costam

.idea/misc.xml

@@ -3,5 +3,5 @@
   <component name="JavaScriptSettings">
     <option name="languageLevel" value="ES6" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (AL-2020)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7 (AL-2020)" project-jdk-type="Python SDK" />
 </project>

.idea/wozek.iml

@@ -4,7 +4,7 @@
     <content url="file://$MODULE_DIR$">
       <excludeFolder url="file://$MODULE_DIR$/venv" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.8 (AL-2020)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.7 (AL-2020)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

decision_tree.py

@@ -62,15 +62,14 @@ def partition(rows, question):
 
 
 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. """
+    """ Gini impurity 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.  """
 
     counts = class_counts(rows)
-    impurity = 1
+    impurity = 0
     for lbl in counts:
         prob_of_lbl = counts[lbl] / float(len(rows))
-        impurity -= prob_of_lbl ** 2
+        impurity += prob_of_lbl * (1 - prob_of_lbl)
     return impurity
 
 
@@ -169,16 +168,15 @@ def print_leaf(counts):
 # print_tree(my_tree)
 #
 # testing_data = [
-#     ['gold', 'rectangle', 50, 'medium', 'Name'],
-#     ['brown', 'rectangle', 55, 'medium', 'Snickers'],
-#     ['white', 'rectangle', 120, 'big', 'Name']
+#     ['red', 'rectangle', 50, 'medium', 'Kit-kat'],
+#     ['blue', 'rectangle', 115, 'big', 'Wedel'],
+#     ['white', 'rectangle', 15, 'small', 'Krowka'],
 # ]
 #
-# test = ['white', 'rectangle', 120, 'big', 'Name']
+# test =  ['white', 'rectangle', 15, 'small', 'Krowka']
 #
-# # for row in testing_data:
-# #     print(print_leaf(classify(row, my_tree)))
+# for row in testing_data:
+#     print(print_leaf(classify(row, my_tree)))
 #
 # wynik = print_leaf(classify(test, my_tree))[0]
 # print(wynik)
-

main.py

@@ -11,6 +11,7 @@ from board import create_board, draw_board
 from random import randint, choice
 from mcda import choseProducts
 
+
 # Inicjalizacja programu i utworzenie obiektu ekrany
 def run():
     pygame.init()
@@ -44,7 +45,6 @@ def run():
                     agent.turn_left()
                 elif event.key == pygame.K_UP:
                     agent.move_forward(board)
-                    print(agent.x, agent.y)
                 elif event.key == pygame.K_SPACE:
                     board[9][0].item = choice(data.learning_data)
                     print("Wybrano: " + board[9][0].item[-1])