forked from s444420/AL-2020
Merge remote-tracking branch 'origin/master'
# Conflicts: # .idea/misc.xml # .idea/workspace.xml # .idea/wozek.iml
This commit is contained in:
shaaqu
commit
9b083201e8
@ -3,5 +3,5 @@
|
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<component name="JavaScriptSettings">
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<option name="languageLevel" value="ES6" />
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</component>
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (AL-2020)" project-jdk-type="Python SDK" />
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7 (AL-2020)" project-jdk-type="Python SDK" />
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@ -1,34 +1,22 @@
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<<<<<<< HEAD
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<change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
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<change beforePath="$PROJECT_DIR$/Archiwum/frontend/js/main.js" beforeDir="false" afterPath="$PROJECT_DIR$/Archiwum/frontend/js/main.js" afterDir="false" />
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||||
<change beforePath="$PROJECT_DIR$/data.py" beforeDir="false" afterPath="$PROJECT_DIR$/data.py" afterDir="false" />
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<change beforePath="$PROJECT_DIR$/field.py" beforeDir="false" afterPath="$PROJECT_DIR$/field.py" afterDir="false" />
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||||
<change beforePath="$PROJECT_DIR$/main.py" beforeDir="false" afterPath="$PROJECT_DIR$/main.py" afterDir="false" />
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||||
=======
|
||||
<change afterPath="$PROJECT_DIR$/Assiging/datadict.py" afterDir="false" />
|
||||
<change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
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<change beforePath="$PROJECT_DIR$/Assiging/feature_hashing.py" beforeDir="false" afterPath="$PROJECT_DIR$/Assiging/feature_hashing.py" afterDir="false" />
|
||||
<change beforePath="$PROJECT_DIR$/data.py" beforeDir="false" afterPath="$PROJECT_DIR$/data.py" afterDir="false" />
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||||
>>>>>>> upstream/master
|
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</list>
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@ -42,22 +30,9 @@
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|
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@ -66,49 +41,31 @@
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|
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|
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|
||||
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|
||||
<recent name="C:\Users\Pawel Lukaszewicz\PycharmProjects\AL-2020\coder\dataset" />
|
||||
<key name="MoveFile.RECENT_KEYS">
|
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<recent name="D:\Studia\Projects\AL-2020" />
|
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|
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|
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|
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<configuration default="true" type="PythonConfigurationType" factoryName="Python">
|
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<module name="wozek" />
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<option name="INTERPRETER_OPTIONS" value="" />
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|
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|
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<option name="SCRIPT_NAME" value="" />
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<option name="PARAMETERS" value="" />
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<option name="SHOW_COMMAND_LINE" value="true" />
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<option name="REDIRECT_INPUT" value="false" />
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<configuration name="board" type="PythonConfigurationType" factoryName="Python" temporary="true">
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<module name="wozek" />
|
||||
<option name="INTERPRETER_OPTIONS" value="" />
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@ -131,6 +88,9 @@
|
||||
<option name="INPUT_FILE" value="" />
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<method v="2" />
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</configuration>
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<<<<<<< HEAD
|
||||
<configuration name="main" type="PythonConfigurationType" factoryName="Python">
|
||||
=======
|
||||
<configuration name="feature_hashing" type="PythonConfigurationType" factoryName="Python" temporary="true">
|
||||
<module name="wozek" />
|
||||
<option name="INTERPRETER_OPTIONS" value="" />
|
||||
@ -154,6 +114,7 @@
|
||||
<method v="2" />
|
||||
</configuration>
|
||||
<configuration name="main" type="PythonConfigurationType" factoryName="Python" temporary="true">
|
||||
>>>>>>> upstream/master
|
||||
<module name="wozek" />
|
||||
<option name="INTERPRETER_OPTIONS" value="" />
|
||||
<option name="PARENT_ENVS" value="true" />
|
||||
@ -175,64 +136,24 @@
|
||||
<option name="INPUT_FILE" value="" />
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<method v="2" />
|
||||
</configuration>
|
||||
<configuration name="rocognizer" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
|
||||
<module name="wozek" />
|
||||
<option name="INTERPRETER_OPTIONS" value="" />
|
||||
<option name="PARENT_ENVS" value="true" />
|
||||
<envs>
|
||||
<env name="PYTHONUNBUFFERED" value="1" />
|
||||
</envs>
|
||||
<option name="SDK_HOME" value="" />
|
||||
<option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/coder" />
|
||||
<option name="IS_MODULE_SDK" value="true" />
|
||||
<option name="ADD_CONTENT_ROOTS" value="true" />
|
||||
<option name="ADD_SOURCE_ROOTS" value="true" />
|
||||
<EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
|
||||
<option name="SCRIPT_NAME" value="$PROJECT_DIR$/coder/rocognizer.py" />
|
||||
<option name="PARAMETERS" value="" />
|
||||
<option name="SHOW_COMMAND_LINE" value="true" />
|
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|
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|
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|
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|
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<method v="2" />
|
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</configuration>
|
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|
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|
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|
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|
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|
||||
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|
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|
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|
||||
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|
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|
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|
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|
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||||
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|
||||
<<<<<<< HEAD
|
||||
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|
||||
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|
||||
<item itemvalue="Python.tescik" />
|
||||
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|
||||
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|
||||
<recent_temporary>
|
||||
<list>
|
||||
=======
|
||||
<item itemvalue="Python.board" />
|
||||
<item itemvalue="Python.main" />
|
||||
<item itemvalue="Python.feature_hashing" />
|
||||
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|
||||
<list>
|
||||
<item itemvalue="Python.feature_hashing" />
|
||||
<item itemvalue="Python.main" />
|
||||
>>>>>>> upstream/master
|
||||
<item itemvalue="Python.board" />
|
||||
<item itemvalue="Python.rocognizer" />
|
||||
<item itemvalue="Python.tescik" />
|
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@ -250,41 +171,17 @@
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<<<<<<< HEAD
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||||
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||||
=======
|
||||
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||||
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||||
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@ -306,110 +203,13 @@
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<updated>1589822196451</updated>
|
||||
>>>>>>> upstream/master
|
||||
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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<<<<<<< HEAD
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<SUITE FILE_PATH="coverage/wozek$board.coverage" NAME="board Coverage Results" MODIFIED="1589210811600" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
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<SUITE FILE_PATH="coverage/AL_2020$main.coverage" NAME="main Coverage Results" MODIFIED="1589729320403" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
|
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=======
|
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<SUITE FILE_PATH="coverage/AL_2020$main.coverage" NAME="main Coverage Results" MODIFIED="1589754127142" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
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>>>>>>> upstream/master
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|
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|
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|
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245
.idea/workspace.xml.orig
Normal file
245
.idea/workspace.xml.orig
Normal file
@ -0,0 +1,245 @@
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<<<<<<< HEAD
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|
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=======
|
||||
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|
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|
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<change beforePath="$PROJECT_DIR$/data.py" beforeDir="false" afterPath="$PROJECT_DIR$/data.py" afterDir="false" />
|
||||
>>>>>>> upstream/master
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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<option name="INTERPRETER_OPTIONS" value="" />
|
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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</configuration>
|
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<<<<<<< HEAD
|
||||
<configuration name="main" type="PythonConfigurationType" factoryName="Python">
|
||||
=======
|
||||
<configuration name="feature_hashing" type="PythonConfigurationType" factoryName="Python" temporary="true">
|
||||
<module name="wozek" />
|
||||
<option name="INTERPRETER_OPTIONS" value="" />
|
||||
<option name="PARENT_ENVS" value="true" />
|
||||
<envs>
|
||||
<env name="PYTHONUNBUFFERED" value="1" />
|
||||
</envs>
|
||||
<option name="SDK_HOME" value="" />
|
||||
<option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/Assiging" />
|
||||
<option name="IS_MODULE_SDK" value="true" />
|
||||
<option name="ADD_CONTENT_ROOTS" value="true" />
|
||||
<option name="ADD_SOURCE_ROOTS" value="true" />
|
||||
<EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
|
||||
<option name="SCRIPT_NAME" value="$PROJECT_DIR$/Assiging/feature_hashing.py" />
|
||||
<option name="PARAMETERS" value="" />
|
||||
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|
||||
<option name="EMULATE_TERMINAL" value="false" />
|
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<option name="MODULE_MODE" value="false" />
|
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<option name="REDIRECT_INPUT" value="false" />
|
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<option name="INPUT_FILE" value="" />
|
||||
<method v="2" />
|
||||
</configuration>
|
||||
<configuration name="main" type="PythonConfigurationType" factoryName="Python" temporary="true">
|
||||
>>>>>>> upstream/master
|
||||
<module name="wozek" />
|
||||
<option name="INTERPRETER_OPTIONS" value="" />
|
||||
<option name="PARENT_ENVS" value="true" />
|
||||
<envs>
|
||||
<env name="PYTHONUNBUFFERED" value="1" />
|
||||
</envs>
|
||||
<option name="SDK_HOME" value="" />
|
||||
<option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
|
||||
<option name="IS_MODULE_SDK" value="true" />
|
||||
<option name="ADD_CONTENT_ROOTS" value="true" />
|
||||
<option name="ADD_SOURCE_ROOTS" value="true" />
|
||||
<EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
|
||||
<option name="SCRIPT_NAME" value="$PROJECT_DIR$/main.py" />
|
||||
<option name="PARAMETERS" value="" />
|
||||
<option name="SHOW_COMMAND_LINE" value="false" />
|
||||
<option name="EMULATE_TERMINAL" value="false" />
|
||||
<option name="MODULE_MODE" value="false" />
|
||||
<option name="REDIRECT_INPUT" value="false" />
|
||||
<option name="INPUT_FILE" value="" />
|
||||
<method v="2" />
|
||||
</configuration>
|
||||
<list>
|
||||
<<<<<<< HEAD
|
||||
<item itemvalue="Python.main" />
|
||||
<item itemvalue="Python.board" />
|
||||
</list>
|
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<recent_temporary>
|
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<list>
|
||||
=======
|
||||
<item itemvalue="Python.board" />
|
||||
<item itemvalue="Python.main" />
|
||||
<item itemvalue="Python.feature_hashing" />
|
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</list>
|
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<recent_temporary>
|
||||
<list>
|
||||
<item itemvalue="Python.feature_hashing" />
|
||||
<item itemvalue="Python.main" />
|
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>>>>>>> upstream/master
|
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<item itemvalue="Python.board" />
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</list>
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</recent_temporary>
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=======
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File diff suppressed because it is too large
Load Diff
165
Raporty/Raport_444420.md
Normal file
165
Raporty/Raport_444420.md
Normal file
@ -0,0 +1,165 @@
|
||||
# Tomasz Kuczyński Raport - mcda
|
||||
|
||||
### Opis podprojektu
|
||||
Podprojekt ma za zadanie wybrać określoną liczbę najlepszych produktów z dostawy.
|
||||
Wykorzystuj on zmodyfikowaną motodę MCDA - Multiple Criteria Decision Analysis.
|
||||
|
||||
#### Uczenie modelu
|
||||
Zbiór uczenia składa się z zdefiniowania wartości atrybutów:
|
||||
|
||||
```javascript
|
||||
color = {'black': 16, 'gold': 10, 'purple': 5, 'brown': 7, 'blue': 12, 'white': 14, 'red': 13, 'orange': 11}
|
||||
shape = {'rectangle': 15, 'pack': 19, 'square': 9, 'jar': 7}
|
||||
size = {'small': 7, 'medium': 16, 'big': 13}
|
||||
```
|
||||
oraz ich wyliczenia:
|
||||
```javascript
|
||||
def sizeValue(X):
|
||||
if X.size == 'small':
|
||||
return X.mass/5
|
||||
if X.size == 'medium':
|
||||
return X.mass/10
|
||||
if X.size == 'big':
|
||||
return X.mass/20
|
||||
```
|
||||
|
||||
Składa się róznierz z parametrów:
|
||||
|
||||
`weights` - waga danego parametru
|
||||
|
||||
`q` - współczynik pomyłki (granicy błędu) pomiędzy atrybutami
|
||||
|
||||
`p` - maksymalna dopuszczalna różnica pomiędy atrybutami
|
||||
|
||||
```javascript
|
||||
parameters = {
|
||||
'color': {'weights': 3, 'q': 1, 'p': 5},
|
||||
'shape': {'weights': 4, 'q': 1, 'p': 6},
|
||||
'mass': {'weights': 0.5, 'q': 2, 'p': 10},
|
||||
'size': {'weights': 1, 'q': 1, 'p': 8}
|
||||
}
|
||||
```
|
||||
|
||||
#### Opis kodu w wspólnym projekcie
|
||||
W wspólnym projekcie w pliku main.py wywołuje funkcję wybierania 5 produktów z dostawy:
|
||||
|
||||
```javascript
|
||||
produsctsFromSupply = choseProducts(5)
|
||||
```
|
||||
|
||||
próbna dostawa jest zapisana w pliku supply.py:
|
||||
|
||||
```javascript
|
||||
def create_data_products():
|
||||
allProducts = []
|
||||
|
||||
product = Product('black', 'rectangle', 51, 'small')
|
||||
allProducts.append(product)
|
||||
product = Product('black', 'rectangle', 51, 'small')
|
||||
allProducts.append(product)
|
||||
product = Product('gold', 'pack', 100, 'big')
|
||||
allProducts.append(product)
|
||||
...
|
||||
return allProducts
|
||||
|
||||
```
|
||||
|
||||
Cały algorytm selekcji produktów znajduje się w pliku mcda.py.
|
||||
|
||||
Funkcja `getConcordance(gA, gB, q, p):` ma za zadanie wyliczyć `Concordance` czyli jakość produktu A względem produktu B, za pomocą wartości atrybutów A, B odpowiednio gA, gB oraz parametrów tego atrybutu q, p i zwrócić liczbę z zakresu [0,1].
|
||||
|
||||
```javascript
|
||||
def getConcordance(gA, gB, q, p):
|
||||
if gB <= gA + q:
|
||||
return 1
|
||||
if gB <= gA + p:
|
||||
return (p - gB + gA) / (p - q)
|
||||
return 0
|
||||
```
|
||||
|
||||
Funkcja `getAllTypeConcordance(A, B)` ma za zadanie wyliczyć wszystkie typy `Concordance` oraz wyliczyć ich średnią ważoną:
|
||||
|
||||
```javascript
|
||||
def getAllTypeConcordance(A, B):
|
||||
concordance = 0.0
|
||||
weight_sum = 0
|
||||
|
||||
parameter = parameters['color']
|
||||
w, q, p = parameter['weights'], parameter['q'], parameter['p']
|
||||
concordance += getConcordance(color[A.color], color[B.color], q, p) * w
|
||||
weight_sum += w
|
||||
|
||||
parameter = parameters['shape']
|
||||
w, q, p = parameter['weights'], parameter['q'], parameter['p']
|
||||
concordance += getConcordance(shape[A.shape], shape[B.shape], q, p) * w
|
||||
weight_sum += w
|
||||
...
|
||||
concordance /= weight_sum
|
||||
return concordance
|
||||
```
|
||||
|
||||
Funkcja `getConcordanceAllProducts()` ma za zadanie przy pomocy funkcji `getAllTypeConcordance(A, B)` wyliczyć `Concordance` dla wszystkich produktów i zwrócić wynik w postaci listy:
|
||||
|
||||
```javascript
|
||||
def getConcordanceAllProducts():
|
||||
C = []
|
||||
|
||||
for i in range(len(allProducts)):
|
||||
c = 0
|
||||
for j in range(len(allProducts)):
|
||||
if j==i:
|
||||
continue
|
||||
else:
|
||||
c += getAllTypeConcordance(allProducts[i], allProducts[j])
|
||||
c /= len(allProducts)-1
|
||||
C.append(c)
|
||||
return C
|
||||
```
|
||||
|
||||
Funkcja `choseProducts(number)` ma za zadanie wybrać `number` produktów z dostawy na podstawie listy `Concordance` zwróconej przez funkcję `getConcordanceAllProducts()`.
|
||||
|
||||
```javascript
|
||||
def choseProducts(number):
|
||||
C = getConcordanceAllProducts()
|
||||
products = []
|
||||
prev = -1
|
||||
if number > len(allProducts):
|
||||
return allProducts
|
||||
while number > 0:
|
||||
max = -1
|
||||
if prev == -1:
|
||||
max = 0
|
||||
for j in range(len(allProducts)):
|
||||
if prev == -1:
|
||||
if C[max]<C[j]:
|
||||
max = j
|
||||
elif max == -1:
|
||||
if C[prev]>C[j]:
|
||||
max = j
|
||||
elif C[max] < C[j] and C[j] < C[prev]:
|
||||
max = j
|
||||
prev = max
|
||||
for j in range(len(allProducts)):
|
||||
if C[max] == C[j]:
|
||||
products.append(allProducts[j])
|
||||
print(C[j])
|
||||
number -= 1
|
||||
if number == 0:
|
||||
break
|
||||
print(C)
|
||||
return products
|
||||
```
|
||||
|
||||
`print(C[j])` oraz `print(C)` służy do sprawdzenia poprawności algorytmu.
|
||||
|
||||
### Uruchomienie algorytmu i jego działanie
|
||||
Film przedstawiający uruchomienie aplikacji:
|
||||
https://www.youtube.com/watch?v=DmwDlJkjtjs
|
||||
|
||||
Parametry pojawiające się w terminalu:
|
||||
|
||||
1.Concordance `number` najlepszych produktów (`print(C[j])`).
|
||||
|
||||
2.Lista wszystkich Concordance ( `print(C)`).
|
||||
|
||||
|
||||
131
Raporty/raport_444428.md
Normal file
131
Raporty/raport_444428.md
Normal file
@ -0,0 +1,131 @@
|
||||
# Wojciech Łukasik - drzewa decyzyjne, algorytm CART
|
||||
|
||||
### Opis podprojektu
|
||||
Podprojekt implementuje tworzenie drzewa decyzyjnego w oparciu o algorytm CART
|
||||
(Classification And Regression Tree), które pomaga Agentowi w rozpoznaniu słodyczy na podstawie
|
||||
ich cech fizycznych (kolor, kształt, masa, rozmiar).
|
||||
|
||||
Wszystkie funkcje oraz klasy wykorzystywane w tym podprojekcie znajdują się w pliku decision_tree.py,
|
||||
dane uczące znajdują się w pliku data.py w liście learning_data
|
||||
|
||||
### Tworzenie drzewa decyzyjnego
|
||||
|
||||
Główną funkcją jest
|
||||
|
||||
`build_tree(rows)` która jak wskazuje nazwa tworzy drzewo.
|
||||
|
||||
Funkcja przyjmuje jako argument listę zawierającą zestaw danych, w tym przypadku będą to słodycze o różnych właściwościach.
|
||||
|
||||
```python
|
||||
def build_tree(rows):
|
||||
gain, question = find_best_split(rows)
|
||||
|
||||
if gain == 0:
|
||||
return Leaf(rows)
|
||||
|
||||
true_rows, false_rows = partition(rows, question)
|
||||
|
||||
true_branch = build_tree(true_rows)
|
||||
|
||||
false_branch = build_tree(false_rows)
|
||||
|
||||
return DecisionNode(question, true_branch, false_branch)
|
||||
```
|
||||
|
||||
Drzewo jest budowane w oparciu o najlepsze możlwe podziały (najbardziej korzystne 'pytanie', które można zadać).
|
||||
Zajmuje się tym funkcja
|
||||
|
||||
`find_best_split(rows)` która dla wszystkich właściwości przekazanego zestawu informacji
|
||||
wylicza dla nich 'zysk informacji'.
|
||||
|
||||
Jeżeli nie otrzymujemy żadnych informacji (gain == 0) to znaczy, że znajdujemy
|
||||
się w liściu drzewa.
|
||||
|
||||
```python
|
||||
def find_best_split(rows):
|
||||
best_gain = 0
|
||||
best_question = None
|
||||
current_uncertainty = gini(rows)
|
||||
n_features = len(rows[0]) - 1
|
||||
|
||||
for col in range(n_features):
|
||||
values = set([row[col] for row in rows])
|
||||
|
||||
for val in values:
|
||||
question = Question(col, val)
|
||||
|
||||
true_rows, false_rows = partition(rows, question)
|
||||
|
||||
if len(true_rows) == 0 or len(false_rows) == 0:
|
||||
continue
|
||||
|
||||
gain = info_gain(true_rows, false_rows, current_uncertainty)
|
||||
|
||||
if gain > best_gain:
|
||||
best_gain, best_question = gain, question
|
||||
|
||||
return best_gain, best_question
|
||||
```
|
||||
|
||||
Zysk informacji z danego podziału otrzymujemy obliczając wartość 'Gini Impurity'. Jest to miara tego jak często losowo
|
||||
wybrany element zbioru byłby źle skategoryzowany, gdyby przypisać mu losową kategorię spośród wszystkich kategorii
|
||||
znajdujących się w danym zbiorze.
|
||||
|
||||
```python
|
||||
def gini(rows):
|
||||
counts = class_counts(rows)
|
||||
impurity = 1
|
||||
for lbl in counts:
|
||||
prob_of_lbl = counts[lbl] / float(len(rows))
|
||||
impurity -= prob_of_lbl ** 2
|
||||
return impurity
|
||||
```
|
||||
`class_counts(rows)` to funkcja, która dla danego zestawu danych zwraca wszystkie unikalne 'kategorie' oraz liczbę ich wystąpień.
|
||||
|
||||
Dla przykładu, w zestawie w którym wszystkie elementy podchodzą pod tę samą kategorię wartość Gini Impurity będzie równa zero, natomiast w zbiorze w którym znajdują się dwie kategorie wartość ta wyniesie 0,5. Im więcej różnych kategorii tym bardziej wartość Gini Impurity będzie zbliżała się do 1.
|
||||
|
||||
Po znalezieniu najbardziej optymalnego pytania, algorytm dzieli zestaw na elementy, dla których pytanie jest prawdziwe
|
||||
(true_rows), oraz te dla których jest fałszywe (false_rows). Następnie wykonuje rekurencyjnie procedurę `build_tree` dla obu poddrzew tak długo aż nie dojdzie do liści.
|
||||
|
||||
Element o zadanym zestawie cech, zostaje odnaleziony w drzewie dzięki prostej procedurze
|
||||
|
||||
`classify(row, node)` 'row' to lista cech elementu, natomiast 'node' na początu jest korzeniem już zbudowanego drzewa.
|
||||
|
||||
Element jest odnaleziony dzięki
|
||||
rekurencyjnym porównaniom atrybutów elementu z pytaniami w kolejnych węzłach drzewa.
|
||||
|
||||
```python
|
||||
def classify(row, node):
|
||||
if isinstance(node, Leaf):
|
||||
return node.predicions
|
||||
|
||||
if node.question.match(row):
|
||||
return classify(row, node.true_branch)
|
||||
else:
|
||||
return classify(row, node.false_branch)
|
||||
```
|
||||
|
||||
### Zestaw uczący
|
||||
|
||||
Zestaw budujący drzewo to lista zawierająca 27 przykładowych słodyczy. Ich atrybuty zapisane są w formacie ['kolor',
|
||||
'kształt', 'masa', 'wielkość', 'nazwa']. Oczywiście przy wyszukiwaniu elementu w drzewie jego nazwa nie jest potrzebna
|
||||
ponieważ to jej szukamy. Przykładowe elementy z zestawu uczącego:
|
||||
|
||||
```python
|
||||
['black', 'rectangle', 51, 'small', 'Mars'],
|
||||
['gold', 'pack', 100, 'big', 'Haribo'],
|
||||
['purple', 'rectangle', 100, 'big', 'Milka'],
|
||||
['brown', 'pack', 45, 'small', 'M&M'],
|
||||
```
|
||||
|
||||
### Implementacja w projekcie
|
||||
|
||||
Przy rozpoczęciu głównej pętli programu w pliku `main.py` drzewo `my_tree` zostaje zbudowane w oparciu o dane
|
||||
`data.learning_data`.
|
||||
|
||||
Gdy program już działa, po wciśnięciu `spacji` jeden ze słodyczy zostanie losowo wybrany z zestawu `data.learning_data`
|
||||
oraz umieszczony na polu `board[9][0]`, a jego nazwa zostanie wypisana w konsoli. Następnie Agent przemieszcza się do
|
||||
punktu `board[9][0]` i rozpoczne procedurę wyszukiwania elementu w zbudowanym drzewie. Na końcu wypisze w
|
||||
konsoli nazwę produktu.
|
||||
|
||||
|
||||
@ -93,7 +93,6 @@ let winner = 0;
|
||||
let current = openSet[winner];
|
||||
```
|
||||
|
||||
|
||||
### Definicja przyjętej heurystyki
|
||||
Jest to szacowana odległość od sprawdzanego pola do celu. Obliczana jest jako `Manhattan Distance`, ponieważ w naszym modelu Agent nie może poruszać się po skosie.
|
||||
|
||||
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9
candy.py
Normal file
9
candy.py
Normal file
@ -0,0 +1,9 @@
|
||||
|
||||
|
||||
class Candy:
|
||||
def __init__(self, producent, type, price):
|
||||
|
||||
self.producent = producent
|
||||
self.type = type
|
||||
self.price = price
|
||||
|
||||
32
data.py
32
data.py
@ -28,6 +28,38 @@ def create_data_sweets():
|
||||
return allProducts
|
||||
|
||||
|
||||
learning_data = [
|
||||
# kolor, kształt, waga, rozmiar, nazwa
|
||||
['black', 'rectangle', 51, 'small', 'Mars'],
|
||||
['gold', 'pack', 100, 'big', 'Haribo'],
|
||||
['purple', 'rectangle', 100, 'big', 'Milka'],
|
||||
['brown', 'pack', 45, 'small', 'M&M'],
|
||||
['blue', 'rectangle', 50, 'medium', 'Bounty'],
|
||||
['blue', 'square', 40, 'small', 'Knoppers'],
|
||||
['blue', 'rectangle', 35, 'small', 'Milky-way'],
|
||||
['gold', 'rectangle', 40, 'medium', 'Twix'],
|
||||
['gold', 'rectangle', 50, 'medium', 'Prince-polo'],
|
||||
['brown', 'rectangle', 55, 'medium', 'Snickers'],
|
||||
['brown', 'rectangle', 45, 'medium', 'Lion'],
|
||||
['white', 'rectangle', 40, 'medium', 'Kinder-bueno'],
|
||||
['red', 'rectangle', 50, 'medium', 'Kit-kat'],
|
||||
['blue', 'rectangle', 115, 'big', 'Wedel'],
|
||||
['white', 'rectangle', 15, 'small', 'Krowka'],
|
||||
['red', 'pack', 70, 'medium', 'Skittles'],
|
||||
['orange', 'rectangle', 45, 'medium', 'Reeses'],
|
||||
['blue', 'rectangle', 55, 'medium', 'Oreo'],
|
||||
['gold', 'rectangle', 120, 'big', 'Ferrero-rocher'],
|
||||
['white', 'rectangle', 120, 'big', 'Rafaello'],
|
||||
['white', 'jar', 600, 'big', 'Nutella'],
|
||||
['white', 'rectangle', 25, 'small', 'Duplo'],
|
||||
['brown', 'jar', 500, 'big', 'GoOn'],
|
||||
['brown', 'jar', 470, 'big', 'Active Orzechowe'],
|
||||
['red', 'jar', 250, 'medium', 'Strawberry Jam'],
|
||||
['black', 'jar', 250, 'medium', 'Blackberry Jam'],
|
||||
['orange', 'jar', 250, 'medium', 'Peach Jam'],
|
||||
]
|
||||
|
||||
|
||||
def create_data_dict():
|
||||
products_as_dict = []
|
||||
|
||||
|
||||
91
data.py.orig
Normal file
91
data.py.orig
Normal file
@ -0,0 +1,91 @@
|
||||
from sweets import Sweets
|
||||
|
||||
|
||||
def create_data_sweets():
|
||||
allProducts = []
|
||||
|
||||
sweet = Sweets('Mars', 'czekoladowy', 'baton', 'sredni', 2.49)
|
||||
allProducts.append(sweet)
|
||||
sweet = Sweets('Mars', 'czekoladowy', 'czekolada', 'duzy', 4.99)
|
||||
allProducts.append(sweet)
|
||||
sweet = Sweets('Mars', 'czekoladowy', 'czekolada', 'ogromny', 11.26)
|
||||
allProducts.append(sweet)
|
||||
sweet = Sweets('M&M', 'czekoladowy', 'czekolada', 'duzy', 3.99)
|
||||
allProducts.append(sweet)
|
||||
sweet = Sweets('M&M', 'czekoladowy', 'baton', 'sredni', 2.89)
|
||||
allProducts.append(sweet)
|
||||
sweet = Sweets('Nestle', 'bananowy', 'landrynka', 'maly', 0.39)
|
||||
allProducts.append(sweet)
|
||||
sweet = Sweets('Nestle', 'truskawkowy', 'landrynka', 'maly', 0.39)
|
||||
allProducts.append(sweet)
|
||||
sweet = Sweets('Nestle', 'cola', 'landrynka', 'maly', 0.49)
|
||||
allProducts.append(sweet)
|
||||
sweet = Sweets('Wedel', 'czekoladowy', 'baton', 'sredni', 1.99)
|
||||
allProducts.append(sweet)
|
||||
sweet = Sweets('Maoam', 'truskawkowy', 'guma', 'maly', 0.25)
|
||||
allProducts.append(sweet)
|
||||
|
||||
<<<<<<< HEAD
|
||||
return allProducts
|
||||
|
||||
|
||||
learning_data = [
|
||||
# kolor, kształt, waga, rozmiar, nazwa
|
||||
['black', 'rectangle', 51, 'small', 'Mars'],
|
||||
['gold', 'pack', 100, 'big', 'Haribo'],
|
||||
['purple', 'rectangle', 100, 'big', 'Milka'],
|
||||
['brown', 'pack', 45, 'small', 'M&M'],
|
||||
['blue', 'rectangle', 50, 'medium', 'Bounty'],
|
||||
['blue', 'square', 40, 'small', 'Knoppers'],
|
||||
['blue', 'rectangle', 35, 'small', 'Milky-way'],
|
||||
['gold', 'rectangle', 40, 'medium', 'Twix'],
|
||||
['gold', 'rectangle', 50, 'medium', 'Prince-polo'],
|
||||
['brown', 'rectangle', 55, 'medium', 'Snickers'],
|
||||
['brown', 'rectangle', 45, 'medium', 'Lion'],
|
||||
['white', 'rectangle', 40, 'medium', 'Kinder-bueno'],
|
||||
['red', 'rectangle', 50, 'medium', 'Kit-kat'],
|
||||
['blue', 'rectangle', 115, 'big', 'Wedel'],
|
||||
['white', 'rectangle', 15, 'small', 'Krowka'],
|
||||
['red', 'pack', 70, 'medium', 'Skittles'],
|
||||
['orange', 'rectangle', 45, 'medium', 'Reeses'],
|
||||
['blue', 'rectangle', 55, 'medium', 'Oreo'],
|
||||
['gold', 'rectangle', 120, 'big', 'Ferrero-rocher'],
|
||||
['white', 'rectangle', 120, 'big', 'Rafaello'],
|
||||
['white', 'jar', 600, 'big', 'Nutella'],
|
||||
['white', 'rectangle', 25, 'small', 'Duplo'],
|
||||
['brown', 'jar', 500, 'big', 'GoOn'],
|
||||
['brown', 'jar', 470, 'big', 'Active Orzechowe'],
|
||||
['red', 'jar', 250, 'medium', 'Strawberry Jam'],
|
||||
['black', 'jar', 250, 'medium', 'Blackberry Jam'],
|
||||
['orange', 'jar', 250, 'medium', 'Peach Jam'],
|
||||
]
|
||||
=======
|
||||
return allProducts
|
||||
|
||||
|
||||
def create_data_dict():
|
||||
products_as_dict = []
|
||||
|
||||
sweet = Sweets('Mars', 'czekoladowy', 'baton', 'sredni', 2.49)
|
||||
products_as_dict.append(vars(sweet))
|
||||
sweet = Sweets('Mars', 'czekoladowy', 'czekolada', 'duzy', 4.99)
|
||||
products_as_dict.append(vars(sweet))
|
||||
sweet = Sweets('Mars', 'czekoladowy', 'czekolada', 'ogromny', 11.26)
|
||||
products_as_dict.append(vars(sweet))
|
||||
sweet = Sweets('M&M', 'czekoladowy', 'czekolada', 'duzy', 3.99)
|
||||
products_as_dict.append(vars(sweet))
|
||||
sweet = Sweets('M&M', 'czekoladowy', 'baton', 'sredni', 2.89)
|
||||
products_as_dict.append(vars(sweet))
|
||||
sweet = Sweets('Nestle', 'bananowy', 'landrynka', 'maly', 0.39)
|
||||
products_as_dict.append(vars(sweet))
|
||||
sweet = Sweets('Nestle', 'truskawkowy', 'landrynka', 'maly', 0.39)
|
||||
products_as_dict.append(vars(sweet))
|
||||
sweet = Sweets('Nestle', 'cola', 'landrynka', 'maly', 0.49)
|
||||
products_as_dict.append(vars(sweet))
|
||||
sweet = Sweets('Wedel', 'czekoladowy', 'baton', 'sredni', 1.99)
|
||||
products_as_dict.append(vars(sweet))
|
||||
sweet = Sweets('Maoam', 'truskawkowy', 'guma', 'maly', 0.25)
|
||||
products_as_dict.append(vars(sweet))
|
||||
|
||||
return products_as_dict
|
||||
>>>>>>> upstream/master
|
||||
184
decision_tree.py
Normal file
184
decision_tree.py
Normal file
@ -0,0 +1,184 @@
|
||||
import data
|
||||
|
||||
training_data = data.learning_data
|
||||
|
||||
header = ['color', 'shape', 'weight', 'size', 'name']
|
||||
|
||||
|
||||
# funkcja która zwraca listę unikalnych wartości z każdej kolumny
|
||||
def uniqie_vals(rows, col):
|
||||
return set([row[col] for row in rows])
|
||||
|
||||
|
||||
# zliczamy liczbę wystąpień danego typu w zestawie danych
|
||||
def class_counts(rows):
|
||||
counts = {} # label -> count
|
||||
|
||||
for row in rows:
|
||||
name = row[-1]
|
||||
if name not in counts:
|
||||
counts[name] = 0
|
||||
counts[name] += 1
|
||||
return counts
|
||||
|
||||
|
||||
# funkcja do sprawdzania czy wartość jest wartością numeryczną
|
||||
def is_numeric(val):
|
||||
return isinstance(val, int) or isinstance(val, float)
|
||||
|
||||
|
||||
# klasa do zadawania pytań
|
||||
class Question:
|
||||
def __init__(self, column, value):
|
||||
self.column = column
|
||||
self.value = value
|
||||
|
||||
def match(self, example):
|
||||
val = example[self.column]
|
||||
|
||||
if is_numeric(val):
|
||||
return val >= self.value
|
||||
else:
|
||||
return val == self.value
|
||||
|
||||
def __repr__(self):
|
||||
condition = '=='
|
||||
if is_numeric(self.value):
|
||||
condition = '>='
|
||||
return "Is %s %s %s?" % (header[self.column], condition, str(self.value))
|
||||
|
||||
|
||||
def partition(rows, question):
|
||||
""" podział zbioru informacji
|
||||
dla każdego rzędu w zbiorze, sprawdź czy zgadza się z pytaniem, jeśli tak
|
||||
dodaj do 'true' inaczej dodaj do 'false' """
|
||||
true_rows, false_rows = [], []
|
||||
for row in rows:
|
||||
if question.match(row):
|
||||
true_rows.append(row)
|
||||
else:
|
||||
false_rows.append(row)
|
||||
return true_rows, false_rows
|
||||
|
||||
|
||||
def gini(rows):
|
||||
""" Gini impurity is a measure of how often a randomly chosen element from
|
||||
the set would be incorrectly labeled if it was randomly labeled according to
|
||||
the distribution of labels in the subset. """
|
||||
|
||||
counts = class_counts(rows)
|
||||
impurity = 1
|
||||
for lbl in counts:
|
||||
prob_of_lbl = counts[lbl] / float(len(rows))
|
||||
impurity -= prob_of_lbl ** 2
|
||||
return impurity
|
||||
|
||||
|
||||
def info_gain(left, right, current_uncertainty):
|
||||
p = float(len(left)) / (len(left) + len(right))
|
||||
return current_uncertainty - p * gini(left) - (1 - p) * gini(right)
|
||||
|
||||
|
||||
def find_best_split(rows):
|
||||
""" znajdź najlepsze możliwe pytanie do zadania, sprawdzając wszystkie
|
||||
właściwośći oraz licząc dla nich 'info_gain' """
|
||||
best_gain = 0
|
||||
best_question = None
|
||||
current_uncertainty = gini(rows)
|
||||
n_features = len(rows[0]) - 1
|
||||
|
||||
for col in range(n_features):
|
||||
values = set([row[col] for row in rows])
|
||||
|
||||
for val in values:
|
||||
question = Question(col, val)
|
||||
|
||||
true_rows, false_rows = partition(rows, question)
|
||||
|
||||
if len(true_rows) == 0 or len(false_rows) == 0:
|
||||
continue
|
||||
|
||||
gain = info_gain(true_rows, false_rows, current_uncertainty)
|
||||
|
||||
if gain > best_gain:
|
||||
best_gain, best_question = gain, question
|
||||
|
||||
return best_gain, best_question
|
||||
|
||||
|
||||
class Leaf:
|
||||
def __init__(self, rows):
|
||||
self.predicions = class_counts(rows)
|
||||
|
||||
|
||||
class DecisionNode:
|
||||
def __init__(self, question, true_branch, false_branch):
|
||||
self.question = question
|
||||
self.true_branch = true_branch
|
||||
self.false_branch = false_branch
|
||||
|
||||
|
||||
def build_tree(rows):
|
||||
gain, question = find_best_split(rows)
|
||||
|
||||
if gain == 0:
|
||||
return Leaf(rows)
|
||||
|
||||
true_rows, false_rows = partition(rows, question)
|
||||
|
||||
true_branch = build_tree(true_rows)
|
||||
|
||||
false_branch = build_tree(false_rows)
|
||||
|
||||
return DecisionNode(question, true_branch, false_branch)
|
||||
|
||||
|
||||
def print_tree(node, spacing=""):
|
||||
if isinstance(node, Leaf):
|
||||
print(spacing + "Predict", node.predicions)
|
||||
|
||||
else:
|
||||
print(spacing + str(node.question))
|
||||
|
||||
print(spacing + '--> True:')
|
||||
print_tree(node.true_branch, spacing + " ")
|
||||
|
||||
print(spacing + '--> False:')
|
||||
print_tree(node.false_branch, spacing + " ")
|
||||
|
||||
|
||||
def classify(row, node):
|
||||
if isinstance(node, Leaf):
|
||||
return node.predicions
|
||||
|
||||
if node.question.match(row):
|
||||
return classify(row, node.true_branch)
|
||||
else:
|
||||
return classify(row, node.false_branch)
|
||||
|
||||
|
||||
def print_leaf(counts):
|
||||
probs = []
|
||||
for lbl in counts.keys():
|
||||
probs.append(lbl)
|
||||
return probs
|
||||
|
||||
|
||||
# my_tree = build_tree(training_data)
|
||||
#
|
||||
# print_tree(my_tree)
|
||||
#
|
||||
# testing_data = [
|
||||
# ['gold', 'rectangle', 50, 'medium', 'Name'],
|
||||
# ['brown', 'rectangle', 55, 'medium', 'Snickers'],
|
||||
# ['white', 'rectangle', 120, 'big', 'Name']
|
||||
# ]
|
||||
#
|
||||
# test = ['white', 'rectangle', 120, 'big', 'Name']
|
||||
#
|
||||
# # for row in testing_data:
|
||||
# # print(print_leaf(classify(row, my_tree)))
|
||||
#
|
||||
# wynik = print_leaf(classify(test, my_tree))[0]
|
||||
# print(wynik)
|
||||
|
||||
4
field.py
4
field.py
@ -23,6 +23,9 @@ class Field:
|
||||
self.f = 0
|
||||
self.previous = None
|
||||
|
||||
# Przedmiot, który podnosi agent
|
||||
self.item = []
|
||||
|
||||
# Te rzeczy są potrzebne do wyświetlenia pola
|
||||
self.image = pygame.image.load('img/Field.png')
|
||||
self.rect = self.image.get_rect()
|
||||
@ -30,7 +33,6 @@ class Field:
|
||||
|
||||
self.rect.center = (center_x, center_y)
|
||||
|
||||
|
||||
# Metoda do wyświetlania pola na ekranie
|
||||
def blitme(self):
|
||||
self.screen.blit(self.image, self.rect)
|
||||
|
||||
31
main.py
31
main.py
@ -2,12 +2,14 @@ import pygame
|
||||
import functions
|
||||
import sys
|
||||
import time
|
||||
import decision_tree
|
||||
import data
|
||||
|
||||
from agent import Agent
|
||||
from settings import Settings
|
||||
from board import create_board, draw_board
|
||||
from random import randint
|
||||
|
||||
from random import randint, choice
|
||||
from mcda import choseProducts
|
||||
|
||||
# Inicjalizacja programu i utworzenie obiektu ekrany
|
||||
def run():
|
||||
@ -17,10 +19,13 @@ def run():
|
||||
pygame.display.set_caption("Inteligentny wózek widłowy")
|
||||
agent = Agent(screen, 50, 50, "Down")
|
||||
board = create_board(screen)
|
||||
my_tree = decision_tree.build_tree(data.learning_data)
|
||||
|
||||
for row in board:
|
||||
for field in row:
|
||||
print(field.cost_of_travel)
|
||||
produsctsFromSupply = choseProducts(5)
|
||||
|
||||
# for row in board:
|
||||
# for field in row:
|
||||
# print(field.cost_of_travel)
|
||||
|
||||
path = []
|
||||
next_step = None
|
||||
@ -41,7 +46,9 @@ def run():
|
||||
agent.move_forward(board)
|
||||
print(agent.x, agent.y)
|
||||
elif event.key == pygame.K_SPACE:
|
||||
field = board[randint(0, 9)][randint(0, 9)]
|
||||
board[9][0].item = choice(data.learning_data)
|
||||
print("Wybrano: " + board[9][0].item[-1])
|
||||
field = board[9][0]
|
||||
if not field.is_shelf:
|
||||
path = functions.a_star(board[agent.y][agent.x], field, board)
|
||||
path.pop(len(path) - 1)
|
||||
@ -56,17 +63,19 @@ def run():
|
||||
next_step = path.pop()
|
||||
else:
|
||||
next_step = None
|
||||
print(next_step, path)
|
||||
# print(next_step, path)
|
||||
for row in board:
|
||||
for field in row:
|
||||
if not field.is_shelf:
|
||||
field.image = pygame.image.load('img/Field.png')
|
||||
for row in board:
|
||||
for field in row:
|
||||
print(field.g, field.h, field.f, field.previous)
|
||||
|
||||
else:
|
||||
functions.change_turn(agent, next_step)
|
||||
print(agent.x, agent.y)
|
||||
|
||||
if board[agent.y][agent.x].item:
|
||||
prediction = decision_tree.print_leaf(decision_tree.classify(board[agent.y][agent.x].item, my_tree))
|
||||
print("Agent uważa, że przedmiot to: " + prediction[0])
|
||||
board[agent.y][agent.x].item = []
|
||||
|
||||
draw_board(board)
|
||||
agent.blitme()
|
||||
|
||||
99
mcda.py
Normal file
99
mcda.py
Normal file
@ -0,0 +1,99 @@
|
||||
from supply import *
|
||||
|
||||
allProducts = create_data_products()
|
||||
|
||||
color = {'black': 16, 'gold': 10, 'purple': 5, 'brown': 7, 'blue': 12, 'white': 14, 'red': 13, 'orange': 11}
|
||||
shape = {'rectangle': 15, 'pack': 19, 'square': 9, 'jar': 7}
|
||||
size = {'small': 7, 'medium': 16, 'big': 13}
|
||||
|
||||
def sizeValue(X):
|
||||
if X.size == 'small':
|
||||
return X.mass/5
|
||||
if X.size == 'medium':
|
||||
return X.mass/10
|
||||
if X.size == 'big':
|
||||
return X.mass/20
|
||||
parameters = {
|
||||
'color': {'weights': 3, 'q': 1, 'p': 5},
|
||||
'shape': {'weights': 4, 'q': 1, 'p': 6},
|
||||
'mass': {'weights': 0.5, 'q': 2, 'p': 10},
|
||||
'size': {'weights': 1, 'q': 1, 'p': 8}
|
||||
|
||||
}
|
||||
def getConcordance(gA, gB, q, p):
|
||||
if gB <= gA + q:
|
||||
return 1
|
||||
if gB <= gA + p:
|
||||
return (p - gB + gA) / (p - q)
|
||||
return 0
|
||||
|
||||
def getAllTypeConcordance(A, B):
|
||||
concordance = 0.0
|
||||
weight_sum = 0
|
||||
|
||||
parameter = parameters['color']
|
||||
w, q, p = parameter['weights'], parameter['q'], parameter['p']
|
||||
concordance += getConcordance(color[A.color], color[B.color], q, p) * w
|
||||
weight_sum += w
|
||||
|
||||
parameter = parameters['shape']
|
||||
w, q, p = parameter['weights'], parameter['q'], parameter['p']
|
||||
concordance += getConcordance(shape[A.shape], shape[B.shape], q, p) * w
|
||||
weight_sum += w
|
||||
|
||||
parameter = parameters['mass']
|
||||
w, q, p = parameter['weights'], parameter['q'], parameter['p']
|
||||
concordance += getConcordance(sizeValue(A), sizeValue(B), q, p) * w
|
||||
weight_sum += w
|
||||
|
||||
parameter = parameters['size']
|
||||
w, q, p = parameter['weights'], parameter['q'], parameter['p']
|
||||
concordance += getConcordance(size[A.size], size[B.size], q, p) * w
|
||||
weight_sum += w
|
||||
|
||||
concordance /= weight_sum
|
||||
return concordance
|
||||
|
||||
def getConcordanceAllProducts():
|
||||
C = []
|
||||
|
||||
for i in range(len(allProducts)):
|
||||
c = 0
|
||||
for j in range(len(allProducts)):
|
||||
if j==i:
|
||||
continue
|
||||
else:
|
||||
c += getAllTypeConcordance(allProducts[i], allProducts[j])
|
||||
c /= len(allProducts)-1
|
||||
C.append(c)
|
||||
return C
|
||||
|
||||
def choseProducts(number):
|
||||
C = getConcordanceAllProducts()
|
||||
products = []
|
||||
prev = -1
|
||||
if number > len(allProducts):
|
||||
return allProducts
|
||||
while number > 0:
|
||||
max = -1
|
||||
if prev == -1:
|
||||
max = 0
|
||||
for j in range(len(allProducts)):
|
||||
if prev == -1:
|
||||
if C[max]<C[j]:
|
||||
max = j
|
||||
elif max == -1:
|
||||
if C[prev]>C[j]:
|
||||
max = j
|
||||
elif C[max] < C[j] and C[j] < C[prev]:
|
||||
max = j
|
||||
prev = max
|
||||
for j in range(len(allProducts)):
|
||||
if C[max] == C[j]:
|
||||
products.append(allProducts[j])
|
||||
print(C[j])
|
||||
number -= 1
|
||||
if number == 0:
|
||||
break
|
||||
print(C)
|
||||
return products
|
||||
7
product.py
Normal file
7
product.py
Normal file
@ -0,0 +1,7 @@
|
||||
class Product:
|
||||
|
||||
def __init__(self, color, shape, mass, size):
|
||||
self.color = color
|
||||
self.shape = shape
|
||||
self.mass = mass
|
||||
self.size = size
|
||||
64
supply.py
Normal file
64
supply.py
Normal file
@ -0,0 +1,64 @@
|
||||
from product import Product
|
||||
|
||||
|
||||
def create_data_products():
|
||||
allProducts = []
|
||||
|
||||
product = Product('black', 'rectangle', 51, 'small')
|
||||
allProducts.append(product)
|
||||
product = Product('black', 'rectangle', 51, 'small')
|
||||
allProducts.append(product)
|
||||
product = Product('gold', 'pack', 100, 'big')
|
||||
allProducts.append(product)
|
||||
product = Product('purple', 'rectangle', 100, 'big')
|
||||
allProducts.append(product)
|
||||
product = Product('brown', 'pack', 45, 'small')
|
||||
allProducts.append(product)
|
||||
product = Product('blue', 'rectangle', 50, 'medium')
|
||||
allProducts.append(product)
|
||||
product = Product('blue', 'square', 40, 'small')
|
||||
allProducts.append(product)
|
||||
product = Product('blue', 'rectangle', 35, 'small')
|
||||
allProducts.append(product)
|
||||
product = Product('gold', 'rectangle', 40, 'medium')
|
||||
allProducts.append(product)
|
||||
product = Product('gold', 'rectangle', 50, 'medium')
|
||||
allProducts.append(product)
|
||||
product = Product('brown', 'rectangle', 55, 'medium')
|
||||
allProducts.append(product)
|
||||
product = Product('brown', 'rectangle', 45, 'medium')
|
||||
allProducts.append(product)
|
||||
product = Product('white', 'rectangle', 40, 'medium')
|
||||
allProducts.append(product)
|
||||
product = Product('red', 'rectangle', 50, 'medium')
|
||||
allProducts.append(product)
|
||||
product = Product('blue', 'rectangle', 115, 'big')
|
||||
allProducts.append(product)
|
||||
product = Product('white', 'rectangle', 15, 'small')
|
||||
allProducts.append(product)
|
||||
product = Product('red', 'pack', 70, 'medium')
|
||||
allProducts.append(product)
|
||||
product = Product('orange', 'rectangle', 45, 'medium')
|
||||
allProducts.append(product)
|
||||
product = Product('blue', 'rectangle', 55, 'medium')
|
||||
allProducts.append(product)
|
||||
product = Product('gold', 'rectangle', 120, 'big')
|
||||
allProducts.append(product)
|
||||
product = Product('white', 'rectangle', 120, 'big')
|
||||
allProducts.append(product)
|
||||
product = Product('white', 'jar', 600, 'big')
|
||||
allProducts.append(product)
|
||||
product = Product('white', 'rectangle', 25, 'small')
|
||||
allProducts.append(product)
|
||||
product = Product('brown', 'jar', 500, 'big')
|
||||
allProducts.append(product)
|
||||
product = Product('brown', 'jar', 470, 'big')
|
||||
allProducts.append(product)
|
||||
product = Product('red', 'jar', 250, 'medium')
|
||||
allProducts.append(product)
|
||||
product = Product('black', 'jar', 250, 'medium')
|
||||
allProducts.append(product)
|
||||
product = Product('orange', 'jar', 250, 'medium')
|
||||
allProducts.append(product)
|
||||
|
||||
return allProducts
|
||||
Loading…
Reference in New Issue
Block a user