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decision-tree #17

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s473577 merged 3 commits from decision-tree into master 2023-05-28 13:44:43 +02:00
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# Byte-compiled / optimized / DLL files
__pycache__/
*.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/
.installed.cfg
*.egg
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
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .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
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.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
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/

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digraph Tree {
node [shape=box, style="filled, rounded", color="black", fontname="helvetica"] ;
edge [fontname="helvetica"] ;
0 [label="g > d <= 0.5\nentropy = 0.997\nsamples = 200\nvalue = [94, 106]\nclass = 1", fillcolor="#e9f4fc"] ;
1 [label="waga, <= 0.5\nentropy = 0.803\nsamples = 98\nvalue = [74, 24]\nclass = 0", fillcolor="#edaa79"] ;
0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
2 [label="wielkosc <= 1.5\nentropy = 0.998\nsamples = 34\nvalue = [16, 18]\nclass = 1", fillcolor="#e9f4fc"] ;
1 -> 2 ;
3 [label="priorytet <= 0.5\nentropy = 0.887\nsamples = 23\nvalue = [7, 16]\nclass = 1", fillcolor="#90c8f0"] ;
2 -> 3 ;
4 [label="kruchosc <= 0.5\nentropy = 0.439\nsamples = 11\nvalue = [1, 10]\nclass = 1", fillcolor="#4da7e8"] ;
3 -> 4 ;
5 [label="entropy = 0.0\nsamples = 7\nvalue = [0, 7]\nclass = 1", fillcolor="#399de5"] ;
4 -> 5 ;
6 [label="wielkosc <= 0.5\nentropy = 0.811\nsamples = 4\nvalue = [1, 3]\nclass = 1", fillcolor="#7bbeee"] ;
4 -> 6 ;
7 [label="ksztalt <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
6 -> 7 ;
8 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
7 -> 8 ;
9 [label="gorna <= 0.5\nentropy = 1.0\nsamples = 2\nvalue = [1, 1]\nclass = 0", fillcolor="#ffffff"] ;
7 -> 9 ;
10 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
9 -> 10 ;
11 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
9 -> 11 ;
12 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
6 -> 12 ;
13 [label="kruchosc <= 0.5\nentropy = 1.0\nsamples = 12\nvalue = [6, 6]\nclass = 0", fillcolor="#ffffff"] ;
3 -> 13 ;
14 [label="entropy = 0.0\nsamples = 5\nvalue = [5, 0]\nclass = 0", fillcolor="#e58139"] ;
13 -> 14 ;
15 [label="ksztalt <= 0.5\nentropy = 0.592\nsamples = 7\nvalue = [1, 6]\nclass = 1", fillcolor="#5aade9"] ;
13 -> 15 ;
16 [label="entropy = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = 1", fillcolor="#399de5"] ;
15 -> 16 ;
17 [label="gorna <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
15 -> 17 ;
18 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
17 -> 18 ;
19 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
17 -> 19 ;
20 [label="ksztalt <= 0.5\nentropy = 0.684\nsamples = 11\nvalue = [9, 2]\nclass = 0", fillcolor="#eb9d65"] ;
2 -> 20 ;
21 [label="dolna <= 0.5\nentropy = 1.0\nsamples = 4\nvalue = [2, 2]\nclass = 0", fillcolor="#ffffff"] ;
20 -> 21 ;
22 [label="kruchosc <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
21 -> 22 ;
23 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
22 -> 23 ;
24 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
22 -> 24 ;
25 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
21 -> 25 ;
26 [label="entropy = 0.0\nsamples = 7\nvalue = [7, 0]\nclass = 0", fillcolor="#e58139"] ;
20 -> 26 ;
27 [label="gorna <= 0.5\nentropy = 0.449\nsamples = 64\nvalue = [58, 6]\nclass = 0", fillcolor="#e88e4d"] ;
1 -> 27 ;
28 [label="entropy = 0.0\nsamples = 33\nvalue = [33, 0]\nclass = 0", fillcolor="#e58139"] ;
27 -> 28 ;
29 [label="wielkosc <= 1.5\nentropy = 0.709\nsamples = 31\nvalue = [25, 6]\nclass = 0", fillcolor="#eb9f69"] ;
27 -> 29 ;
30 [label="ksztalt <= 0.5\nentropy = 0.918\nsamples = 18\nvalue = [12, 6]\nclass = 0", fillcolor="#f2c09c"] ;
29 -> 30 ;
31 [label="kruchosc <= 0.5\nentropy = 1.0\nsamples = 10\nvalue = [5, 5]\nclass = 0", fillcolor="#ffffff"] ;
30 -> 31 ;
32 [label="dolna <= 0.5\nentropy = 0.722\nsamples = 5\nvalue = [4, 1]\nclass = 0", fillcolor="#eca06a"] ;
31 -> 32 ;
33 [label="priorytet <= 0.5\nentropy = 1.0\nsamples = 2\nvalue = [1, 1]\nclass = 0", fillcolor="#ffffff"] ;
32 -> 33 ;
34 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
33 -> 34 ;
35 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
33 -> 35 ;
36 [label="entropy = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = 0", fillcolor="#e58139"] ;
32 -> 36 ;
37 [label="dolna <= 0.5\nentropy = 0.722\nsamples = 5\nvalue = [1, 4]\nclass = 1", fillcolor="#6ab6ec"] ;
31 -> 37 ;
38 [label="entropy = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = 1", fillcolor="#399de5"] ;
37 -> 38 ;
39 [label="waga, <= 1.5\nentropy = 1.0\nsamples = 2\nvalue = [1, 1]\nclass = 0", fillcolor="#ffffff"] ;
37 -> 39 ;
40 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
39 -> 40 ;
41 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
39 -> 41 ;
42 [label="waga, <= 1.5\nentropy = 0.544\nsamples = 8\nvalue = [7, 1]\nclass = 0", fillcolor="#e99355"] ;
30 -> 42 ;
43 [label="entropy = 0.0\nsamples = 4\nvalue = [4, 0]\nclass = 0", fillcolor="#e58139"] ;
42 -> 43 ;
44 [label="wielkosc <= 0.5\nentropy = 0.811\nsamples = 4\nvalue = [3, 1]\nclass = 0", fillcolor="#eeab7b"] ;
42 -> 44 ;
45 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
44 -> 45 ;
46 [label="kruchosc <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [2, 1]\nclass = 0", fillcolor="#f2c09c"] ;
44 -> 46 ;
47 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
46 -> 47 ;
48 [label="priorytet <= 0.5\nentropy = 1.0\nsamples = 2\nvalue = [1, 1]\nclass = 0", fillcolor="#ffffff"] ;
46 -> 48 ;
49 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
48 -> 49 ;
50 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
48 -> 50 ;
51 [label="entropy = 0.0\nsamples = 13\nvalue = [13, 0]\nclass = 0", fillcolor="#e58139"] ;
29 -> 51 ;
52 [label="wielkosc <= 1.5\nentropy = 0.714\nsamples = 102\nvalue = [20, 82]\nclass = 1", fillcolor="#69b5eb"] ;
0 -> 52 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
53 [label="waga, <= 0.5\nentropy = 0.469\nsamples = 70\nvalue = [7, 63]\nclass = 1", fillcolor="#4fa8e8"] ;
52 -> 53 ;
54 [label="entropy = 0.0\nsamples = 21\nvalue = [0, 21]\nclass = 1", fillcolor="#399de5"] ;
53 -> 54 ;
55 [label="ksztalt <= 0.5\nentropy = 0.592\nsamples = 49\nvalue = [7, 42]\nclass = 1", fillcolor="#5aade9"] ;
53 -> 55 ;
56 [label="wielkosc <= 0.5\nentropy = 0.25\nsamples = 24\nvalue = [1, 23]\nclass = 1", fillcolor="#42a1e6"] ;
55 -> 56 ;
57 [label="entropy = 0.0\nsamples = 15\nvalue = [0, 15]\nclass = 1", fillcolor="#399de5"] ;
56 -> 57 ;
58 [label="kruchosc <= 0.5\nentropy = 0.503\nsamples = 9\nvalue = [1, 8]\nclass = 1", fillcolor="#52a9e8"] ;
56 -> 58 ;
59 [label="dolna <= 0.5\nentropy = 0.722\nsamples = 5\nvalue = [1, 4]\nclass = 1", fillcolor="#6ab6ec"] ;
58 -> 59 ;
60 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
59 -> 60 ;
61 [label="gorna <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
59 -> 61 ;
62 [label="priorytet <= 0.5\nentropy = 1.0\nsamples = 2\nvalue = [1, 1]\nclass = 0", fillcolor="#ffffff"] ;
61 -> 62 ;
63 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
62 -> 63 ;
64 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
62 -> 64 ;
65 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
61 -> 65 ;
66 [label="entropy = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = 1", fillcolor="#399de5"] ;
58 -> 66 ;
67 [label="kruchosc <= 0.5\nentropy = 0.795\nsamples = 25\nvalue = [6, 19]\nclass = 1", fillcolor="#78bced"] ;
55 -> 67 ;
68 [label="priorytet <= 0.5\nentropy = 0.98\nsamples = 12\nvalue = [5, 7]\nclass = 1", fillcolor="#c6e3f8"] ;
67 -> 68 ;
69 [label="dolna <= 0.5\nentropy = 0.764\nsamples = 9\nvalue = [2, 7]\nclass = 1", fillcolor="#72b9ec"] ;
68 -> 69 ;
70 [label="entropy = 0.0\nsamples = 5\nvalue = [0, 5]\nclass = 1", fillcolor="#399de5"] ;
69 -> 70 ;
71 [label="gorna <= 0.5\nentropy = 1.0\nsamples = 4\nvalue = [2, 2]\nclass = 0", fillcolor="#ffffff"] ;
69 -> 71 ;
72 [label="entropy = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = 0", fillcolor="#e58139"] ;
71 -> 72 ;
73 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
71 -> 73 ;
74 [label="entropy = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = 0", fillcolor="#e58139"] ;
68 -> 74 ;
75 [label="dolna <= 0.5\nentropy = 0.391\nsamples = 13\nvalue = [1, 12]\nclass = 1", fillcolor="#49a5e7"] ;
67 -> 75 ;
76 [label="entropy = 0.0\nsamples = 7\nvalue = [0, 7]\nclass = 1", fillcolor="#399de5"] ;
75 -> 76 ;
77 [label="gorna <= 0.5\nentropy = 0.65\nsamples = 6\nvalue = [1, 5]\nclass = 1", fillcolor="#61b1ea"] ;
75 -> 77 ;
78 [label="priorytet <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
77 -> 78 ;
79 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
78 -> 79 ;
80 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
78 -> 80 ;
81 [label="entropy = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = 1", fillcolor="#399de5"] ;
77 -> 81 ;
82 [label="gorna <= 0.5\nentropy = 0.974\nsamples = 32\nvalue = [13, 19]\nclass = 1", fillcolor="#c0e0f7"] ;
52 -> 82 ;
83 [label="kruchosc <= 0.5\nentropy = 0.65\nsamples = 12\nvalue = [10, 2]\nclass = 0", fillcolor="#ea9a61"] ;
82 -> 83 ;
84 [label="entropy = 0.0\nsamples = 7\nvalue = [7, 0]\nclass = 0", fillcolor="#e58139"] ;
83 -> 84 ;
85 [label="waga, <= 1.5\nentropy = 0.971\nsamples = 5\nvalue = [3, 2]\nclass = 0", fillcolor="#f6d5bd"] ;
83 -> 85 ;
86 [label="priorytet <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
85 -> 86 ;
87 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
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

57
DecisionTree/drzewo_decyzyjne.py Normal file
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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)

200
DecisionTree/training_data.txt Normal file
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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
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