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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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{
"python.analysis.extraPaths": [
"./DecisionTree"
]
}

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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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@ -1,84 +0,0 @@
def read_text_document(file_path): # read given amount of lines (lines of 8 integers)
string_array = []
try:
with open(file_path, 'r') as file:
# Read each line of the document
for line in file:
# Remove trailing newline characters and append to the array
string_array.append(line.rstrip('\n'))
except FileNotFoundError:
print("File not found.")
return string_array # returns array of lines from the file
def gen_output(array, second_array): # transcribes line of integers into elemental decisions of shelf "g" and "d"
if array[0] == 0 or array[0] == 1:
second_array[0] = "g"
else:
second_array[0] = "d"
if array[1] == 1 or array[1] == 2:
second_array[1] = "d"
else:
second_array[1] = "g"
if array[2] == 0:
second_array[2] = "g"
else:
second_array[2] = "d"
if array[3] == 0:
second_array[3] = "g"
else:
second_array[3] = "d"
if array[4] == 0:
second_array[4] = "d"
else:
second_array[4] = "g"
if array[5] == 0:
second_array[5] = "g"
else:
second_array[5] = "d"
if array[6] == 0:
second_array[6] = "d"
else:
second_array[6] = "g"
if array[7] == 0:
second_array[7] = "d"
else:
second_array[7] = "g"
def count(array): # count number of g and d and make decision, if same number return 2 instead
d = 0
g = 0
for digit in array:
if digit == "g":
g += 1
else:
d += 1
if d > g:
return 0 # lower shelf
elif g > d:
return 1 # upper shelf
else:
return 2 # optimisation of space, goes to more empty shelf overall
file_path = 'file/path/to/input/lines/of/integers'
examples = read_text_document(file_path) # array of given number of examples from file
for input in examples:
digit_array = []
for char in input:
# Convert the character to an integer and add it to the array
digit_array.append(int(char))
output_array = [None] * 8
gen_output(digit_array, output_array)
decision_output = count(output_array)
if decision_output == 2: # in case d == g, check which shelf is more empty
if output_array[7] == "g":
decision_output = 1
elif output_array[7] == "d":
decision_output = 0
print(decision_output) # final decision

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@ -1,200 +0,0 @@
1
1
0
0
1
0
1
1
1
0
1
1
0
1
0
0
1
0
1
1
0
1
0
1
1
1
1
0
0
0
1
1
0
1
1
0
1
1
0
0
0
1
0
0
0
1
0
0
0
1
0
1
0
1
1
0
1
1
1
0
1
1
0
0
0
1
0
1
1
0
1
0
1
1
0
1
1
0
0
0
1
0
0
1
1
0
1
1
1
0
1
0
0
1
0
0
0
1
1
1
1
0
1
0
0
0
1
0
0
1
1
0
0
1
0
1
1
0
1
1
0
1
0
0
1
1
0
1
0
0
1
1
0
1
1
1
0
1
0
0
1
1
0
0
0
0
1
1
1
1
1
1
1
0
1
1
1
1
0
0
0
1
1
1
0
0
1
1
0
0
1
1
1
1
0
1
1
0
0
0
0
0
0
0
1
1
1
1
0
0
0
1
1
1
0
1
1
0
0
0

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@ -1,200 +0,0 @@
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
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@ -1,31 +0,0 @@
Epoch: 1 Train Loss: 65 Train Accuracy: 0.5754245754245755
Epoch: 2 Train Loss: 25 Train Accuracy: 0.7457542457542458
Epoch: 3 Train Loss: 8 Train Accuracy: 0.8431568431568431
Epoch: 4 Train Loss: 2 Train Accuracy: 0.9010989010989011
Epoch: 5 Train Loss: 1 Train Accuracy: 0.9335664335664335
Epoch: 6 Train Loss: 0 Train Accuracy: 0.9545454545454546
Epoch: 7 Train Loss: 0 Train Accuracy: 0.972027972027972
Epoch: 8 Train Loss: 0 Train Accuracy: 0.9820179820179821
Epoch: 9 Train Loss: 0 Train Accuracy: 0.994005994005994
Epoch: 10 Train Loss: 0 Train Accuracy: 0.9945054945054945
Epoch: 1 Train Loss: 42 Train Accuracy: 0.6428571428571429
Epoch: 2 Train Loss: 11 Train Accuracy: 0.8306693306693307
Epoch: 3 Train Loss: 3 Train Accuracy: 0.8921078921078921
Epoch: 4 Train Loss: 2 Train Accuracy: 0.8891108891108891
Epoch: 5 Train Loss: 1 Train Accuracy: 0.9335664335664335
Epoch: 6 Train Loss: 0 Train Accuracy: 0.952047952047952
Epoch: 7 Train Loss: 0 Train Accuracy: 0.9545454545454546
Epoch: 8 Train Loss: 0 Train Accuracy: 0.9655344655344655
Epoch: 9 Train Loss: 0 Train Accuracy: 0.9815184815184815
Epoch: 10 Train Loss: 0 Train Accuracy: 0.9805194805194806
Epoch: 11 Train Loss: 0 Train Accuracy: 0.9855144855144855
Epoch: 12 Train Loss: 0 Train Accuracy: 0.989010989010989
Epoch: 13 Train Loss: 0 Train Accuracy: 0.9925074925074925
Epoch: 14 Train Loss: 0 Train Accuracy: 0.9915084915084915
Epoch: 15 Train Loss: 0 Train Accuracy: 0.9885114885114885
Epoch: 16 Train Loss: 0 Train Accuracy: 0.994005994005994
Epoch: 17 Train Loss: 0 Train Accuracy: 0.997002997002997
Epoch: 18 Train Loss: 0 Train Accuracy: 0.9965034965034965
Epoch: 19 Train Loss: 0 Train Accuracy: 0.999000999000999
Epoch: 20 Train Loss: 0 Train Accuracy: 1.0

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@ -1,60 +0,0 @@
import glob
from src.torchvision_resize_dataset import combined_dataset, images_path, classes
import src.data_model
from torch.optim import Adam
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
train_loader = DataLoader(
combined_dataset, #dataset of images
batch_size=256, # accuracy
shuffle=True # rand order
)
model = src.data_model.DataModel(num_objects=2).to(device)
#optimizer
optimizer = Adam(model.parameters(), lr=0.001, weight_decay=0.0001)
#loss function
criterion = nn.CrossEntropyLoss()
num_epochs = 20
# train_size = len(glob.glob(images_path+'*.jpg'))
train_size = 2002
go_to_accuracy = 0.0
for epoch in range(num_epochs):
#training on dataset
model.train()
train_accuracy = 0.0
train_loss = 0.0
for i, (images, labels) in enumerate(train_loader):
if torch.cuda.is_available():
images = torch.Variable(images.cuda())
labels = torch.Variable(labels.cuda())
# clearing the optimizer gradients
optimizer.zero_grad()
outputs = model(images) # predoction
loss = criterion(outputs, labels) #loss calculation
loss.backward()
optimizer.step()
train_loss += loss.cpu().data*images.size(0)
_, prediction = torch.max(outputs.data, 1)
train_accuracy += int(torch.sum(prediction == labels.data))
train_accuracy = train_accuracy/train_size
train_loss = train_loss/train_size
model.eval()
print('Epoch: '+ str(epoch+1) +' Train Loss: '+ str(int(train_loss)) +' Train Accuracy: '+ str(train_accuracy))
if train_accuracy > go_to_accuracy:
go_to_accuracy= train_accuracy
torch.save(model.state_dict(), "best_model.pth")

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@ -1,147 +0,0 @@
import torch
import torch.nn as nn
from torchvision.transforms import transforms
import numpy as np
from torch.autograd import Variable
from torchvision.models import squeezenet1_1
import torch.functional as F
from io import open
import os
from PIL import Image
import pathlib
import glob
from tkinter import Tk, Label
from PIL import Image, ImageTk
absolute_path = os.path.abspath('NeuralNetwork/src/train_images')
train_path = absolute_path
absolute_path = os.path.abspath('Images/Items_test')
pred_path = absolute_path
root=pathlib.Path(train_path)
classes=sorted([j.name.split('/')[-1] for j in root.iterdir()])
class DataModel(nn.Module):
def __init__(self, num_classes):
super(DataModel, self).__init__()
#input (batch=256, nr of channels rgb=3 , size=244x244)
# convolution
self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1)
#shape (256, 12, 224x224)
# batch normalization
self.bn1 = nn.BatchNorm2d(num_features=12)
#shape (256, 12, 224x224)
self.reul1 = nn.ReLU()
self.pool=nn.MaxPool2d(kernel_size=2, stride=2)
# reduce image size by factor 2
# pooling window moves by 2 pixels at a time instead of 1
# shape (256, 12, 112x112)
self.conv2 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(num_features=24)
self.reul2 = nn.ReLU()
# shape (256, 24, 112x112)
self.conv3 = nn.Conv2d(in_channels=24, out_channels=48, kernel_size=3, stride=1, padding=1)
#shape (256, 48, 112x112)
self.bn3 = nn.BatchNorm2d(num_features=48)
#shape (256, 48, 112x112)
self.reul3 = nn.ReLU()
# connected layer
self.fc = nn.Linear(in_features=48*112*112, out_features=num_classes)
def forward(self, input):
output = self.conv1(input)
output = self.bn1(output)
output = self.reul1(output)
output = self.pool(output)
output = self.conv2(output)
output = self.bn2(output)
output = self.reul2(output)
output = self.conv3(output)
output = self.bn3(output)
output = self.reul3(output)
# output shape matrix (256, 48, 112x112)
#print(output.shape)
#print(self.fc.weight.shape)
output = output.view(-1, 48*112*112)
output = self.fc(output)
return output
script_dir = os.path.dirname(os.path.abspath(__file__))
file_path = os.path.join(script_dir, 'best_model.pth')
checkpoint=torch.load(file_path)
model = DataModel(num_classes=2)
model.load_state_dict(checkpoint)
model.eval()
transformer = transforms.Compose([
transforms.Resize((224, 224)), # Resize images to (224, 224)
transforms.ToTensor(), # Convert images to tensors, 0-255 to 0-1
# transforms.RandomHorizontalFlip(), # 0.5 chance to flip the image
transforms.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5])
])
def prediction(img_path,transformer):
image=Image.open(img_path)
image_tensor=transformer(image).float()
image_tensor=image_tensor.unsqueeze_(0)
if torch.cuda.is_available():
image_tensor.cuda()
input=Variable(image_tensor)
output=model(input)
index=output.data.numpy().argmax()
pred=classes[index]
return pred
def prediction_keys():
#funkcja zwracajaca sciezki do kazdego pliku w folderze w postaci listy
images_path=glob.glob(pred_path+'/*.jpg')
pred_list=[]
for i in images_path:
pred_list.append(i)
return pred_list
def predict_one(path):
#wyswietlanie obrazka po kazdym podniesieniu itemu
root = Tk()
root.title("Okno z obrazkiem")
image = Image.open(path)
photo = ImageTk.PhotoImage(image)
label = Label(root, image=photo)
label.pack()
root.mainloop()
#uruchamia sie funkcja spr czy obrazek to paczka czy list
pred_print = prediction(path,transformer)
print('Zdjecie jest: '+pred_print)
return pred_print

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@ -1,61 +0,0 @@
import torch.nn as nn
import torch
class DataModel(nn.Module):
def __init__(self, num_objects):
super(DataModel, self).__init__()
#input (batch=256, nr of channels rgb=3 , size=244x244)
# convolution
self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1)
#shape (256, 12, 224x224)
# batch normalization
self.bn1 = nn.BatchNorm2d(num_features=12)
#shape (256, 12, 224x224)
self.reul1 = nn.ReLU()
self.pool=nn.MaxPool2d(kernel_size=2, stride=2)
# reduce image size by factor 2
# pooling window moves by 2 pixels at a time instead of 1
# shape (256, 12, 112x112)
self.conv2 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(num_features=24)
self.reul2 = nn.ReLU()
# shape (256, 24, 112x112)
self.conv3 = nn.Conv2d(in_channels=24, out_channels=48, kernel_size=3, stride=1, padding=1)
#shape (256, 48, 112x112)
self.bn3 = nn.BatchNorm2d(num_features=48)
#shape (256, 48, 112x112)
self.reul3 = nn.ReLU()
# connected layer
self.fc = nn.Linear(in_features=48*112*112, out_features=num_objects)
def forward(self, input):
output = self.conv1(input)
output = self.bn1(output)
output = self.reul1(output)
output = self.pool(output)
output = self.conv2(output)
output = self.bn2(output)
output = self.reul2(output)
output = self.conv3(output)
output = self.bn3(output)
output = self.reul3(output)
# output shape matrix (256, 48, 112x112)
#print(output.shape)
#print(self.fc.weight.shape)
output = output.view(-1, 48*112*112)
output = self.fc(output)
return output

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import glob
import pathlib
import torchvision.transforms as transforms
from torchvision.datasets import ImageFolder
from torch.utils.data import ConcatDataset
# images have to be the same size for the algorithm to work
transform = transforms.Compose([
transforms.Resize((224, 224)), # Resize images to (224, 224)
transforms.ToTensor(), # Convert images to tensors, 0-255 to 0-1
# transforms.RandomHorizontalFlip(), # 0.5 chance to flip the image
transforms.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5])
])
letters_path = 'C:/Users/wojmed/Documents/VS repositories/Inteligentny_Wozek/NeuralNetwork/src/train_images/letters'
package_path = 'C:/Users/wojmed/Documents/VS repositories/Inteligentny_Wozek/NeuralNetwork/src/train_images/package'
images_path = 'C:/Users/wojmed/Documents/VS repositories/Inteligentny_Wozek/NeuralNetwork/src/train_images'
# # Load images from folders
# letter_folder = ImageFolder(letters_path, transform=transform)
# package_folder = ImageFolder(package_path, transform=transform)
# Combine the both datasets into a single dataset
#combined_dataset = ConcatDataset([letter_folder, package_folder])
combined_dataset = ImageFolder(images_path, transform=transform)
#image classes
path=pathlib.Path(images_path)
classes = sorted([i.name.split("/")[-1] for i in path.iterdir()])
# print(classes)

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