decision-tree #17

Merged
s473577 merged 3 commits from decision-tree into master 2023-05-28 13:44:43 +02:00
6 changed files with 815 additions and 0 deletions

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# Byte-compiled / optimized / DLL files
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*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
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MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
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# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
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nosetests.xml
coverage.xml
*.cover
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# Translations
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*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
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target/
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# IPython
profile_default/
ipython_config.py
# pyenv
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# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
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#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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.pdm.toml
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# PyCharm
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digraph Tree {
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edge [fontname="helvetica"] ;
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29 -> 30 ;
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30 -> 31 ;
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39 -> 41 ;
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42 -> 43 ;
44 [label="wielkosc <= 0.5\nentropy = 0.811\nsamples = 4\nvalue = [3, 1]\nclass = 0", fillcolor="#eeab7b"] ;
42 -> 44 ;
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44 -> 45 ;
46 [label="kruchosc <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [2, 1]\nclass = 0", fillcolor="#f2c09c"] ;
44 -> 46 ;
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46 -> 47 ;
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48 -> 49 ;
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29 -> 51 ;
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53 [label="waga, <= 0.5\nentropy = 0.469\nsamples = 70\nvalue = [7, 63]\nclass = 1", fillcolor="#4fa8e8"] ;
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53 -> 54 ;
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55 -> 56 ;
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56 -> 57 ;
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56 -> 58 ;
59 [label="dolna <= 0.5\nentropy = 0.722\nsamples = 5\nvalue = [1, 4]\nclass = 1", fillcolor="#6ab6ec"] ;
58 -> 59 ;
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59 -> 60 ;
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59 -> 61 ;
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62 -> 63 ;
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58 -> 66 ;
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71 -> 73 ;
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68 -> 74 ;
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75 -> 76 ;
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77 -> 81 ;
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52 -> 82 ;
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82 -> 83 ;
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83 -> 84 ;
85 [label="waga, <= 1.5\nentropy = 0.971\nsamples = 5\nvalue = [3, 2]\nclass = 0", fillcolor="#f6d5bd"] ;
83 -> 85 ;
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85 -> 86 ;
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86 -> 87 ;
88 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
86 -> 88 ;
89 [label="entropy = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = 0", fillcolor="#e58139"] ;
85 -> 89 ;
90 [label="dolna <= 0.5\nentropy = 0.61\nsamples = 20\nvalue = [3, 17]\nclass = 1", fillcolor="#5caeea"] ;
82 -> 90 ;
91 [label="entropy = 0.0\nsamples = 11\nvalue = [0, 11]\nclass = 1", fillcolor="#399de5"] ;
90 -> 91 ;
92 [label="kruchosc <= 0.5\nentropy = 0.918\nsamples = 9\nvalue = [3, 6]\nclass = 1", fillcolor="#9ccef2"] ;
90 -> 92 ;
93 [label="waga, <= 0.5\nentropy = 0.811\nsamples = 4\nvalue = [3, 1]\nclass = 0", fillcolor="#eeab7b"] ;
92 -> 93 ;
94 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
93 -> 94 ;
95 [label="entropy = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = 0", fillcolor="#e58139"] ;
93 -> 95 ;
96 [label="entropy = 0.0\nsamples = 5\nvalue = [0, 5]\nclass = 1", fillcolor="#399de5"] ;
92 -> 96 ;
}

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wielkosc,"waga,",priorytet,ksztalt,kruchosc,dolna,gorna,g > d,polka
1,0,0,1,0,0,1,0,1
0,0,1,0,1,1,0,1,1
2,0,1,1,0,0,0,1,0
2,2,1,0,1,1,1,0,0
1,0,0,1,0,0,0,1,1
2,1,0,0,1,1,0,0,0
1,0,0,0,1,0,0,1,1
1,1,0,1,0,0,0,1,1
0,0,1,0,1,1,1,0,1
0,2,0,0,0,1,1,0,0
0,0,1,0,0,1,0,1,1
0,0,0,0,0,1,1,0,1
0,2,1,0,1,1,0,0,0
2,0,0,0,1,0,0,0,1
2,1,0,1,0,1,1,1,0
0,1,1,0,1,1,1,0,0
0,2,0,1,1,1,0,1,1
1,2,1,0,1,1,0,0,0
0,0,1,1,1,1,0,1,1
0,0,0,1,1,0,0,1,1
1,1,1,1,1,0,1,0,0
1,2,1,0,0,1,1,1,1
2,2,1,1,0,1,1,1,0
1,2,1,0,1,1,0,1,1
0,1,0,0,0,1,0,1,1
1,1,0,0,0,1,0,1,1
0,1,0,0,0,1,1,1,1
2,1,0,1,0,1,0,1,0
0,1,1,0,1,1,0,0,0
2,1,0,1,0,1,1,0,0
1,2,1,0,0,0,1,1,1
1,2,0,1,0,1,1,1,1
0,2,0,1,0,1,0,1,0
2,1,1,0,1,1,1,1,1
0,2,0,1,0,0,0,1,1
0,1,1,0,0,1,1,0,0
2,2,1,0,0,0,1,1,1
1,0,0,0,0,0,1,0,1
0,0,1,1,0,1,0,0,0
2,2,0,1,1,1,0,0,0
1,2,1,1,0,0,0,1,0
1,2,0,1,0,0,1,1,1
0,1,0,1,1,1,1,0,0
0,1,0,0,1,1,0,0,0
0,1,0,1,1,0,0,0,0
1,1,1,0,1,1,0,1,1
1,1,1,1,0,1,1,0,0
2,1,1,1,0,1,1,0,0
2,2,0,0,1,1,0,0,0
1,0,0,1,0,1,0,1,1
2,1,1,1,1,0,1,0,0
0,0,0,0,1,1,0,0,1
2,1,1,1,0,1,0,1,0
1,2,1,1,1,0,1,1,1
0,2,0,0,1,1,1,1,1
2,1,0,1,1,0,0,0,0
0,2,1,1,1,0,1,1,1
1,2,0,1,1,1,1,0,1
0,2,0,0,0,1,0,1,1
1,2,0,0,0,1,0,0,0
2,0,0,1,0,1,1,1,1
2,1,1,0,0,0,1,1,1
0,1,1,1,0,1,0,0,0
2,1,0,1,1,1,0,0,0
0,2,0,1,0,0,0,0,0
2,1,0,0,1,0,0,1,1
1,1,0,0,1,1,0,0,0
2,0,0,1,0,0,1,1,1
2,0,1,1,1,0,1,1,1
2,2,0,1,1,0,0,0,0
0,1,0,1,1,1,0,1,1
1,0,1,1,1,0,0,0,0
2,0,0,1,1,1,1,1,1
1,0,0,0,0,0,0,1,1
2,1,1,0,0,0,0,1,0
0,0,0,0,1,1,0,1,1
0,1,0,1,0,0,0,1,1
2,2,0,1,0,0,0,0,0
0,2,1,1,1,1,0,1,0
2,2,1,0,0,1,1,0,0
1,2,0,0,1,1,1,0,1
0,1,1,1,0,0,0,1,0
1,1,1,0,1,0,0,0,0
2,0,1,1,0,0,1,1,1
2,0,1,0,1,0,1,0,1
2,2,0,0,0,1,1,0,0
1,1,0,1,1,0,1,1,1
2,0,0,0,0,0,1,1,1
1,2,0,0,1,1,0,1,1
1,2,1,1,0,0,0,0,0
0,0,1,1,1,1,1,0,1
0,2,1,1,0,1,0,0,0
2,1,1,0,0,0,1,0,0
1,0,0,1,1,0,0,0,1
2,2,0,1,1,1,0,1,0
2,0,0,1,1,1,0,0,0
0,2,1,0,0,0,0,0,0
1,2,1,1,1,0,0,1,1
0,0,0,0,0,1,1,1,1
2,2,1,1,1,0,1,1,1
0,1,0,0,1,0,1,0,1
2,1,1,0,1,1,0,0,0
0,1,1,1,1,1,1,1,1
1,2,1,1,1,0,1,0,0
2,0,1,1,1,1,1,0,0
1,0,1,1,0,0,1,0,0
0,2,0,0,1,0,0,1,1
2,2,0,0,0,1,0,0,0
0,2,0,0,1,1,0,0,0
0,1,0,0,0,0,1,1,1
1,0,0,0,0,1,0,1,1
2,1,0,0,0,0,1,0,0
0,1,1,0,0,1,0,0,0
1,0,1,0,1,0,1,0,1
2,0,0,0,1,1,0,0,0
0,0,0,0,0,0,0,0,1
0,0,1,0,1,0,0,0,1
1,0,1,0,0,0,0,0,0
0,2,1,0,0,0,0,1,1
2,0,0,1,1,1,0,1,1
0,2,0,1,1,1,1,0,0
0,2,1,1,1,1,1,1,1
1,2,0,1,0,1,1,0,0
0,2,1,0,0,1,0,0,0
2,0,1,1,1,1,1,1,1
0,0,0,1,1,1,1,1,1
1,2,0,1,1,0,0,0,0
1,2,0,1,1,0,0,1,1
2,2,0,1,0,0,1,0,0
2,2,0,0,0,0,1,0,0
0,0,0,1,0,0,1,0,1
1,0,1,0,1,0,0,0,1
0,2,0,0,0,0,0,0,0
2,0,1,0,1,1,1,1,1
0,2,1,0,0,0,1,1,1
0,2,1,0,1,1,1,1,1
2,2,1,0,1,0,1,0,0
1,1,1,1,1,1,1,1,1
0,1,1,0,1,0,0,0,0
2,1,1,0,0,1,1,1,0
0,0,1,0,1,1,1,1,1
0,1,1,0,1,0,1,0,1
2,0,0,1,0,0,1,0,0
1,1,0,1,1,1,1,0,0
2,0,0,1,1,1,1,0,0
0,0,1,0,0,1,1,0,0
1,0,1,0,1,1,1,1,1
0,1,0,0,0,0,0,1,1
0,2,0,1,1,0,0,1,1
2,1,1,0,1,0,1,1,1
1,1,1,1,1,0,1,1,1
1,0,1,1,0,0,1,1,1
1,0,0,1,1,0,0,1,1
2,1,1,1,0,0,1,0,0
1,0,0,0,0,0,0,0,1
0,0,0,1,1,1,1,0,1
1,0,1,1,0,0,0,1,1
2,1,1,1,1,0,1,1,1
1,2,0,1,0,1,0,1,0
1,1,0,0,0,1,1,0,0
2,2,1,0,1,1,0,1,0
0,0,0,0,0,0,1,0,1
0,2,0,0,0,1,1,1,1
2,1,0,0,0,0,1,1,1
0,0,0,1,1,1,0,0,0
1,0,1,0,0,1,1,0,0
2,0,0,0,1,1,1,1,1
1,2,1,0,0,0,0,1,1
2,2,0,0,0,1,0,1,0
0,1,1,0,0,0,1,0,0
0,2,0,0,1,0,1,0,1
1,1,0,0,1,1,1,1,1
0,0,0,1,0,0,1,1,1
0,1,1,0,0,1,1,1,1
2,2,0,1,1,0,1,0,0
1,0,1,0,1,0,1,1,1
1,1,0,1,0,0,1,1,1
2,0,1,1,0,0,1,0,0
2,0,1,0,0,0,1,0,0
1,1,1,1,0,1,1,1,0
2,1,1,0,1,0,0,0,0
0,2,0,1,1,0,0,0,0
1,2,1,1,0,1,0,0,0
2,1,1,1,1,1,0,1,0
0,2,0,1,0,1,1,1,1
0,2,1,0,1,0,0,1,1
0,1,1,0,0,0,1,1,1
1,0,0,1,1,0,1,1,1
2,2,1,1,0,0,0,0,0
0,1,1,0,0,0,0,0,0
2,0,1,1,0,1,0,0,0
0,1,1,0,0,0,0,1,1
0,0,1,1,1,0,1,0,1
0,2,0,0,0,0,1,0,1
2,0,0,1,0,1,1,0,0
0,0,1,0,1,0,1,1,1
2,2,0,0,1,0,1,1,1
2,2,0,1,0,0,0,1,0
2,2,0,1,0,1,0,1,0
1,2,1,0,0,1,0,1,0
1 wielkosc waga, priorytet ksztalt kruchosc dolna gorna g > d polka
2 1 0 0 1 0 0 1 0 1
3 0 0 1 0 1 1 0 1 1
4 2 0 1 1 0 0 0 1 0
5 2 2 1 0 1 1 1 0 0
6 1 0 0 1 0 0 0 1 1
7 2 1 0 0 1 1 0 0 0
8 1 0 0 0 1 0 0 1 1
9 1 1 0 1 0 0 0 1 1
10 0 0 1 0 1 1 1 0 1
11 0 2 0 0 0 1 1 0 0
12 0 0 1 0 0 1 0 1 1
13 0 0 0 0 0 1 1 0 1
14 0 2 1 0 1 1 0 0 0
15 2 0 0 0 1 0 0 0 1
16 2 1 0 1 0 1 1 1 0
17 0 1 1 0 1 1 1 0 0
18 0 2 0 1 1 1 0 1 1
19 1 2 1 0 1 1 0 0 0
20 0 0 1 1 1 1 0 1 1
21 0 0 0 1 1 0 0 1 1
22 1 1 1 1 1 0 1 0 0
23 1 2 1 0 0 1 1 1 1
24 2 2 1 1 0 1 1 1 0
25 1 2 1 0 1 1 0 1 1
26 0 1 0 0 0 1 0 1 1
27 1 1 0 0 0 1 0 1 1
28 0 1 0 0 0 1 1 1 1
29 2 1 0 1 0 1 0 1 0
30 0 1 1 0 1 1 0 0 0
31 2 1 0 1 0 1 1 0 0
32 1 2 1 0 0 0 1 1 1
33 1 2 0 1 0 1 1 1 1
34 0 2 0 1 0 1 0 1 0
35 2 1 1 0 1 1 1 1 1
36 0 2 0 1 0 0 0 1 1
37 0 1 1 0 0 1 1 0 0
38 2 2 1 0 0 0 1 1 1
39 1 0 0 0 0 0 1 0 1
40 0 0 1 1 0 1 0 0 0
41 2 2 0 1 1 1 0 0 0
42 1 2 1 1 0 0 0 1 0
43 1 2 0 1 0 0 1 1 1
44 0 1 0 1 1 1 1 0 0
45 0 1 0 0 1 1 0 0 0
46 0 1 0 1 1 0 0 0 0
47 1 1 1 0 1 1 0 1 1
48 1 1 1 1 0 1 1 0 0
49 2 1 1 1 0 1 1 0 0
50 2 2 0 0 1 1 0 0 0
51 1 0 0 1 0 1 0 1 1
52 2 1 1 1 1 0 1 0 0
53 0 0 0 0 1 1 0 0 1
54 2 1 1 1 0 1 0 1 0
55 1 2 1 1 1 0 1 1 1
56 0 2 0 0 1 1 1 1 1
57 2 1 0 1 1 0 0 0 0
58 0 2 1 1 1 0 1 1 1
59 1 2 0 1 1 1 1 0 1
60 0 2 0 0 0 1 0 1 1
61 1 2 0 0 0 1 0 0 0
62 2 0 0 1 0 1 1 1 1
63 2 1 1 0 0 0 1 1 1
64 0 1 1 1 0 1 0 0 0
65 2 1 0 1 1 1 0 0 0
66 0 2 0 1 0 0 0 0 0
67 2 1 0 0 1 0 0 1 1
68 1 1 0 0 1 1 0 0 0
69 2 0 0 1 0 0 1 1 1
70 2 0 1 1 1 0 1 1 1
71 2 2 0 1 1 0 0 0 0
72 0 1 0 1 1 1 0 1 1
73 1 0 1 1 1 0 0 0 0
74 2 0 0 1 1 1 1 1 1
75 1 0 0 0 0 0 0 1 1
76 2 1 1 0 0 0 0 1 0
77 0 0 0 0 1 1 0 1 1
78 0 1 0 1 0 0 0 1 1
79 2 2 0 1 0 0 0 0 0
80 0 2 1 1 1 1 0 1 0
81 2 2 1 0 0 1 1 0 0
82 1 2 0 0 1 1 1 0 1
83 0 1 1 1 0 0 0 1 0
84 1 1 1 0 1 0 0 0 0
85 2 0 1 1 0 0 1 1 1
86 2 0 1 0 1 0 1 0 1
87 2 2 0 0 0 1 1 0 0
88 1 1 0 1 1 0 1 1 1
89 2 0 0 0 0 0 1 1 1
90 1 2 0 0 1 1 0 1 1
91 1 2 1 1 0 0 0 0 0
92 0 0 1 1 1 1 1 0 1
93 0 2 1 1 0 1 0 0 0
94 2 1 1 0 0 0 1 0 0
95 1 0 0 1 1 0 0 0 1
96 2 2 0 1 1 1 0 1 0
97 2 0 0 1 1 1 0 0 0
98 0 2 1 0 0 0 0 0 0
99 1 2 1 1 1 0 0 1 1
100 0 0 0 0 0 1 1 1 1
101 2 2 1 1 1 0 1 1 1
102 0 1 0 0 1 0 1 0 1
103 2 1 1 0 1 1 0 0 0
104 0 1 1 1 1 1 1 1 1
105 1 2 1 1 1 0 1 0 0
106 2 0 1 1 1 1 1 0 0
107 1 0 1 1 0 0 1 0 0
108 0 2 0 0 1 0 0 1 1
109 2 2 0 0 0 1 0 0 0
110 0 2 0 0 1 1 0 0 0
111 0 1 0 0 0 0 1 1 1
112 1 0 0 0 0 1 0 1 1
113 2 1 0 0 0 0 1 0 0
114 0 1 1 0 0 1 0 0 0
115 1 0 1 0 1 0 1 0 1
116 2 0 0 0 1 1 0 0 0
117 0 0 0 0 0 0 0 0 1
118 0 0 1 0 1 0 0 0 1
119 1 0 1 0 0 0 0 0 0
120 0 2 1 0 0 0 0 1 1
121 2 0 0 1 1 1 0 1 1
122 0 2 0 1 1 1 1 0 0
123 0 2 1 1 1 1 1 1 1
124 1 2 0 1 0 1 1 0 0
125 0 2 1 0 0 1 0 0 0
126 2 0 1 1 1 1 1 1 1
127 0 0 0 1 1 1 1 1 1
128 1 2 0 1 1 0 0 0 0
129 1 2 0 1 1 0 0 1 1
130 2 2 0 1 0 0 1 0 0
131 2 2 0 0 0 0 1 0 0
132 0 0 0 1 0 0 1 0 1
133 1 0 1 0 1 0 0 0 1
134 0 2 0 0 0 0 0 0 0
135 2 0 1 0 1 1 1 1 1
136 0 2 1 0 0 0 1 1 1
137 0 2 1 0 1 1 1 1 1
138 2 2 1 0 1 0 1 0 0
139 1 1 1 1 1 1 1 1 1
140 0 1 1 0 1 0 0 0 0
141 2 1 1 0 0 1 1 1 0
142 0 0 1 0 1 1 1 1 1
143 0 1 1 0 1 0 1 0 1
144 2 0 0 1 0 0 1 0 0
145 1 1 0 1 1 1 1 0 0
146 2 0 0 1 1 1 1 0 0
147 0 0 1 0 0 1 1 0 0
148 1 0 1 0 1 1 1 1 1
149 0 1 0 0 0 0 0 1 1
150 0 2 0 1 1 0 0 1 1
151 2 1 1 0 1 0 1 1 1
152 1 1 1 1 1 0 1 1 1
153 1 0 1 1 0 0 1 1 1
154 1 0 0 1 1 0 0 1 1
155 2 1 1 1 0 0 1 0 0
156 1 0 0 0 0 0 0 0 1
157 0 0 0 1 1 1 1 0 1
158 1 0 1 1 0 0 0 1 1
159 2 1 1 1 1 0 1 1 1
160 1 2 0 1 0 1 0 1 0
161 1 1 0 0 0 1 1 0 0
162 2 2 1 0 1 1 0 1 0
163 0 0 0 0 0 0 1 0 1
164 0 2 0 0 0 1 1 1 1
165 2 1 0 0 0 0 1 1 1
166 0 0 0 1 1 1 0 0 0
167 1 0 1 0 0 1 1 0 0
168 2 0 0 0 1 1 1 1 1
169 1 2 1 0 0 0 0 1 1
170 2 2 0 0 0 1 0 1 0
171 0 1 1 0 0 0 1 0 0
172 0 2 0 0 1 0 1 0 1
173 1 1 0 0 1 1 1 1 1
174 0 0 0 1 0 0 1 1 1
175 0 1 1 0 0 1 1 1 1
176 2 2 0 1 1 0 1 0 0
177 1 0 1 0 1 0 1 1 1
178 1 1 0 1 0 0 1 1 1
179 2 0 1 1 0 0 1 0 0
180 2 0 1 0 0 0 1 0 0
181 1 1 1 1 0 1 1 1 0
182 2 1 1 0 1 0 0 0 0
183 0 2 0 1 1 0 0 0 0
184 1 2 1 1 0 1 0 0 0
185 2 1 1 1 1 1 0 1 0
186 0 2 0 1 0 1 1 1 1
187 0 2 1 0 1 0 0 1 1
188 0 1 1 0 0 0 1 1 1
189 1 0 0 1 1 0 1 1 1
190 2 2 1 1 0 0 0 0 0
191 0 1 1 0 0 0 0 0 0
192 2 0 1 1 0 1 0 0 0
193 0 1 1 0 0 0 0 1 1
194 0 0 1 1 1 0 1 0 1
195 0 2 0 0 0 0 1 0 1
196 2 0 0 1 0 1 1 0 0
197 0 0 1 0 1 0 1 1 1
198 2 2 0 0 1 0 1 1 1
199 2 2 0 1 0 0 0 1 0
200 2 2 0 1 0 1 0 1 0
201 1 2 1 0 0 1 0 1 0

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@ -0,0 +1,57 @@
import graphviz
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz
plikZPrzecinkami = open("training_data.txt", 'w')
with open('200permutations_table.txt', 'r') as plik:
for linia in plik:
liczby = linia.strip()
wiersz = ""
licznik = 0
for liczba in liczby:
wiersz += liczba
wiersz += ";"
wiersz = wiersz[:-1]
wiersz += '\n'
plikZPrzecinkami.write(wiersz)
plikZPrzecinkami.close()
x = pd.read_csv('training_data.txt', delimiter=';',
names=['wielkosc', 'waga,', 'priorytet', 'ksztalt', 'kruchosc', 'dolna', 'gorna', 'g > d'])
y = pd.read_csv('decisions.txt', names=['polka'])
# X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1) # 70% treningowe and 30% testowe
# Tworzenie instancji klasyfikatora ID3
clf = DecisionTreeClassifier(criterion='entropy')
# Trenowanie klasyfikatora
clf.fit(x.values, y.values)
# clf.fit(X_train, y_train)
# Predykcja na nowych danych
new_data = [[2, 2, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]]
predictions = clf.predict(new_data)
# y_pred = clf.predict(X_test)
print(predictions)
# print("Accuracy:", clf.score(new_data, predictions))
# print("Accuracy:", metrics.accuracy_score(y_test, y_pred))
# Wygenerowanie pliku .dot reprezentującego drzewo
dot_data = export_graphviz(clf, out_file=None, feature_names=list(x.columns), class_names=['0', '1'], filled=True,
rounded=True)
# Tworzenie obiektu graphviz z pliku .dot
graph = graphviz.Source(dot_data)
# Wyświetlanie drzewa
graph.view()
z = pd.concat([x, y], axis=1)
z.to_csv('dane.csv', index=False)

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@ -0,0 +1,200 @@
1;0;0;1;0;0;1;0
0;0;1;0;1;1;0;1
2;0;1;1;0;0;0;1
2;2;1;0;1;1;1;0
1;0;0;1;0;0;0;1
2;1;0;0;1;1;0;0
1;0;0;0;1;0;0;1
1;1;0;1;0;0;0;1
0;0;1;0;1;1;1;0
0;2;0;0;0;1;1;0
0;0;1;0;0;1;0;1
0;0;0;0;0;1;1;0
0;2;1;0;1;1;0;0
2;0;0;0;1;0;0;0
2;1;0;1;0;1;1;1
0;1;1;0;1;1;1;0
0;2;0;1;1;1;0;1
1;2;1;0;1;1;0;0
0;0;1;1;1;1;0;1
0;0;0;1;1;0;0;1
1;1;1;1;1;0;1;0
1;2;1;0;0;1;1;1
2;2;1;1;0;1;1;1
1;2;1;0;1;1;0;1
0;1;0;0;0;1;0;1
1;1;0;0;0;1;0;1
0;1;0;0;0;1;1;1
2;1;0;1;0;1;0;1
0;1;1;0;1;1;0;0
2;1;0;1;0;1;1;0
1;2;1;0;0;0;1;1
1;2;0;1;0;1;1;1
0;2;0;1;0;1;0;1
2;1;1;0;1;1;1;1
0;2;0;1;0;0;0;1
0;1;1;0;0;1;1;0
2;2;1;0;0;0;1;1
1;0;0;0;0;0;1;0
0;0;1;1;0;1;0;0
2;2;0;1;1;1;0;0
1;2;1;1;0;0;0;1
1;2;0;1;0;0;1;1
0;1;0;1;1;1;1;0
0;1;0;0;1;1;0;0
0;1;0;1;1;0;0;0
1;1;1;0;1;1;0;1
1;1;1;1;0;1;1;0
2;1;1;1;0;1;1;0
2;2;0;0;1;1;0;0
1;0;0;1;0;1;0;1
2;1;1;1;1;0;1;0
0;0;0;0;1;1;0;0
2;1;1;1;0;1;0;1
1;2;1;1;1;0;1;1
0;2;0;0;1;1;1;1
2;1;0;1;1;0;0;0
0;2;1;1;1;0;1;1
1;2;0;1;1;1;1;0
0;2;0;0;0;1;0;1
1;2;0;0;0;1;0;0
2;0;0;1;0;1;1;1
2;1;1;0;0;0;1;1
0;1;1;1;0;1;0;0
2;1;0;1;1;1;0;0
0;2;0;1;0;0;0;0
2;1;0;0;1;0;0;1
1;1;0;0;1;1;0;0
2;0;0;1;0;0;1;1
2;0;1;1;1;0;1;1
2;2;0;1;1;0;0;0
0;1;0;1;1;1;0;1
1;0;1;1;1;0;0;0
2;0;0;1;1;1;1;1
1;0;0;0;0;0;0;1
2;1;1;0;0;0;0;1
0;0;0;0;1;1;0;1
0;1;0;1;0;0;0;1
2;2;0;1;0;0;0;0
0;2;1;1;1;1;0;1
2;2;1;0;0;1;1;0
1;2;0;0;1;1;1;0
0;1;1;1;0;0;0;1
1;1;1;0;1;0;0;0
2;0;1;1;0;0;1;1
2;0;1;0;1;0;1;0
2;2;0;0;0;1;1;0
1;1;0;1;1;0;1;1
2;0;0;0;0;0;1;1
1;2;0;0;1;1;0;1
1;2;1;1;0;0;0;0
0;0;1;1;1;1;1;0
0;2;1;1;0;1;0;0
2;1;1;0;0;0;1;0
1;0;0;1;1;0;0;0
2;2;0;1;1;1;0;1
2;0;0;1;1;1;0;0
0;2;1;0;0;0;0;0
1;2;1;1;1;0;0;1
0;0;0;0;0;1;1;1
2;2;1;1;1;0;1;1
0;1;0;0;1;0;1;0
2;1;1;0;1;1;0;0
0;1;1;1;1;1;1;1
1;2;1;1;1;0;1;0
2;0;1;1;1;1;1;0
1;0;1;1;0;0;1;0
0;2;0;0;1;0;0;1
2;2;0;0;0;1;0;0
0;2;0;0;1;1;0;0
0;1;0;0;0;0;1;1
1;0;0;0;0;1;0;1
2;1;0;0;0;0;1;0
0;1;1;0;0;1;0;0
1;0;1;0;1;0;1;0
2;0;0;0;1;1;0;0
0;0;0;0;0;0;0;0
0;0;1;0;1;0;0;0
1;0;1;0;0;0;0;0
0;2;1;0;0;0;0;1
2;0;0;1;1;1;0;1
0;2;0;1;1;1;1;0
0;2;1;1;1;1;1;1
1;2;0;1;0;1;1;0
0;2;1;0;0;1;0;0
2;0;1;1;1;1;1;1
0;0;0;1;1;1;1;1
1;2;0;1;1;0;0;0
1;2;0;1;1;0;0;1
2;2;0;1;0;0;1;0
2;2;0;0;0;0;1;0
0;0;0;1;0;0;1;0
1;0;1;0;1;0;0;0
0;2;0;0;0;0;0;0
2;0;1;0;1;1;1;1
0;2;1;0;0;0;1;1
0;2;1;0;1;1;1;1
2;2;1;0;1;0;1;0
1;1;1;1;1;1;1;1
0;1;1;0;1;0;0;0
2;1;1;0;0;1;1;1
0;0;1;0;1;1;1;1
0;1;1;0;1;0;1;0
2;0;0;1;0;0;1;0
1;1;0;1;1;1;1;0
2;0;0;1;1;1;1;0
0;0;1;0;0;1;1;0
1;0;1;0;1;1;1;1
0;1;0;0;0;0;0;1
0;2;0;1;1;0;0;1
2;1;1;0;1;0;1;1
1;1;1;1;1;0;1;1
1;0;1;1;0;0;1;1
1;0;0;1;1;0;0;1
2;1;1;1;0;0;1;0
1;0;0;0;0;0;0;0
0;0;0;1;1;1;1;0
1;0;1;1;0;0;0;1
2;1;1;1;1;0;1;1
1;2;0;1;0;1;0;1
1;1;0;0;0;1;1;0
2;2;1;0;1;1;0;1
0;0;0;0;0;0;1;0
0;2;0;0;0;1;1;1
2;1;0;0;0;0;1;1
0;0;0;1;1;1;0;0
1;0;1;0;0;1;1;0
2;0;0;0;1;1;1;1
1;2;1;0;0;0;0;1
2;2;0;0;0;1;0;1
0;1;1;0;0;0;1;0
0;2;0;0;1;0;1;0
1;1;0;0;1;1;1;1
0;0;0;1;0;0;1;1
0;1;1;0;0;1;1;1
2;2;0;1;1;0;1;0
1;0;1;0;1;0;1;1
1;1;0;1;0;0;1;1
2;0;1;1;0;0;1;0
2;0;1;0;0;0;1;0
1;1;1;1;0;1;1;1
2;1;1;0;1;0;0;0
0;2;0;1;1;0;0;0
1;2;1;1;0;1;0;0
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