temp.output.txt

一个matlab的工具包,里面包括一些分类器 例如 KNN KMEAN SVM NETLAB 等等有很多.

0 2 0.7317073170731707 2 
1 2 1.0 2 
2 2 0.7317073170731707 2 
3 2 0.7317073170731707 1 
4 2 0.9636363636363636 2 
5 2 0.9636363636363636 2 
6 1 0.9090909090909091 2 
7 2 1.0 2 
8 1 1.0 1 
9 2 1.0 2 
10 2 0.8571428571428571 1 
11 2 1.0 2 
12 1 1.0 1 
13 2 1.0 2 
14 2 0.7317073170731707 1 
15 2 1.0 2 
16 1 0.8888888888888888 1 
17 2 0.9636363636363636 2 
18 2 0.9636363636363636 2 
19 2 0.9636363636363636 2 
20 2 0.9636363636363636 2 
21 2 0.9636363636363636 2 
22 2 0.9636363636363636 2 
23 2 1.0 2 
24 2 0.8526315789473684 2 
25 2 0.9636363636363636 2 
26 2 0.8526315789473684 2 
27 2 0.8526315789473684 2 
28 2 0.8526315789473684 1 
29 2 1.0 2 
30 2 0.9636363636363636 2 
31 2 1.0 2 
32 2 0.8526315789473684 2 
33 2 0.9636363636363636 2 
34 2 0.8526315789473684 2 
35 2 0.8526315789473684 2 
36 2 0.8526315789473684 2 
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38 2 1.0 2 
39 2 1.0 2 
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41 2 0.9636363636363636 2 
42 2 0.8526315789473684 2 
43 2 0.8526315789473684 2 
44 2 0.8526315789473684 1 
45 2 0.8333333333333334 2 
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47 2 1.0 2 
48 2 0.8526315789473684 2 
49 2 0.9636363636363636 2 
50 2 0.8526315789473684 2 
51 2 0.8526315789473684 2 
52 2 0.8526315789473684 2 
53 2 0.8526315789473684 1 
54 2 0.8526315789473684 2 
55 2 1.0 2 
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58 2 0.8526315789473684 2 
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63 2 0.9636363636363636 2 
64 2 0.8526315789473684 1 
65 2 1.0 2 
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68 2 0.9636363636363636 2 
69 2 0.9636363636363636 2 
70 2 0.8526315789473684 2 
71 2 1.0 2 
72 2 1.0 2 
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74 2 0.9636363636363636 2 
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76 2 0.8526315789473684 2 
77 2 1.0 2 
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83 2 1.0 2 
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94 2 0.8526315789473684 2 
95 2 1.0 2 
96 2 1.0 2 
97 2 0.8526315789473684 2 
98 1 0.8888888888888888 1 
99 1 0.875 2 
100 2 0.8526315789473684 2 
101 2 1.0 2 
102 2 1.0 2 
103 2 0.8526315789473684 2 
104 2 0.8526315789473684 1 
105 2 1.0 2 
106 2 1.0 1 
107 2 0.9636363636363636 2 
108 2 0.9636363636363636 2 
109 1 1.0 1 
110 1 0.875 2 
111 2 0.9636363636363636 2 
112 2 0.8571428571428571 1 
113 2 0.7317073170731707 2 
114 2 0.7317073170731707 2 
115 1 0.9090909090909091 1 
116 2 1.0 2 
117 2 1.0 2 
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120 2 0.7317073170731707 1 
121 2 1.0 2 
122 2 1.0 2 
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126 2 0.9636363636363636 2 
127 2 0.9636363636363636 2 
128 2 1.0 2 
129 2 0.7317073170731707 2 
130 1 1.0 1 
131 1 0.875 2 
132 2 0.9636363636363636 2 
133 2 0.9636363636363636 2 
134 2 0.9636363636363636 2 
135 2 1.0 2 
136 2 0.8526315789473684 2 
137 2 0.8526315789473684 2 
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141 2 0.8526315789473684 2 
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156 2 0.8526315789473684 2 
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197 2 0.8526315789473684 2 
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214 2 0.8526315789473684 2 
215 2 0.8526315789473684 1 
216 2 0.9636363636363636 2 
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218 2 1.0 2 
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221 2 0.8526315789473684 2 
222 2 0.8526315789473684 2 
223 2 0.8526315789473684 2 
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225 2 1.0 2 
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227 1 0.9090909090909091 1 
228 2 0.9636363636363636 2 
229 2 0.9636363636363636 2 
230 2 1.0 2 
231 1 1.0 1 
232 2 0.8571428571428571 2 
233 2 1.0 2 
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235 2 0.9636363636363636 2 
236 2 1.0 2 
237 2 0.8571428571428571 1 
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239 2 1.0 2 
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241 2 0.8571428571428571 2 
242 2 0.7317073170731707 2 
243 2 1.0 2 
244 2 0.7317073170731707 1 
245 2 0.9636363636363636 2 
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247 2 1.0 2 
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255 2 0.8526315789473684 2 
256 2 1.0 2 
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261 2 1.0 1 
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265 2 1.0 1 
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