App.py

@@ -11,6 +11,7 @@ import BFS
 import AStar
 import random
 import Condition
+import Drzewo
 
 bfs1_flag=False
 bfs2_flag=False #Change this lines to show different bfs implementation
@@ -19,7 +20,7 @@ Astar = False
 Astar2 = False
 if bfs3_flag or Astar or Astar2:
    Pole.stoneFlag = True
-
+TreeFlag=True
 
 pygame.init()
 show_console=True
@@ -36,7 +37,7 @@ ui=Ui.Ui(screen)
 traktor_slot = pole.get_slot_from_cord((0, 0))
 traktor = Tractor.Tractor(traktor_slot, screen, Osprzet.opryskiwacz,clock,bfs2_flag)
 condition=Condition.Condition()
-
+drzewo=Drzewo.Drzewo()
 
 def init_demo(): #Demo purpose
     old_info=""
@@ -115,10 +116,14 @@ def init_demo(): #Demo purpose
                 else:
                     print_to_console("Nie można znaleźć ścieżki A*")  # Wyświetl komunikat, jeśli nie znaleziono ścieżki
            
-
+            if(TreeFlag):
+                drzewo.treeLearn()
+                drzewo.plotTree()
+                print("Decyzja to: ",drzewo.makeDecision([[10,60,0,1,0,20,1,20]]))
+                #do moves and condtion cycles
             start_flag=False
         # demo_move()
-        condition.cycle()
+        condition.cycle() #powinno zostac wrzucone razem z getCondition do ruchu traktora. Aktualnie tutaj by zobaczyc czy dziala
         condition.getCondition()
         old_info=get_info(old_info)
         for event in pygame.event.get():

Data/dataTree.csv

@@ -0,0 +1,3 @@
+plant_water_lever,tractor_water_lever,weather,season,current_time,growth,disease,fertility,action
+80,60,0,1,0,20,1,20,0
+20,60,0,1,0,20,1,20,1

Drzewo.py

@@ -0,0 +1,25 @@
+from sklearn import tree as skltree
+import pandas,os
+import matplotlib.pyplot as plt
+
+atributes=['plant_water_lever','tractor_water_lever','weather','season','current_time','growth','disease','fertility'] #Columns in CSV file should be in the same order
+class Drzewo:
+    def __init__(self):
+        self.tree=self.treeLearn()
+
+    def treeLearn(self):
+        csvdata=pandas.read_csv('Data/dataTree.csv')
+        x=csvdata[atributes]
+        decision=csvdata['action']
+        self.tree=skltree.DecisionTreeClassifier()
+        self.tree=self.tree.fit(x,decision)
+
+    def plotTree(self):
+        plt.figure()
+        skltree.plot_tree(self.tree,filled=True,feature_names=atributes)
+        plt.title("Drzewo decyzyjne wytrenowane na przygotowanych danych")
+        plt.show()
+
+    def makeDecision(self,values):
+        action=self.tree.predict(values) #0- nie podlewac, 1-podlewac
+        return action

Tree.py

@@ -1,3 +0,0 @@
-class Tree:
-    def __init__(self) -> None:
-        pass

readme.txt

@@ -1,6 +1,7 @@
 Required packages:
-    pygame,matplotlib,sklearn
+    pygame,matplotlib,sklearn,pandas
     How to install:
         pip install pygame
         pip install matplotlib
-        pip install scikit-learn
+        pip install scikit-learn
+        pip install pandas