forked from s444420/AL-2020
costam
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@ -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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</project>
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@ -4,7 +4,7 @@
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<content url="file://$MODULE_DIR$">
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<excludeFolder url="file://$MODULE_DIR$/venv" />
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</content>
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<orderEntry type="jdk" jdkName="Python 3.8 (AL-2020)" jdkType="Python SDK" />
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<orderEntry type="jdk" jdkName="Python 3.7 (AL-2020)" jdkType="Python SDK" />
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<orderEntry type="sourceFolder" forTests="false" />
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</component>
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</module>
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2
data.py
2
data.py
@ -40,7 +40,7 @@ learning_data = [
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['gold', 'rectangle', 40, 'medium', 'Twix'],
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['gold', 'rectangle', 50, 'medium', 'Prince-polo'],
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['brown', 'rectangle', 55, 'medium', 'Snickers'],
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['brown', 'rectangle', 45, 'medium', 'Lion'],
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['brown', 'rectangle', 45, 'medium', 'Lion'],
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['white', 'rectangle', 40, 'medium', 'Kinder-bueno'],
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['red', 'rectangle', 50, 'medium', 'Kit-kat'],
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['blue', 'rectangle', 115, 'big', 'Wedel'],
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@ -62,15 +62,14 @@ def partition(rows, question):
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def gini(rows):
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""" Gini impurity is a measure of how often a randomly chosen element from
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the set would be incorrectly labeled if it was randomly labeled according to
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the distribution of labels in the subset. """
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""" Gini impurity to miara tego jak często losowo wybrany element zbioru byłby źle skategoryzowany, gdyby
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przypisać mu losową kategorię spośród wszystkich kategorii znajdujących się w danym zbiorze. """
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counts = class_counts(rows)
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impurity = 1
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impurity = 0
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for lbl in counts:
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prob_of_lbl = counts[lbl] / float(len(rows))
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impurity -= prob_of_lbl ** 2
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impurity += prob_of_lbl * (1 - prob_of_lbl)
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return impurity
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@ -169,16 +168,15 @@ def print_leaf(counts):
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# print_tree(my_tree)
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#
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# testing_data = [
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# ['gold', 'rectangle', 50, 'medium', 'Name'],
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# ['brown', 'rectangle', 55, 'medium', 'Snickers'],
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# ['white', 'rectangle', 120, 'big', 'Name']
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# ['red', 'rectangle', 50, 'medium', 'Kit-kat'],
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# ['blue', 'rectangle', 115, 'big', 'Wedel'],
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# ['white', 'rectangle', 15, 'small', 'Krowka'],
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# ]
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#
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# test = ['white', 'rectangle', 120, 'big', 'Name']
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# test = ['white', 'rectangle', 15, 'small', 'Krowka']
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#
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# # for row in testing_data:
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# # print(print_leaf(classify(row, my_tree)))
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# for row in testing_data:
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# print(print_leaf(classify(row, my_tree)))
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#
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# wynik = print_leaf(classify(test, my_tree))[0]
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# print(wynik)
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environment.yml
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environment.yml
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img/shelf.png
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img/shelf.png
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2
main.py
2
main.py
@ -11,6 +11,7 @@ from board import create_board, draw_board
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from random import randint, choice
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from mcda import choseProducts
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# Inicjalizacja programu i utworzenie obiektu ekrany
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def run():
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pygame.init()
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@ -44,7 +45,6 @@ def run():
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agent.turn_left()
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elif event.key == pygame.K_UP:
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agent.move_forward(board)
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print(agent.x, agent.y)
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elif event.key == pygame.K_SPACE:
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board[9][0].item = choice(data.learning_data)
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print("Wybrano: " + board[9][0].item[-1])
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