source: orange/Orange/testing/regression/results_reference/freeviz-pca.py.txt @ 10475:61c2249d671f

Revision 10475:61c2249d671f, 4.2 KB checked in by Matija Polajnar <matija.polajnar@…>, 2 years ago (diff)

Major refactorization of linear projections, fixing some bugs in the process.

Line 
1PCA
2['first', 'adult', 'male', 'yes'] [0.719, 1.218]
3['second', 'adult', 'male', 'no'] [1.684, 1.468]
4['second', 'adult', 'male', 'no'] [1.684, 1.468]
5['second', 'adult', 'female', 'yes'] [1.932, 0.499]
6['third', 'adult', 'male', 'no'] [2.650, 1.717]
7['third', 'adult', 'male', 'no'] [2.650, 1.717]
8['third', 'adult', 'male', 'no'] [2.650, 1.717]
9['third', 'adult', 'male', 'no'] [2.650, 1.717]
10['third', 'adult', 'male', 'no'] [2.650, 1.717]
11['third', 'adult', 'male', 'no'] [2.650, 1.717]
12['third', 'adult', 'female', 'yes'] [2.898, 0.749]
13['third', 'adult', 'female', 'no'] [2.898, 0.749]
14['third', 'child', 'male', 'no'] [2.736, 1.701]
15['crew', 'adult', 'male', 'yes'] [-0.247, 0.969]
16['crew', 'adult', 'male', 'yes'] [-0.247, 0.969]
17['crew', 'adult', 'male', 'yes'] [-0.247, 0.969]
18['crew', 'adult', 'male', 'yes'] [-0.247, 0.969]
19['crew', 'adult', 'male', 'no'] [-0.247, 0.969]
20['crew', 'adult', 'male', 'no'] [-0.247, 0.969]
21['crew', 'adult', 'male', 'no'] [-0.247, 0.969]
22['crew', 'adult', 'male', 'no'] [-0.247, 0.969]
23['crew', 'adult', 'male', 'no'] [-0.247, 0.969]
24SPCA
25['first', 'adult', 'male', 'yes'] [1.030, 0.195]
26['second', 'adult', 'male', 'no'] [1.062, 0.322]
27['second', 'adult', 'male', 'no'] [1.062, 0.322]
28['second', 'adult', 'female', 'yes'] [0.064, 0.254]
29['third', 'adult', 'male', 'no'] [1.094, 0.449]
30['third', 'adult', 'male', 'no'] [1.094, 0.449]
31['third', 'adult', 'male', 'no'] [1.094, 0.449]
32['third', 'adult', 'male', 'no'] [1.094, 0.449]
33['third', 'adult', 'male', 'no'] [1.094, 0.449]
34['third', 'adult', 'male', 'no'] [1.094, 0.449]
35['third', 'adult', 'female', 'yes'] [0.096, 0.381]
36['third', 'adult', 'female', 'no'] [0.096, 0.381]
37['third', 'child', 'male', 'no'] [1.021, 1.439]
38['crew', 'adult', 'male', 'yes'] [0.998, 0.068]
39['crew', 'adult', 'male', 'yes'] [0.998, 0.068]
40['crew', 'adult', 'male', 'yes'] [0.998, 0.068]
41['crew', 'adult', 'male', 'yes'] [0.998, 0.068]
42['crew', 'adult', 'male', 'no'] [0.998, 0.068]
43['crew', 'adult', 'male', 'no'] [0.998, 0.068]
44['crew', 'adult', 'male', 'no'] [0.998, 0.068]
45['crew', 'adult', 'male', 'no'] [0.998, 0.068]
46['crew', 'adult', 'male', 'no'] [0.998, 0.068]
47SPCA w/out generalization
48['first', 'adult', 'male', 'yes'] [0.548, 1.303]
49['second', 'adult', 'male', 'no'] [1.471, 1.680]
50['second', 'adult', 'male', 'no'] [1.471, 1.680]
51['second', 'adult', 'female', 'yes'] [1.845, 0.752]
52['third', 'adult', 'male', 'no'] [2.394, 2.056]
53['third', 'adult', 'male', 'no'] [2.394, 2.056]
54['third', 'adult', 'male', 'no'] [2.394, 2.056]
55['third', 'adult', 'male', 'no'] [2.394, 2.056]
56['third', 'adult', 'male', 'no'] [2.394, 2.056]
57['third', 'adult', 'male', 'no'] [2.394, 2.056]
58['third', 'adult', 'female', 'yes'] [2.768, 1.129]
59['third', 'adult', 'female', 'no'] [2.768, 1.129]
60['third', 'child', 'male', 'no'] [2.492, 2.057]
61['crew', 'adult', 'male', 'yes'] [-0.374, 0.927]
62['crew', 'adult', 'male', 'yes'] [-0.374, 0.927]
63['crew', 'adult', 'male', 'yes'] [-0.374, 0.927]
64['crew', 'adult', 'male', 'yes'] [-0.374, 0.927]
65['crew', 'adult', 'male', 'no'] [-0.374, 0.927]
66['crew', 'adult', 'male', 'no'] [-0.374, 0.927]
67['crew', 'adult', 'male', 'no'] [-0.374, 0.927]
68['crew', 'adult', 'male', 'no'] [-0.374, 0.927]
69['crew', 'adult', 'male', 'no'] [-0.374, 0.927]
70PCA with 2 attributes
71['first', 'adult', 'male', 'yes'] [0.996, 0.086]
72['second', 'adult', 'male', 'no'] [1.993, 0.171]
73['second', 'adult', 'male', 'no'] [1.993, 0.171]
74['second', 'adult', 'female', 'yes'] [1.993, 0.171]
75['third', 'adult', 'male', 'no'] [2.989, 0.257]
76['third', 'adult', 'male', 'no'] [2.989, 0.257]
77['third', 'adult', 'male', 'no'] [2.989, 0.257]
78['third', 'adult', 'male', 'no'] [2.989, 0.257]
79['third', 'adult', 'male', 'no'] [2.989, 0.257]
80['third', 'adult', 'male', 'no'] [2.989, 0.257]
81['third', 'adult', 'female', 'yes'] [2.989, 0.257]
82['third', 'adult', 'female', 'no'] [2.989, 0.257]
83['third', 'child', 'male', 'no'] [3.075, -0.739]
84['crew', 'adult', 'male', 'yes'] [0.000, 0.000]
85['crew', 'adult', 'male', 'yes'] [0.000, 0.000]
86['crew', 'adult', 'male', 'yes'] [0.000, 0.000]
87['crew', 'adult', 'male', 'yes'] [0.000, 0.000]
88['crew', 'adult', 'male', 'no'] [0.000, 0.000]
89['crew', 'adult', 'male', 'no'] [0.000, 0.000]
90['crew', 'adult', 'male', 'no'] [0.000, 0.000]
91['crew', 'adult', 'male', 'no'] [0.000, 0.000]
92['crew', 'adult', 'male', 'no'] [0.000, 0.000]
Note: See TracBrowser for help on using the repository browser.