improved decision tree implementation

source/area/tractor.py

@@ -1,5 +1,6 @@
+from NN.neural_network import clear_text_area
 from crop_protection_product import CropProtectionProduct
-from area.constants import TILE_SIZE, DIRECTION_EAST, DIRECTION_SOUTH, DIRECTION_WEST, DIRECTION_NORTH
+from area.constants import TILE_SIZE, DIRECTION_EAST, DIRECTION_SOUTH, DIRECTION_WEST, DIRECTION_NORTH, WIDTH
 from area.field import fieldX, fieldY, tiles
 import pygame
 import time
@@ -38,16 +39,19 @@ class Tractor:
             self.image = pygame.image.load('resources/images/tractor_left.png').convert_alpha()
         
 
-    def work_on_field(self, tile, ground, plant1):
+    def work_on_field(self, screen, tile, ground, plant1):
+        results = []
         if plant1 is None:
             tile.randomizeContent()
             # sprobuj zasadzic cos
             print("Tarctor planted something")
+            results.append("Tarctor planted something")
         elif plant1.growth_level == 100:
             tile.plant = None
             ground.nutrients_level -= 40
             ground.water_level -= 40
             print("Tractor collected something")
+            results.append("Tractor collected something")
         else:
             plant1.try_to_grow(50,50) #mozna dostosowac jeszcze
             ground.nutrients_level -= 11
@@ -61,6 +65,7 @@ class Tractor:
             elif plant1.plant_type == self.spinosad.plant_type:
                 t = "Tractor used Spinosad"
             print(t)
+            results.append(t)
             ground.pest = False
         if ground.weed:
             # traktor pozbywa się chwastow
@@ -71,13 +76,21 @@ class Tractor:
             elif plant1.plant_type == self.metazachlor.plant_type:
                 t = "Tractor used Metazachlor"
             print(t)
+            results.append(t)
             ground.weed = False
         if ground.water_level < plant1.water_requirements:
             ground.water_level += 20
             print("Tractor watered the plant")
+            results.append("Tractor watered the plant")
         if ground.nutrients_level < plant1.nutrients_requirements:
             ground.nutrients_level += 20
             print("Tractor added some nutrients")
+            results.append("Tractor added some nutrients")
+
+        clear_text_area(screen, WIDTH-90, 100, 400, 100)
+        for idx, result in enumerate(results):
+            display_work_results(screen, result, (WIDTH-90, 100 + idx * 30))
+        
 
 
 
@@ -158,4 +171,11 @@ def do_actions(tractor, WIN, move_list):
         pygame.display.update()
         time.sleep(0.5)
 
+#displays results of the "work_on_field" function next to the field:
+def display_work_results(screen, text, position):
+    font = pygame.font.Font(None, 30)
+    displayed_text = font.render(text, 1, (255,255,255))
+    screen.blit(displayed_text, position)
+    pygame.display.update()
+    
 

source/main.py

@@ -5,7 +5,7 @@ import pandas as pd
 import joblib
 from area.constants import WIDTH, HEIGHT, TILE_SIZE
 from area.field import drawWindow
-from area.tractor import Tractor, do_actions
+from area.tractor import Tractor, do_actions, display_work_results
 from area.field import tiles, fieldX, fieldY
 from area.field import get_tile_coordinates, get_tile_index
 from ground import Dirt
@@ -106,8 +106,9 @@ def main():
         goalTile.ground=d1
         #getting the name and type of the recognized plant:
         p1.update_name(prediction)
-
+        
-        #decission tree test:
+        
+#decission tree test:
         if d1.pest:
             pe = 1
         else:
@@ -136,19 +137,71 @@ def main():
                     t3 = True
                     t4 = False
 
+        weather_n = random.randint(1, 4)
+        if weather_n == 1:
+            h1 = True
+            h2 = False
+            h3 = False
+            h4 = False
+        else:
+            h1 = False
+            if weather_n == 2:
+                h2 = True
+                h3 = False
+                h4 = False
+            else:
+                h2 = False
+                if weather_n == 3:
+                    h3 = True
+                    h4 = False
+                else:
+                    h3 = False
+                    h4 = True   
+
+        season_n = random.randint(1,4)
+        if season_n == 1:
+            s1 = True
+            s2 = False
+            s3 = False
+            s4 = False
+            temp_n = random.randint(0,22)
+        else:
+            s1 = False
+            if season_n == 2:
+                s2 = True
+                s3 = False
+                s4 = False
+                temp_n = random.randint(0,22)
+            else:
+                s2 = False
+                if season_n == 3:
+                    s3 = True
+                    s4 = False
+                    temp_n = random.randint(20,39)
+                else:
+                    s3 = False
+                    s4 = True
+                    temp_n = random.randint(-20, 10)
+        
+        anomaly_n = random.randint(1, 10)
+        if anomaly_n == 1:
+            a1 = True
+        else:
+            a1 = False
         dane = {
-            'anomalies': [True],
+                'anomalies': [a1],
-            'temp': [17],
+                'temp': [temp_n],
-            'water': [d1.water_level],
+                'water': [d1.water_level],
-            'nutri': [d1.nutrients_level],
+                'nutri': [d1.nutrients_level],
-            'pests': [pe],
+                'pests': [pe],
-            'weeds': [we],
+                'weeds': [we],
-            'ripeness': [p1.growth_level],
+                'ripeness': [p1.growth_level],
-            'season_autumn': [True], 'season_spring': [False], 'season_summer': [False], 'season_winter': [False],
+                'season_autumn': [s1], 'season_spring': [s2], 'season_summer': [s3], 'season_winter': [s4],
-            'weather_heavyCloudy': [False], 'weather_partCloudy': [False], 'weather_precipitation': [False],
+                'weather_heavyCloudy': [h1], 'weather_partCloudy': [h2], 'weather_precipitation': [h3],
-            'weather_sunny': [True],
+                'weather_sunny': [h4],
-            'type_cereal': [t1], 'type_fruit': [t2], 'type_none': [t3], 'type_vegetable': [t4]
+                'type_cereal': [t1], 'type_fruit': [t2], 'type_none': [t3], 'type_vegetable': [t4]
         }
+        
         df = pd.DataFrame(dane)
         df.to_csv('model_data.csv', index=False)
 
@@ -159,11 +212,11 @@ def main():
 
         #work on field:
         if predykcje == 'work':
-            tractor.work_on_field(goalTile, d1, p1)
+            tractor.work_on_field(WIN, goalTile, d1, p1)
-        
+    
         #update the initial state for the next target:
         istate = Istate(tile_x, tile_y, tractor.direction)
-        time.sleep(5)
+        time.sleep(2)
         print("\n")