App.py

@@ -9,9 +9,7 @@ import Osprzet
 import Ui
 import BFS
 import AStar
-import random
+
-import Condition
-import Drzewo
 
 bfs1_flag=False
 bfs2_flag=False #Change this lines to show different bfs implementation
@@ -36,8 +34,7 @@ ui=Ui.Ui(screen)
 #Tractor creation
 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=""
@@ -117,14 +114,10 @@ def init_demo(): #Demo purpose
                     print_to_console("Nie można znaleźć ścieżki A*")  # Wyświetl komunikat, jeśli nie znaleziono ścieżki
            
             if(TreeFlag):
-                drzewo.treeLearn()
+                traktor.move_forward(pole)
-                drzewo.plotTree()
+                traktor.tree_move()
-                print("Decyzja to: ",drzewo.makeDecision([[10,60,0,1,1,0,20,1,20]]))
-                #do moves and condtion cycles
             start_flag=False
         # demo_move()
-        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():
             if event.type == pygame.QUIT:

Condition.py

@@ -38,6 +38,10 @@ class Condition:
             self.temperature=self.setRandomTemperature()
             self.clock=self.clock+1
 
+    def return_condition(self):
+        return [self.temperature,self.rain,self.season,self.currentTime]
+
+
     def getCondition(self):
         print(f"Aktualny czas: {Climate.time[self.currentTime]},opady: {Climate.rain[self.rain]},temperatura: {Climate.temperature[self.temperature]}, pora roku: {Climate.seasons[self.season]}")
             

Data/dataTree.csv

@@ -1,3 +1,3 @@
-plant_water_level,tractor_water_level,temperature,rain,season,current_time,growth,disease,fertility,action
+plant_water_level,growth,disease,fertility,tractor_water_level,temperature,rain,season,current_time,action
-80,60,3,2,1,0,20,1,20,0
+80,20,0,90,40,2,0,1,0,0
-20,60,3,0,1,0,20,1,20,1
+50,20,0,90,80,2,0,1,0,1

Drzewo.py

@@ -2,7 +2,7 @@ from sklearn import tree as skltree
 import pandas,os
 import matplotlib.pyplot as plt
 
-atributes=['plant_water_level','tractor_water_level','temperature','rain','season','current_time','growth','disease','fertility'] #Columns in CSV file should be in the same order
+atributes=['plant_water_level','growth','disease','fertility','tractor_water_level','temperature','rain','season','current_time'] #Columns in CSV file has to be in the same order
 class Drzewo:
     def __init__(self):
         self.tree=self.treeLearn()
@@ -12,14 +12,14 @@ class Drzewo:
         x=csvdata[atributes]
         decision=csvdata['action']
         self.tree=skltree.DecisionTreeClassifier()
-        self.tree=self.tree.fit(x,decision)
+        self.tree=self.tree.fit(x.values,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.savefig('tree.png')
         plt.show()
-
     def makeDecision(self,values):
-        action=self.tree.predict(values) #0- nie podlewac, 1-podlewac
+        action=self.tree.predict([values]) #0- nie podlewac, 1-podlewac
         return action

Image.py

@@ -37,7 +37,7 @@ class Image:
         self.gasStation_image=pygame.transform.scale(gasStation,(dCon.CUBE_SIZE,dCon.CUBE_SIZE))
 
     def return_random_plant(self):
-        x=random.randint(0,7)
+        x=random.randint(0,5) #disabled dirt and mud generation
         keys=list(self.plants_image_dict.keys())
         plant=keys[x]
         return (plant,self.plants_image_dict[plant])

Roslina.py

@@ -110,6 +110,9 @@ class Roslina:
     def return_stan(self):
         return self.stan
     
+    def return_stan_for_tree(self):
+        return self.stan.return_stan_for_tree()
+    
     def get_hydrate_stats(self):
         return self.stan.return_hydrate()
     

Slot.py

@@ -82,4 +82,6 @@ class Slot:
             self.plant.stan.nawodnienie=random.randint(61,100)
         elif(index==-1):
             pass
-    
+    
+    def return_stan_for_tree(self):
+        return self.plant.return_stan_for_tree()

Stan.py

@@ -57,5 +57,9 @@ class Stan:
             return "Zdrowa"
         if(self.choroba==1):
             return "Chora"       
+        
+    def return_stan_for_tree(self):
+        return [self.nawodnienie,self.wzrost,self.choroba,self.zyznosc]
+    
     def report_all(self):
         return f"Nawodnienie: {self.nawodnienie} Zyznosc: {self.zyznosc} Wzrost: {self.wzrost} Choroba: {self.return_disease_as_string()}"

Tractor.py

@@ -7,6 +7,12 @@ import displayControler as dCon
 import Slot
 import Osprzet
 import Node
+import Condition
+import Drzewo
+
+condition=Condition.Condition()
+drzewo=Drzewo.Drzewo()
+
 
 tab = [-1, 0, 0, 0, 0, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 0, 1, 0, 1, 1,
@@ -57,6 +63,17 @@ class Tractor:
         self.current_tractor_image = self.tractor_images[self.direction]
         self.draw_tractor()
 
+    def tree_move(self):
+        drzewo.treeLearn()
+        drzewo.plotTree()
+        slot_attributes=self.slot.return_stan_for_tree()
+        climate_attributes=condition.return_condition()
+        attributes=[]
+        attributes=attributes+slot_attributes+[self.waterLevel]+climate_attributes
+        print("Decyzja czy podlac:",drzewo.makeDecision(attributes),"Atrybuty tego stanu to:",attributes)
+        #TODO SNAKE MOVE AND USING drzewo.makeDecision(attributes)for each slot. Also we need to cycle climate for each slot change by 
+        #condition.cycle()
+        #condition.getCondition()
     def turn_right(self):
         # zmiana kierunku w prawo
         direction_map = {