tworzenie pola na podstawie algorytmu genetycznego

.idea/Traktor AI.iml

@@ -4,7 +4,7 @@
     <content url="file://$MODULE_DIR$">
       <excludeFolder url="file://$MODULE_DIR$/venv" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.10 (Traktor)" jdkType="Python SDK" />
+    <orderEntry type="inheritedJdk" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

.idea/misc.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9" project-jdk-type="Python SDK" />
+</project>

source/area/field.py

@@ -6,6 +6,9 @@ from area.constants import WIDTH,FIELD_WIDTH,TILE_SIZE,GREY,ROWS,COLS
 from tile import Tile
 from ground import Dirt
 
+from genetic import genetic_algorithm
+import os
+
 tiles = []
 
 fieldX = (WIDTH-FIELD_WIDTH)/2
@@ -20,7 +23,7 @@ def positionFieldElements():
         t.y += fieldY
 
 
-def createTiles():
+''' def createTiles():
     for y in range(0, COLS):
         for x in range(0, ROWS):
             tile = Tile(x*TILE_SIZE, y*TILE_SIZE)
@@ -30,8 +33,47 @@ def createTiles():
             tile.randomizeContent()
             tiles.append(tile)
     positionFieldElements()
+    return tiles '''
+
+def createTiles():
+    best = genetic_algorithm(50, 20, 0.7, 10)
+
+    for y in range(COLS):
+        for x in range (ROWS):
+            tile = Tile(x * TILE_SIZE, y * TILE_SIZE)
+            dirt = Dirt(random.randint(1, 100), random.randint(1, 100))
+            dirt.pests_and_weeds()
+
+            crop = best[y][x]
+            if crop == 'apple':
+                tile.image = "resources/images/sampling.png"
+                photo_path = random.choice(os.listdir("resources/images/plant_photos/apples"))
+                tile.photo = os.path.join("resources/images/plant_photos/apples", photo_path)
+            elif crop == 'cauliflower':
+                tile.image = "resources/images/sampling.png"
+                photo_path = random.choice(os.listdir("resources/images/plant_photos/cauliflowers"))
+                tile.photo = os.path.join("resources/images/plant_photos/cauliflowers", photo_path)
+            elif crop == 'radish':
+                tile.image = "resources/images/sampling.png"
+                photo_path = random.choice(os.listdir("resources/images/plant_photos/radishes"))
+                tile.photo = os.path.join("resources/images/plant_photos/radishes", photo_path)
+            elif crop == 'wheat':
+                tile.image = "resources/images/sampling.png"
+                photo_path = random.choice(os.listdir("resources/images/plant_photos/wheats"))
+                tile.photo = os.path.join("resources/images/plant_photos/wheats", photo_path)
+            elif crop == 'rock_dirt':
+                tile.image = "resources/images/rock_dirt.png"
+                dirt.set_ocstacle(True)
+            else:
+                tile.image = "resources/images/dirt.png"
+                tile.ground = "resources/images/background.jpg"
+
+            tile.ground = dirt
+            tiles.append(tile)
+    positionFieldElements()
     return tiles
 
+
 def createField(win):
     createTiles()
     for t in tiles:

source/genetic.py

@@ -0,0 +1,98 @@
+import random
+
+def make_population(population_s, field_s):
+    population = []
+    crops = ['apple', 'cauliflower', 'radish', 'wheat', 'rock_dirt', 'dirt']
+
+    for _ in range(population_s):
+        i = []
+        for _ in range(field_s):
+            row = random.choices(crops, k=field_s)
+            i.append(row)
+        population.append(i)
+    return population
+
+def calculate_fitness(individual):
+    cost = 0
+    for i in range(len(individual)):
+        for j in range(len(individual[i])):
+            crop = individual[i][j]
+            neighbors = [
+                individual[x][y]
+                for x in range(max(0, i-1), min(len(individual), i+2))
+                for y in range(max(0, j-1), min(len(individual), j+2))
+                if (x,y) != (i,j)
+            ]
+            for n in neighbors:
+                if crop == 'wheat' and n == 'apple':
+                    cost += 2
+                elif crop == 'cauliflower' and n == 'radish':
+                    cost += 4
+
+    fitness = 1/(1+cost)
+    return fitness
+
+def select_parents(population, fitnesses):
+    fitnesses_sum = sum(fitnesses)
+    selection_parts = [fitness / fitnesses_sum for fitness in fitnesses]
+    parents = random.choices(population, weights=selection_parts, k=2)
+    return parents
+
+def crossover(parent_1, parent_2):
+    crossover_point = random.randint(1, (len(parent_1)-1))
+    child_1 = parent_1[:crossover_point] + parent_2[crossover_point:]
+    child_2 = parent_2[:crossover_point] + parent_1[crossover_point:]
+    return child_1, child_2
+
+def mutation(individual, chance):
+    crops = ['apple', 'cauliflower', 'radish', 'wheat', 'rock_dirt', 'dirt']
+    if random.random() < chance:
+        row = random.randint(0, len(individual) - 1)
+        column = random.randint(0, len(individual[0]) - 1)
+        individual[row][column] = random.choice(crops)
+    return individual
+
+def genetic_algorithm(population_s, field_s, chance, limit):
+    population = make_population(population_s, field_s)
+
+    best_fitness = 0
+    count = 0
+
+    while best_fitness < 1:
+        fitnesses = [calculate_fitness(individual) for individual in population]
+        new_population = []
+
+        for _ in range(population_s // 2):
+            parent_1, parent_2 = select_parents(population, fitnesses)
+
+            p1c = calculate_fitness(parent_1)
+            p2c = calculate_fitness(parent_2)
+            print("p1c: ",p1c,"\np2c: ",p2c)
+
+            child_1, child_2 = crossover(parent_1, parent_2)
+            child_1 = mutation(child_1, chance)
+            child_2 = mutation(child_2, chance)
+            new_population.append(child_1)
+            new_population.append(child_2)
+
+        combined_population = population + new_population
+        combined_population = sorted(combined_population, key=calculate_fitness, reverse=True)
+
+        population = combined_population[:population_s]
+
+        current_best_fitness = calculate_fitness(population[0])
+        if current_best_fitness > best_fitness:
+            best_fitness = current_best_fitness
+            count = 0
+        else:
+            count += 1
+
+        if count >= limit:
+            break
+
+    best_child = max(population, key=calculate_fitness)
+
+    bsf = calculate_fitness(best_child)
+    print("bsf: ", bsf)
+
+    return best_child

source/ground.py

@@ -20,7 +20,10 @@ class Dirt:
         elif i == 4:
             self.weed = True
             self.pest = True
-        elif i == 5:
-            self.obstacle = True
+        '''elif i == 5:
+            self.obstacle = True'''
+
+    def set_ocstacle(self, obstacle_status):
+        self.obstacle = obstacle_status
 
 # add init, getters,setters

source/main.py

@@ -14,6 +14,9 @@ from bfs import graphsearch, Istate, succ
 from astar import a_star
 from NN.neural_network import load_model, load_image, guess_image, display_image, display_result
 from PIL import Image
+from genetic import genetic_algorithm
+
+from area.field import createTiles
 
 pygame.init()
 WIN_WIDTH = WIDTH + 300
@@ -23,6 +26,7 @@ pygame.display.set_caption('Intelligent tractor')
 
 def main():
     run = True
+
     window = drawWindow(WIN)
     pygame.display.update()
 

source/model_data.csv

@@ -1,2 +1,2 @@
 anomalies,temp,water,nutri,pests,weeds,ripeness,season_autumn,season_spring,season_summer,season_winter,weather_heavyCloudy,weather_partCloudy,weather_precipitation,weather_sunny,type_cereal,type_fruit,type_none,type_vegetable
-True,17,32,2,0,0,54,True,False,False,False,False,False,False,True,True,False,False,False
+True,17,72,73,0,0,60,True,False,False,False,False,False,False,True,False,True,False,False