DataClass.java
View File

@ -1,7 +0,0 @@
package machine_learning.nearest_neighbour;
public enum DataClass
{
POSITIVE,
NEGATIVE
}

28
src/machine_learning/nearest_neighbour/KNearestNeighbour.java
View File

@ -1,5 +1,7 @@
package machine_learning.nearest_neighbour;
import machine_learning.DataClass;
import machine_learning.MachineLearning;
import machine_learning.Vector;
import java.util.List;
@ -8,8 +10,11 @@ import java.util.Random;
import java.util.stream.Collectors;
import java.util.stream.Stream;
public class KNearestNeighbour
public class KNearestNeighbour implements MachineLearning
{
private List<Vector> positives;
private List<Vector> negatives;
private Distance distance;
private int k;
@ -25,20 +30,26 @@ public class KNearestNeighbour
this.k = k;
}
public DataClass kNearestNeighbour(List<Vector> positives, List<Vector> negatives, Vector toClassify)
public void learn(List<Vector> positives, List<Vector> negatives)
{
this.positives = positives;
this.negatives = negatives;
}
public DataClass classify(Vector toClassify)
{
var nearestNeighbours = this.nearestNeighbours(
Stream.concat(positives.stream(), negatives.stream())
Stream.concat(this.positives.stream(), this.negatives.stream())
.collect(Collectors.toList()),
toClassify
);
var positivesWithNearestNeighboursAmount = nearestNeighbours.stream()
.filter(positives::contains)
.filter(this.positives::contains)
.count();
var negativesWithNearestNeighboursAmount = nearestNeighbours.stream()
.filter(negatives::contains)
.filter(this.negatives::contains)
.count();
if (positivesWithNearestNeighboursAmount > negativesWithNearestNeighboursAmount)
@ -55,13 +66,12 @@ public class KNearestNeighbour
private List<Vector> nearestNeighbours(List<Vector> vectors, Vector vector)
{
var nearestNeighbours = vectors.stream()
return vectors.parallelStream()
.map(v -> Map.entry(this.distance.distance(v, vector), v))
.sorted((e1, e2) -> e1.getKey() >= e2.getKey() ? (e1.getKey().equals(e2.getKey()) ? 0 : 1) : -1)
.map(Map.Entry::getValue)
.collect(Collectors.toList());
return nearestNeighbours.subList(0, this.k);
.collect(Collectors.toList())
.subList(0, this.k);
}
}

5
src/machine_learning/perceptron/Perceptron.java
View File

@ -1,11 +1,12 @@
package machine_learning.perceptron;
import machine_learning.MachineLearning;
import machine_learning.Vector;
import machine_learning.nearest_neighbour.DataClass;
import machine_learning.DataClass;
import java.util.List;
public class Perceptron
public class Perceptron implements MachineLearning
{
private Vector weight;

49
test/machine_learning/VectorTest.java
View File

@ -24,10 +24,10 @@ class VectorTest
var v1 = new Vector(1d, 2d);
var v2 = new Vector(3d, 4d);
var result = v1.add(v2);
var actual = v1.add(v2);
var expected = new Vector(4d, 6d);
assertEquals(expected, result);
assertEquals(expected, actual);
}
@Test
@ -36,10 +36,10 @@ class VectorTest
var v1 = new Vector(1d, 2d);
var v2 = new Vector(3d, 4d);
var result = v1.subtract(v2);
var actual = v1.subtract(v2);
var expected = new Vector(-2d, -2d);
assertEquals(expected, result);
assertEquals(expected, actual);
}
@Test
@ -48,10 +48,10 @@ class VectorTest
var v1 = new Vector(1d, 2d);
var v2 = new Vector(3d, 4d);
var result = v1.scalar(v2);
var actual = v1.scalar(v2);
var expected = 11d;
assertEquals(expected, result);
assertEquals(expected, actual);
}
@Test
@ -59,10 +59,10 @@ class VectorTest
{
var v1 = new Vector(1d, 2d);
var result = v1.euclid();
var actual = v1.euclid();
var expected = Math.sqrt(5);
assertEquals(expected, result);
assertEquals(expected, actual);
}
@Test
@ -71,10 +71,10 @@ class VectorTest
var v1 = new Vector(1d, 2d);
var v2 = new Vector(3d, 4d);
var result = v1.distance(v2);
var actual = v1.distance(v2);
var expected = Math.sqrt(8);
assertEquals(expected, result);
assertEquals(expected, actual);
}
@Test
@ -83,9 +83,34 @@ class VectorTest
var v1 = new Vector(1d, 2d);
var div = 2d;
var result = v1.divide(div);
var actual = v1.divide(div);
var expected = new Vector(0.5d, 1d);
assertEquals(expected, result);
assertEquals(expected, actual);
}
@Test
void shouldDecreaseDimensionCorrect()
{
var v = new Vector(1d, 2d, 3d, 4d);
var decreasedDimensionVector = v.decreasedDimension();
var actual = decreasedDimensionVector.dimension();
var expected = 3;
assertEquals(expected, actual);
}
@Test
void shouldNormalizeCorrect()
{
var v = new Vector(4d, 4d, 4d, 4d);
var actual = v.normalized();
var expected = new Vector(0.5d, 0.5d, 0.5d, 0.5d);
assertEquals(expected, actual);
}
}

119
test/machine_learning/nearest_neighbour/KNearestNeighbourTest.java
View File

@ -1,13 +1,20 @@
package machine_learning.nearest_neighbour;
import machine_learning.DataClass;
import machine_learning.Vector;
import org.junit.jupiter.api.BeforeAll;
import org.junit.jupiter.api.Test;
import org.junit.jupiter.api.TestInstance;
import org.opentest4j.AssertionFailedError;
import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;
import java.util.stream.Stream;
import static org.junit.jupiter.api.Assertions.*;
@ -40,9 +47,10 @@ class KNearestNeighbourTest
public void shouldReturnCorrectClassForVectorWithKEquals3()
{
var kNearestNeighbour = new KNearestNeighbour((a ,b) -> Math.abs(a.get(0) - b.get(0)) + Math.abs(a.get(1) - b.get(1)), 3);
kNearestNeighbour.learn(this.positives, this.negatives);
var vector = new Vector(8, 3.5);
var actualClass = kNearestNeighbour.kNearestNeighbour(this.positives, this.negatives, vector);
var actualClass = kNearestNeighbour.classify(vector);
var expectedClass = DataClass.NEGATIVE;
assertEquals(expectedClass, actualClass);
@ -52,11 +60,118 @@ class KNearestNeighbourTest
public void shouldReturnCorrectClassForVectorWithKEquals5()
{
var kNearestNeighbour = new KNearestNeighbour((a ,b) -> Math.abs(a.get(0) - b.get(0)) + Math.abs(a.get(1) - b.get(1)), 5);
kNearestNeighbour.learn(this.positives, this.negatives);
var vector = new Vector(8, 3.5);
var actualClass = kNearestNeighbour.kNearestNeighbour(this.positives, this.negatives, vector);
var actualClass = kNearestNeighbour.classify(vector);
var expectedClass = DataClass.POSITIVE;
assertEquals(expectedClass, actualClass);
}
@Test
public void shouldReturnCorrectClassesForAppendicitisData()
{
var trainDataFile = "./resources/app1.data";
var testDataFile = "./resources/app1.test";
var trainDataVectors = readFromFile(trainDataFile);
var dataClasses = splitIntoClasses(trainDataVectors);
var negatives = dataClasses.get(DataClass.NEGATIVE);
var positives = dataClasses.get(DataClass.POSITIVE);
var kNearestNeighbour = new KNearestNeighbour(Vector::distance);
kNearestNeighbour.learn(positives, negatives);
var testDataVectors = readFromFile(testDataFile);
var failCount = 0;
for (var vector : testDataVectors)
{
var expectedClass = DataClass.valueOf(Double.valueOf(vector.get(vector.dimension() - 1)).intValue());
var testVector = vector.decreasedDimension();
var actualClass = kNearestNeighbour.classify(testVector.normalized());
try
{
assertEquals(expectedClass, actualClass);
}
catch (AssertionFailedError e)
{
failCount++;
}
}
System.out.println(failCount + " of " + testDataVectors.size() + " are not correct classified.");
System.out.println("Fail rate of " + Math.round(100d * failCount / testDataVectors.size()) + " %");
}
@Test
public void shouldReturnOptimum()
{
var trainDataFile = "./resources/app1.data";
var testDataFile = "./resources/app1.test";
var trainDataVectors = readFromFile(trainDataFile);
var testDataVectors = readFromFile(testDataFile);
var data = Stream.concat(trainDataVectors.stream(), testDataVectors.stream())
.collect(Collectors.toList());
var crossValidation = new CrossValidation(1, 100);
var kNearestNeighbour = crossValidation.validate(data, data.size());
}
private List<Vector> readFromFile(String file)
{
List<Vector> vectorList = new ArrayList<>();
try (var reader = new BufferedReader(new FileReader(file)))
{
String line;
while ((line = reader.readLine()) != null)
{
vectorList.add(new Vector(
Arrays.stream(line.split(","))
.map(Double::valueOf)
.collect(Collectors.toList())
));
}
}
catch (IOException e)
{
e.printStackTrace();
}
return vectorList;
}
private Map<DataClass, List<Vector>> splitIntoClasses(List<Vector> data)
{
var positives = data.stream()
.filter(v -> v.get(v.dimension()-1) == 1)
.collect(Collectors.toList());
var negatives = data.stream()
.filter(v -> v.get(v.dimension()-1) == 0)
.collect(Collectors.toList());
positives = positives.stream()
.map(Vector::decreasedDimension)
.map(Vector::normalized)
.collect(Collectors.toList());
negatives = negatives.stream()
.map(Vector::decreasedDimension)
.map(Vector::normalized)
.collect(Collectors.toList());
return Map.ofEntries(Map.entry(DataClass.NEGATIVE, negatives), Map.entry(DataClass.POSITIVE, positives));
}
}

3
test/machine_learning/perceptron/PerceptronTest.java
View File

@ -1,8 +1,7 @@
package machine_learning.perceptron;
import machine_learning.Vector;
import machine_learning.nearest_neighbour.DataClass;
import org.junit.jupiter.api.Assertions;
import machine_learning.DataClass;
import org.junit.jupiter.api.BeforeAll;
import org.junit.jupiter.api.Test;
import org.junit.jupiter.api.TestInstance;