main.py

@@ -4,11 +4,12 @@ import random
 from settings import screen_height, screen_width, SIZE, SPECIES, block_size, tile, road_coords, directions
 from src.map import drawRoads, seedForFirstTime, return_fields_list, WORLD_MATRIX
 from src.Tractor import Tractor
-from src.Plant import Plant
 from src.bfs import Astar
+from src.Plant import Plant
 from src.Field import Field
 import pickle
 import os
+from src.ID3 import make_decision
 
 
 # pygame initialization
@@ -34,13 +35,15 @@ tractor = Tractor('oil','manual', 'fuel', 'fertilizer1', 20)
 tractor_group = pygame.sprite.Group()
 tractor_group.add(tractor)
 
+tractor.setCapacity(90)
+tractor.setFuel(100)
+
 #PLANTS
 plant_group = pygame.sprite.Group()
 plant_group = seedForFirstTime()
 fields = return_fields_list()
 
-#ID3 TREE
-tree = pickle.load(open(os.path.join('src','tree.plk'),'rb'))
+
 
 #
 tractor_move = pygame.USEREVENT + 1
@@ -54,8 +57,29 @@ print("Destination: ", destination)
 mx=int((mx+18)/36)
 my=int((my+18)/36)
 print("Destination: ", mx,my)
-tmp = WORLD_MATRIX[mx][my]
-print(tmp)
+
+#POBIERZ MATRIXA OBJEKT FILD, Z FIELD POBIERZ PLANT, Z PLANTA PARAMETRY, WYWOŁAJ DECYCJNOSC
+#ID3 TREE
+dtree = pickle.load(open(os.path.join('src','tree.plk'),'rb'))
+     
+# pobierz dane o polu field i czy ma na sobie roslinke, zadecyduj czy zebrac
+this_field = WORLD_MATRIX[mx][my]
+this_contain = Field.getContain(this_field)
+if isinstance(this_contain, Plant): 
+     this_plant = this_contain
+     params=Plant.getParameters(this_plant)
+     decision=make_decision(params[0],params[1],params[2],params[3],params[4],tractor.fuel,tractor.capacity,params[5],dtree)
+     print('wzorst',params[0],'wilgotnosc',params[1],'dni_od_nawiezienia',params[2],'pogoda',params[3],'zdrowa',params[4],'paliwo',tractor.fuel,'pojemnosc eq',tractor.capacity,'cena sprzedazy',params[5])
+     print(decision)
+     if decision == 1:
+        #   Tractor.collect(self=tractor, plant_group=plant_group)
+          print('Gotowe do zbioru')
+     else:
+          print('nie zbieramy')
+     
+else:
+     print('Road, no plant growing')
+
 
 moves = goal_astar.search(
     [tractor.rect.x, tractor.rect.y, directions[tractor.rotation]], destination)

src/Field.py

@@ -3,7 +3,7 @@ from src.Plant import Plant
 
 class Field(Sprite):    
     def __init__(self, type, x, y, image, cost, hydration_level , soil,
-                 fertilizer_degree, development_degree, plant_type, fertilizer_type, to_water, plantObj):
+                 fertilizer_degree, development_degree, plant_type, fertilizer_type, to_water, contain):
         super().__init__()
         self.type = type
         self.x = x
@@ -19,11 +19,12 @@ class Field(Sprite):
         self.plant_type = plant_type
         self.fertilizer_type = fertilizer_type
         self.to_water = to_water
-        self.plantObj = plantObj
+        self.contain = contain
 
-    def getPlantObj(self):
-        return self.plantObj
+    def getContain(self):
+        return self.contain
+
+    def setContain(self,newPlant):
+        self.contain=newPlant
 
-    def setPlantObj(self,newPlant):
-        self.plantObj=newPlant
 

src/ID3.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -18,21 +18,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "globals must be a real dict; try eval(expr, {}, mapping)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[18], line 14\u001b[0m\n\u001b[1;32m     11\u001b[0m data \u001b[39m=\u001b[39m tree\u001b[39m.\u001b[39mexport_graphviz(d_tree, out_file\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, feature_names\u001b[39m=\u001b[39m[\u001b[39m'\u001b[39m\u001b[39mWzrost\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mwilgotnosc\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mdni_od_nawiezienia\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39maktualna_pogoda\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mczy_roslina_robaczywa\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mpaliwo\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mpojemnosc_ekwipunku\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mcena_sprzedarzy\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[1;32m     12\u001b[0m graph \u001b[39m=\u001b[39m pydotplus\u001b[39m.\u001b[39mgraph_from_dot_data(data)\n\u001b[0;32m---> 14\u001b[0m accuracy \u001b[39m=\u001b[39m \u001b[39meval\u001b[39;49m(d_tree,train,\u001b[39m'\u001b[39;49m\u001b[39mczy_zebrac\u001b[39;49m\u001b[39m'\u001b[39;49m)\n",
-      "\u001b[0;31mTypeError\u001b[0m: globals must be a real dict; try eval(expr, {}, mapping)"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Read the CSV file\n",
     "train = pd.read_csv(\"train_3.csv\", delimiter=\";\")\n",
@@ -50,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {

src/ID3.py

@@ -7,25 +7,39 @@ import pydotplus
 import matplotlib.image as pltimg
 import matplotlib.pyplot as plt
 
-# Read the CSV file
-train = pd.read_csv("train_3.csv", delimiter=";")
+def make_decision(Wzrost, wilgotnosc, dni_od_nawiezienia, aktualna_pogoda, czy_roslina_robaczywa, paliwo, pojemnosc_ekwipunku,cena_sprzedarzy, tree):
+    decision = tree.predict([[Wzrost,wilgotnosc, dni_od_nawiezienia, aktualna_pogoda, czy_roslina_robaczywa, paliwo, pojemnosc_ekwipunku,cena_sprzedarzy]])
+    return decision
 
-x_train = train.drop('czy_zebrac',axis=1)
-y_train = train['czy_zebrac']
+def learnTree():
+    # Read the CSV file
+    train = pd.read_csv("src/train_3.csv", delimiter=";")
 
-d_tree = DecisionTreeClassifier()
-d_tree = d_tree.fit(x_train,y_train)
+    # print(f'Shape: {train.shape}')
+    # print(f'Head:\n{train.head()}')
 
-pickle.dump(d_tree, open(os.path.join('.','tree.plk'),'wb'))
-data = tree.export_graphviz(d_tree, out_file=None, feature_names=['Wzrost','wilgotnosc','dni_od_nawiezienia','aktualna_pogoda','czy_roslina_robaczywa','paliwo','pojemnosc_ekwipunku','cena_sprzedarzy'])
-graph = pydotplus.graph_from_dot_data(data)
+    x_train = train.drop('czy_zebrac',axis=1)
+    y_train = train['czy_zebrac']
 
-# Save the graph as a PNG image in the script's folder
-graph.write_png(os.path.join('.', 'mytree.png'))
+    d_tree = DecisionTreeClassifier()
+    d_tree = d_tree.fit(x_train,y_train)
 
-# Read the PNG image
-# img = pltimg.imread(os.path.join('.', 'mytree.png'))
+    pickle.dump(d_tree, open(os.path.join('.','tree.plk'),'wb'))
+    data = tree.export_graphviz(d_tree, out_file=None, feature_names=['Wzrost','wilgotnosc','dni_od_nawiezienia','aktualna_pogoda','czy_roslina_robaczywa','paliwo','pojemnosc_ekwipunku','cena_sprzedarzy'])
+    graph = pydotplus.graph_from_dot_data(data)
 
-# Display the image
-# imgplot = plt.imshow(img)
-# plt.show()
+    # Save the graph as a PNG image in the script's folder
+    graph.write_png(os.path.join('.', 'mytree.png'))
+
+    # Read the PNG image
+    # img = pltimg.imread(os.path.join('.', 'mytree.png'))
+    # Display the image
+    # imgplot = plt.imshow(img)
+    # plt.show()
+
+    return d_tree
+
+# dtree = learnTree()    #w, w, d, p,R,P,P,S
+                            #przy robaczywej == 1 daje ok czyli jak 1 to git jest mozna zbierac, ale planowalem inaczej
+# decision=make_decision(70,85,12,3,1,65,54,1000,dtree)
+# print(decision)

src/Plant.py

@@ -61,5 +61,5 @@ class Plant(pygame.sprite.Sprite):
         self.rect = self.image.get_rect()
         self.rect.center = [pos_x,pos_y]
 
-def getParameters(self):
-    return [self.species, self.wzrost, self.wilgotnosc, self.dni_od_nawiezienia,self.aktualna_pogoda,self.czy_robaczywa,self.cena_sprzedarzy]
+    def getParameters(self):
+        return [self.wzrost, self.wilgotnosc, self.dni_od_nawiezienia,self.aktualna_pogoda,self.czy_robaczywa,self.cena_sprzedarzy]

src/Tractor.py

@@ -1,9 +1,13 @@
 import pygame
 from settings import block_size, tile
+import pickle
+from sklearn import tree
 
 mx=0
 my=0
 
+
+
 class Tractor(pygame.sprite.Sprite):
     def __init__(self, engine, transmission, fuel, fertilizer, capacity):
         super().__init__()
@@ -22,14 +26,21 @@ class Tractor(pygame.sprite.Sprite):
         self.rotation = 90
 
         self.collected = 0
-        self.capacity = capacity
         self.engine = engine
         self.transmission = transmission
-        self.fuel = fuel
         self.fertilizer = fertilizer
+        self.capacity = capacity
+        self.fuel = fuel
 
 
 
+    def setFuel(self,fuelLevel):
+        self.fuel=fuelLevel
+
+    def setCapacity(self, newCapacity):
+        self.capacity = newCapacity    
+        
+
     def movement_using_keys(self):
         keys = pygame.key.get_pressed()
         
@@ -75,7 +86,7 @@ class Tractor(pygame.sprite.Sprite):
             self.image = self.left
             
     def movement(self, direction):
-        print(int((self.rect.x-18)/36),';',int((self.rect.y-18)/36))
+        # print(int((self.rect.x-18)/36),';',int((self.rect.y-18)/36))
         if direction == 'F':
             self.move_forward()
         elif direction == 'L':
@@ -108,3 +119,7 @@ class Tractor(pygame.sprite.Sprite):
     def find_nearest_plant(self, plant_group):
         self.plant_group = plant_group
 
+    def getLocation(self):
+        return [self.rect.x, self.rect.y]
+    
+        

src/map.py

@@ -42,15 +42,17 @@ def drawRoads(screen):
     for x in road_coords:
         for block in range(int(fields_amount)+1):
             screen.blit(road, (x*block_size, block * 36))
-            tmp_field=Field('road', x*block_size, block * 36, None, get_cost_by_type('road'), None, None, None, None, 'road', None, None,plantObj=None)
+            new_road = Road(x,block)
+            tmp_field=Field('road', x*block_size, block * 36, None, get_cost_by_type('road'), None, None, None, None, 'road', None, None,contain=new_road)
             fields.add(tmp_field)
-            WORLD_MATRIX[x][block]=Road(x,block)
+            WORLD_MATRIX[x][block]=tmp_field
     for y in road_coords:
         for block in range(int(fields_amount)+1):
             screen.blit(road, (block * block_size, y*block_size))
-            tmp_field=Field('road', block * block_size, y*block_size, None, get_cost_by_type('road'), None, None, None, None, 'road', None, None,plantObj=None)
+            new_road = Road(block,y)
+            tmp_field=Field('road', block * block_size, y*block_size, None, get_cost_by_type('road'), None, None, None, None, 'road', None, None,contain=new_road)
             fields.add(tmp_field)
-            WORLD_MATRIX[block][y]=Road(block,y)
+            WORLD_MATRIX[block][y]=tmp_field
 
 
     barn_img = pygame.image.load('assets/barn.png')
@@ -74,7 +76,7 @@ def seedForFirstTime():
                     wzrost=random.randint(0, 100),
                     wilgotnosc=random.randint(0, 100),
                     dni_od_nawiezienia=random.randint(0, 31),
-                    aktualna_pogoda='_',
+                    aktualna_pogoda=random.randint(1,4),
                     czy_robaczywa=random.randint(0, 1),
                     cena_sprzedarzy=random.randint(500, 2000),
                     species=plant_name,
@@ -82,7 +84,7 @@ def seedForFirstTime():
                     pos_y=y)
             blocks_seeded_in_field = blocks_seeded_in_field + 1
             plant_group.add(new_plant)
-            tmp_field_plant = Field('field', x-18, y-18, None, get_cost_by_type(plant_name), None, None, None, None, plant_name, None, None, plantObj=new_plant)
+            tmp_field_plant = Field('field', x-18, y-18, None, get_cost_by_type(plant_name), None, None, None, None, plant_name, None, None, contain=new_plant)
             fields.add(tmp_field_plant)
 
             mx = int((x-18)/36)