.idea/Traktor AI.iml

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

.idea/misc.xml

@@ -1,4 +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" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (Traktor)" project-jdk-type="Python SDK" />
 </project>

source/NN/model.py

@@ -1,27 +0,0 @@
-import torch.nn as nn
-import torch
-import torch.nn.functional as F
-
-
-class Conv_Neural_Network_Model(nn.Module):
-    def __init__(self, num_classes=5,hidden_layer1 = 512,hidden_layer2 = 256):
-        super(Conv_Neural_Network_Model, self).__init__()
-        
-        self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1)
-        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
-        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
-
-        self.fc1 = nn.Linear(64*25*25,hidden_layer1)
-        self.fc2 = nn.Linear(hidden_layer1,hidden_layer2)
-        self.out = nn.Linear(hidden_layer2,num_classes)
-
-    def forward(self, x):
-        x = self.pool1(F.relu(self.conv1(x)))
-        x = self.pool1(F.relu(self.conv2(x)))
-        x = x.view(-1, 64*25*25) #<----flattening the image
-        x = self.fc1(x)
-        x = torch.relu(x)
-        x = self.fc2(x)
-        x = torch.relu(x)
-        x = self.out(x)
-        return F.log_softmax(x, dim=-1)

source/NN/neural_network.py

@@ -1,127 +0,0 @@
-import torch
-import torch.nn as nn
-from torch.utils.data import DataLoader
-from torchvision import datasets, transforms, utils
-from torchvision.transforms import Compose, Lambda, ToTensor
-import matplotlib.pyplot as plt
-from NN.model import *
-from PIL import Image
-import pygame
-from area.constants import GREY
-
-device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
-
-#data transform to tensors:
-data_transformer = transforms.Compose([
-    transforms.Resize((100, 100)), 
-    transforms.ToTensor(),  
-    transforms.Normalize((0.5, 0.5, 0.5 ), (0.5, 0.5, 0.5))  
-    ])
-
-
-#loading data:
-train_set = datasets.ImageFolder(root='resources/train', transform=data_transformer)
-test_set = datasets.ImageFolder(root='resources/test', transform=data_transformer)
-
-
-#to mozna nawet przerzucic do funkcji train:
-# train_loader = DataLoader(train_set, batch_size=64, shuffle=True)
-#test_loader = DataLoader(test_set, batch_size=32, shuffle=True)
-
-
-#function for training model
-def train(model, dataset, iter=100, batch_size=64):
-    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
-    criterion = nn.NLLLoss()
-    train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
-    model.train()
-
-    for epoch in range(iter):
-        for inputs, labels in train_loader:
-            optimizer.zero_grad()
-            output = model(inputs.to(device))
-            loss = criterion(output, labels.to(device))
-            loss.backward()
-            optimizer.step()
-        if epoch % 10 == 0:
-                print('epoch: %3d loss: %.4f' % (epoch, loss))
-
-#function for getting accuracy
-def accuracy(model, dataset):
-    model.eval()
-    with torch.no_grad():
-        correct = sum([
-            (model(inputs.to(device)).argmax(dim=1) == labels.to(device)).sum()
-            for inputs, labels in DataLoader(dataset, batch_size=64, shuffle=True)
-        ])
-
-    return correct.float() / len(dataset)
-
-
-# model = Conv_Neural_Network_Model()
-# model.to(device)
-
-#loading the already saved model:
-# model.load_state_dict(torch.load('CNN_model.pth'))
-# model.eval()
-
-# #training the model:
-# train(model, train_set)
-# print(f"Accuracy of the network is: {100*accuracy(model, test_set)}%")
-# torch.save(model.state_dict(), 'CNN_model.pth')
-
-
-
-def load_model():
-    model = Conv_Neural_Network_Model()
-    model.load_state_dict(torch.load('CNN_model.pth', map_location=torch.device('cpu')))
-    model.eval()
-    return model
-
-
-def load_image(image_path):
-    testImage = Image.open(image_path).convert('RGB')
-    testImage = data_transformer(testImage)
-    testImage = testImage.unsqueeze(0)
-    return testImage
-
-#display the image for prediction next to the field
-def display_image(screen, image_path, position):
-    image = pygame.image.load(image_path)
-    image = pygame.transform.scale(image, (250, 250))
-    screen.blit(image, position)
-
-#display result of the guessed image (text under the image)
-def display_result(screen, position, predicted_class):
-    font = pygame.font.Font(None, 30)
-    displayed_text = font.render("The predicted image is: "+str(predicted_class), 1, (255,255,255))
-    screen.blit(displayed_text, position)
-
-def clear_text_area(win, x, y, width, height, color=GREY):
-    pygame.draw.rect(win, color, (x, y, width, height))
-    pygame.display.update()
-
-def guess_image(model, image_tensor):
-    with torch.no_grad():
-        testOutput = model(image_tensor)
-        _, predicted = torch.max(testOutput, 1)
-        predicted_class = train_set.classes[predicted.item()]
-    return predicted_class
-
-
-
-#TEST - loading the image and getting results:
-# testImage_path = 'resources/images/plant_photos/1c76aa4d-11f4-47d1-8bdd-2cb78deeeccf.jpg'
-# testImage = Image.open(testImage_path)
-# testImage = data_transformer(testImage)
-# testImage = testImage.unsqueeze(0)
-# testImage = testImage.to(device)
-
-# model.load_state_dict(torch.load('CNN_model.pth'))
-# model.to(device)
-# model.eval()
-
-# testOutput = model(testImage)
-# _, predicted = torch.max(testOutput, 1)
-# predicted_class = train_set.classes[predicted.item()]
-# print(f'The predicted class is: {predicted_class}')

source/area/constants.py

@@ -12,9 +12,6 @@ GROUND = ""
 # path to ground image
 GREY = (20, 17, 17, 255)
 
-DIRECTION_NORTH = 1
-DIRECTION_EAST = 2
-DIRECTION_SOUTH = 3
-DIRECTION_WEST = 4
+
 
 

source/area/field.py

@@ -1,16 +1,12 @@
 #   create a field here : 1: add tiles, 2: place them
 import pygame
-import random
 
-from area.constants import WIDTH,FIELD_WIDTH,TILE_SIZE,GREY,ROWS,COLS
+from area.constants import WIDTH,HEIGHT,FIELD_WIDTH,FIELD_HEIGHT,TILE_SIZE,GREY,ROWS,COLS
+from area.tractor import Tractor
 from tile import Tile
-from ground import Dirt
-
-from genetic import genetic_algorithm
-import os
 
 tiles = []
-
+tractor = Tractor(0*TILE_SIZE, 0*TILE_SIZE)
 fieldX = (WIDTH-FIELD_WIDTH)/2
 # in center of the screen
 fieldY = 100
@@ -21,58 +17,16 @@ def positionFieldElements():
     for t in tiles:
         t.x += fieldX
         t.y += fieldY
+    tractor.x += fieldX
+    tractor.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)
-            dirt = Dirt(random.randint(1, 100), random.randint(1, 100))
-            dirt.pests_and_weeds()
-            tile.ground = dirt          
             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()
@@ -81,24 +35,11 @@ def createField(win):
         image = pygame.transform.scale(image, (TILE_SIZE, TILE_SIZE))
         win.blit(image, (t.x, t.y))
         pygame.display.flip()
-        
+    imageTractor = pygame.image.load(tractor.image).convert_alpha()
+    imageTractor = pygame.transform.scale(imageTractor, (TILE_SIZE, TILE_SIZE))
+    win.blit(imageTractor, (tractor.x, tractor.y))
+    pygame.display.flip()
 
 def drawWindow(win):
     win.fill(GREY)
     createField(win)
-    pygame.display.flip()
-    
-
-def get_tile_coordinates(index):
-    if index < len(tiles):
-        tile = tiles[index]
-        return tile.x, tile.y
-    else:
-        return None
-    
-def get_tile_index():
-    valid_indices = []
-    for index, tile in enumerate(tiles):
-        if tile.image=="resources/images/sampling.png":
-            valid_indices.append(index)
-    return random.choice(valid_indices)

source/area/tractor.py

@@ -1,15 +1,9 @@
-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, WIDTH
-from area.field import fieldX, fieldY, tiles
-import pygame
-import time
 
 
 class Tractor:
     x = None
     y = None
-    direction = None #direction takes values in the range of 1 to 4 (1->North, 2->East etc...)
     image = None
     cypermetryna = CropProtectionProduct("pests", "cereal")
     diflufenikan = CropProtectionProduct("weeds", "cereal")
@@ -19,39 +13,21 @@ class Tractor:
     metazachlor = CropProtectionProduct("weeds", "vegetable")
 # etc
 
-    def __init__(self, x, y, direction, tractor_start, tractor_end):
+    def __init__(self, x, y):
         self.x = x
         self.y = y
+        self.image = 'resources/images/tractor.png'
 
-        self.rect = pygame.Rect(x, y, TILE_SIZE, TILE_SIZE)
-
-        self.direction = direction
-        self.image = pygame.image.load('resources/images/tractor_right.png').convert_alpha()
-
-        self.tractor_start = tractor_start  #important for bfs - prevents from spawning obstacles on these positions
-        self.tractor_end = tractor_end      #
-
-        if (self.direction==1):
-            self.image = pygame.image.load('resources/images/tractor_up.png').convert_alpha()
-        elif (self.direction==3):
-            self.image = pygame.image.load('resources/images/tractor_down.png').convert_alpha()
-        elif (self.direction==4):
-            self.image = pygame.image.load('resources/images/tractor_left.png').convert_alpha()
-        
-
-    def work_on_field(self, screen, tile, ground, plant1):
-        results = []
+    def work_on_field(self, tile, ground, plant1):
         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
@@ -65,7 +41,6 @@ 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
@@ -76,130 +51,10 @@ 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))
-        
-
-
-
-    def move(self):
-        if self.direction == DIRECTION_EAST:
-            self.rect.x += TILE_SIZE
-
-        elif self.direction == DIRECTION_WEST:
-            self.rect.x -= TILE_SIZE
-
-        elif self.direction == DIRECTION_NORTH:
-            self.rect.y -= TILE_SIZE
-        
-        elif self.direction == DIRECTION_SOUTH:
-            self.rect.y += TILE_SIZE
-
-    
-    def rotate_to_right(self):
-        if self.direction == 4:
-            self.direction = 1
-        else:
-            self.direction += 1
-        self.image = pygame.transform.rotate(self.image, -90)
-
-    
-    def rotate_to_left(self):
-        if self.direction == 1:
-            self.direction = 4
-        else:
-            self.direction -= 1
-        self.image = pygame.transform.rotate(self.image, 90)
-
-    #checks if we can move further and if we won't get out of boundaries - for bfs:
-    def can_it_move_node(node):
-        if node.get_direction() == DIRECTION_EAST and node.get_x() + TILE_SIZE < 830:  #last tile on the west has y:797
-            return "move east"
-        elif node.get_direction() == DIRECTION_WEST and node.get_x() - TILE_SIZE >=fieldX:
-            return "move west"
-        elif node.get_direction() == DIRECTION_NORTH and node.get_y() - TILE_SIZE >=fieldY:
-            return "move north"
-        elif node.get_direction() == DIRECTION_SOUTH and node.get_y() + TILE_SIZE < 760: #last tile on the south has y:727
-            return "move south"
-        else:
-            return False
-        
-
-    #checks if there's an obstacle on the given coordinates (important for bfs):
-    def is_obstacle(self, x, y):
-        for tile in tiles:
-            if tile.x == x and tile.y == y:
-                ground = tile.ground
-                if ground.obstacle and self.tractor_start != (x, y) and self.tractor_end != (x,y):
-                    return True
-        return False
-
-
-    def draw_tractor(self, win):
-        imageTractor = pygame.transform.scale(self.image, (TILE_SIZE, TILE_SIZE))
-        win.blit(imageTractor, (self.rect.x, self.rect.y))
-        pygame.display.flip()
-        
-     
-    def store_tile_image(self, tile):
-        return pygame.image.load(tile.image).convert_alpha()
-
-    def restore_tile_image(self, screen, tile):
-        image = pygame.image.load(tile.image).convert_alpha()
-        image = pygame.transform.scale(image, (TILE_SIZE, TILE_SIZE))
-        screen.blit(image, (tile.x, tile.y))
-        pygame.display.update()
-
-
-
-#translates move_list generated by bfs into the actual movement:
-def do_actions(tractor, WIN, move_list):
-    # trail = pygame.image.load("resources/images/background.jpg").convert_alpha()
-    # trail = pygame.transform.scale(trail, (TILE_SIZE, TILE_SIZE))
-    tile_images = {}
-    for tile in tiles:
-        tile_images[(tile.x, tile.y)] = tractor.store_tile_image(tile)
-    
-    pygame.display.update()
-    for move in move_list:
-        # WIN.blit(trail, (tractor.rect.x, tractor.rect.y, TILE_SIZE, TILE_SIZE))
-        current_tile = None
-        for tile in tiles:
-            if tile.x == tractor.rect.x and tile.y == tractor.rect.y:
-                current_tile = tile
-                break
-        if current_tile:
-            tractor.restore_tile_image(WIN, current_tile)
-
-        if move == "move":
-            tractor.move()            
-        elif move == "rotate_right":
-            tractor.rotate_to_right()
-        elif move == "rotate_left":
-            tractor.rotate_to_left()
-
-        tractor.draw_tractor(WIN)
-        pygame.display.update()
-        time.sleep(0.35)
-
-
-#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/astar.py

@@ -1,132 +0,0 @@
-from area.constants import TILE_SIZE, ROWS
-import copy
-import heapq
-from area.field import tiles, fieldX, fieldY
-from bfs import get_moves
-
-class Node:
-
-    def __init__(self, priority, x, y, direction, parent, action, cost=0):
-        self.x = x
-        self.y = y
-        self.direction = direction
-        self.action = action
-        self.parent = parent
-        self.cost = cost
-        self.priority = priority
-
-
-    def __int__(self):
-        return self.priority
-
-        # getters and setters:
-
-    def get_parent(self):
-        return self.parent
-
-    def set_parent(self, _parent):
-        self.parent = _parent
-
-    def get_action(self):
-        return self.action
-
-    def set_action(self, _action):
-        self.parent = _action
-
-    def get_x(self):
-        return self.x
-
-    def set_x(self, _x):
-        self.x = _x
-
-    def get_y(self):
-        return self.y
-
-    def set_y(self, _y):
-        self.y = _y
-
-    def get_direction(self):
-        return self.direction
-
-    def set_direction(self, _direction):
-        self.parent = _direction
-
-    def get_cost(self):
-        return self.cost
-
-    def set_cost(self, _cost):
-        self.cost = _cost
-
-    def get_priority(self):
-        return self.priority
-
-    def set_priority(self, _priority):
-        self.priority = _priority
-
-    def __lt__(self, other):
-        return self.get_priority() < other.get_priority()
-
-    def copy(self):
-        return copy.copy(self)
-
-
-def goal_test(elem, goalstate):
-    if elem.get_x() == goalstate[0] and elem.get_y() == goalstate[1]:
-        return True
-    else:
-        return False
-
-def tile_cost(tile):
-    if tile.image == "resources/images/sampling.png":
-        return 100
-    if tile.image == "resources/images/rock_dirt.png":
-        return 1000
-    else:
-        return 1
-
-def heuristic(current_x, current_y, end_x, end_y):
-    return abs(end_x - current_x) + abs(end_y - current_y)
-# State as a tuple (x,y,direction)
-
-# actions(string): move, rotate_to_left, rotate_to_right
-
-# main search function:
-def a_star(istate, succ_astar, goaltest):
-    fringe = []
-    explored = set()
-    node = Node(0, istate.get_x(), istate.get_y(), istate.get_direction(), None, None, 0)
-    heapq.heappush(fringe, node)
-
-    while True:
-
-        if not fringe:
-            return False
-
-        elem = heapq.heappop(fringe)
-        temp = copy.copy(elem)
-        if goal_test(elem, goaltest) is True:  # jesli True zwroc ciag akcji
-            return get_moves(elem)
-
-        explored.add(elem)
-
-        for (action, state) in succ_astar(temp):
-            fringe_tuple = []
-            explored_tuple = []
-
-            for node in fringe:
-                fringe_tuple.append((node.get_x(), node.get_y(), node.get_direction()))
-            for node in explored:
-                explored_tuple.append((node.get_x(), node.get_y(), node.get_direction()))
-
-
-            tile = int((temp.get_y() - fieldY) / TILE_SIZE) * ROWS + int((temp.get_x() - fieldX) / TILE_SIZE)
-            cost = temp.cost + tile_cost(tiles[tile])
-            priority = cost + heuristic(state[0], state[1], goaltest[0], goaltest[1])
-
-            x = Node(priority, state[0], state[1], state[2], elem, action, cost)
-
-            if state not in fringe_tuple and state not in explored_tuple:
-                heapq.heappush(fringe, x)
-
-            elif state in fringe_tuple and elem.get_priority() < priority:
-                elem.set_priority(priority)

source/bfs.py

@@ -1,207 +0,0 @@
-from area.constants import TILE_SIZE
-import copy
-from area.tractor import Tractor
-
-class Istate:
-
-    def __init__(self, x, y, direction ):
-        self.x = x
-        self.y = y
-        self.direction = direction
-
-
-    def get_x(self):
-        return self.x
-
-    def set_x(self, _x):
-        self.x = _x
-
-    def get_y(self):
-        return self.y
-
-    def set_y(self, _y):
-        self.y = _y   
-    
-    def get_direction(self):
-        return self.direction
-    
-    def set_direction(self, _direction):
-        self.parent = _direction
-
-class Node:
-
-    def __init__(self, x, y, direction, parent, action):
-        self.x = x
-        self.y = y
-        self.direction = direction
-        self.action = action
-        self.parent = parent
-
-
-
-        #getters and setters:
-
-    def get_parent(self):
-        return self.parent
-    
-    def set_parent(self, _parent):
-        self.parent = _parent
-        
-    def get_action(self):
-        return self.action
-    
-    def set_action(self, _action):
-        self.parent = _action
-
-    def get_x(self):
-        return self.x
-
-    def set_x(self, _x):
-        self.x = _x
-
-    def get_y(self):
-        return self.y
-
-    def set_y(self, _y):
-        self.y = _y   
-    
-    def get_direction(self):
-        return self.direction
-    
-    def set_direction(self, _direction):
-        self.parent = _direction
-
-    def copy(self):
-        return copy.copy(self)
-    
-
-def goal_test(elem, goalstate):
-    if elem.get_x() == goalstate[0] and elem.get_y() == goalstate[1]:
-        return True
-    else:
-        return False
-
-
-#State as a tuple (x,y,direction)
-    
-#actions(string): move, rotate_to_left, rotate_to_right
-
-#main search function:
-def graphsearch(istate, succ, goaltest, tractor):
-
-    fringe = []
-    explored = []
-    node = Node(istate.get_x(), istate.get_y(), istate.get_direction(), None, None)
-    fringe.append(node)
-
-    while True:
-
-        if not fringe:
-            return False
-        
-        elem = fringe.pop(0)
-        temp = copy.copy(elem)
-        if goal_test(elem, goaltest) is True:   #jesli True zwroc ciag akcji
-            return get_moves(elem)
-            
-        explored.append(elem)
-
-        for (action, state) in succ(temp, tractor): #dla wszystkich mozliwych stanow i akcjach otrzymanych dla danego wierzcholka
-            fringe_tuple = []
-            explored_tuple = []
-
-            for node in fringe:
-                 fringe_tuple.append((node.get_x(), node.get_y(), node.get_direction()))
-            for node in explored:
-                 explored_tuple.append((node.get_x(), node.get_y(), node.get_direction()))
-
-            
-            if state not in fringe_tuple and state not in explored_tuple:
-                x = Node(state[0], state[1], state[2], elem, action)
-                fringe.append(x)
-
-
-#funkcja nastepnika - jakie akcje sa mozlwie na danym polu i jaki bedzie stan po wykonaniu tych akcji
-def succ(elem, tractor):
-    actions_states = []
-    temp = copy.copy(elem.get_direction())
-    
-    if temp == 1:
-        temp = 4
-    else:
-        temp -= 1
-    actions_states.append(("rotate_left", (elem.get_x(), elem.get_y(), temp)))
-
-    temp = copy.copy(elem.get_direction())   
-    if temp == 4:
-        temp = 1
-    else:
-        temp += 1
-    actions_states.append(("rotate_right", (elem.get_x(), elem.get_y(), temp)))
-   
-    temp_move_east = elem.get_x() + TILE_SIZE
-    temp_move_west = elem.get_x() - TILE_SIZE
-    temp_move_north = elem.get_y() - TILE_SIZE
-    temp_move_south = elem.get_y() + TILE_SIZE
-
-    if Tractor.can_it_move_node(elem) == "move east" and not Tractor.is_obstacle(tractor, temp_move_east, elem.get_y()):
-         actions_states.append(("move", (temp_move_east, elem.get_y(), elem.get_direction())))
-
-    elif Tractor.can_it_move_node(elem) == "move west" and not Tractor.is_obstacle(tractor, temp_move_west, elem.get_y()):
-         actions_states.append(("move", (temp_move_west, elem.get_y(), elem.get_direction())))
-
-    elif Tractor.can_it_move_node(elem) == "move north" and not Tractor.is_obstacle(tractor,elem.get_x(), temp_move_north):
-         actions_states.append(("move", (elem.get_x(), temp_move_north, elem.get_direction())))
-
-    elif Tractor.can_it_move_node(elem) == "move south" and not Tractor.is_obstacle(tractor, elem.get_x(), temp_move_south):
-         actions_states.append(("move", (elem.get_x(), temp_move_south, elem.get_direction())))
-
-    return actions_states
- 
- 
- #its the copy of successor function for A* only - tractor can ride through stones if there is no other way:
-def succ_astar(elem):
-    actions_states = []
-    temp = copy.copy(elem.get_direction())
-    
-    if temp == 1:
-        temp = 4
-    else:
-        temp -= 1
-    actions_states.append(("rotate_left", (elem.get_x(), elem.get_y(), temp)))
-
-    temp = copy.copy(elem.get_direction())   
-    if temp == 4:
-        temp = 1
-    else:
-        temp += 1
-    actions_states.append(("rotate_right", (elem.get_x(), elem.get_y(), temp)))
-   
-    temp_move_east = elem.get_x() + TILE_SIZE
-    temp_move_west = elem.get_x() - TILE_SIZE
-    temp_move_north = elem.get_y() - TILE_SIZE
-    temp_move_south = elem.get_y() + TILE_SIZE
-
-    if Tractor.can_it_move_node(elem) == "move east":
-         actions_states.append(("move", (temp_move_east, elem.get_y(), elem.get_direction())))
-
-    elif Tractor.can_it_move_node(elem) == "move west":
-         actions_states.append(("move", (temp_move_west, elem.get_y(), elem.get_direction())))
-
-    elif Tractor.can_it_move_node(elem) == "move north":
-         actions_states.append(("move", (elem.get_x(), temp_move_north, elem.get_direction())))
-
-    elif Tractor.can_it_move_node(elem) == "move south":
-         actions_states.append(("move", (elem.get_x(), temp_move_south, elem.get_direction())))
-
-    return actions_states
-
-#returns list of actions      
-def get_moves(elem):
-    move_list = []
-    while (elem.get_parent() != None):
-          move_list.append(elem.get_action())
-          elem = elem.get_parent()
-    move_list.reverse()
-    return move_list
-

source/decision_tree.py

@@ -1,45 +0,0 @@
-import pandas as pd
-from sklearn.model_selection import train_test_split
-import joblib
-from sklearn.tree import DecisionTreeClassifier, plot_tree
-import matplotlib.pyplot as plt
-dtype_dict = {
-    'season': str,
-    'anomalies': bool,
-    'weather': str,
-    'temp': int,
-    'water': int,
-    'nutri': int,
-    'pests': int,
-    'weeds': int,
-    'type': str,
-    'ripeness': int,
-    'target_column': str
-}
-# Wczytaj dane z pliku CSV
-data = pd.read_csv('resources/dataset.csv', header=0, dtype=dtype_dict)
-#print(data)
-X = data.drop('target_column', axis=1)
-y = data['target_column']
-X = pd.get_dummies(X)
-# X.to_csv('model_data1.csv', index=False)
-
-
-# Podział danych na zbiór treningowy i testowy
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-# Dopasowanie modelu drzewa decyzyjnego
-model = DecisionTreeClassifier()
-model.fit(X_train, y_train)
-
-
-fig = plt.figure(figsize=(25, 20))
-_ = plot_tree(model, feature_names=X.columns, class_names=model.classes_, filled=True)
-plt.savefig('drzewo_decyzyjne.png')
-plt.show()
-
-# 'model' to wcześniej wytrenowany model drzewa decyzyjnego
-joblib.dump(model, 'model.pkl')
-
-
-

source/genetic.py

@@ -1,98 +0,0 @@
-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

@@ -12,7 +12,7 @@ class Dirt:
 #   add a couple new properties
 
     def pests_and_weeds(self):
-        i = random.randint(1, 20)   # 5% szans na szkodniki, 10% na chwasty, 5 na obydwa 5 na kamien
+        i = random.randint(1, 20)   # 5% szans na szkodniki, 10% na chwasty, 5 na obydwa 15 na kamien
         if i == 1:
             self.pest = True
         elif i == 2 or i == 3:
@@ -20,10 +20,7 @@ class Dirt:
         elif i == 4:
             self.weed = True
             self.pest = True
-        '''elif i == 5:
-            self.obstacle = True'''
-
-    def set_ocstacle(self, obstacle_status):
-        self.obstacle = obstacle_status
+        elif i == 5 or i == 6 or i == 7:
+            self.obstacle = True
 
 # add init, getters,setters

source/main.py

@@ -1,26 +1,14 @@
 import pygame
 import time
 import random
-import pandas as pd
-import joblib
-from area.constants import WIDTH, HEIGHT, TILE_SIZE
+
+from area.constants import WIDTH, HEIGHT
 from area.field import drawWindow
-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 area.tractor import Tractor
+from area.field import tiles
 from ground import Dirt
 from plant import Plant
-from bfs import graphsearch, Istate, succ_astar, succ
-from astar import a_star
-from NN.neural_network import load_model, load_image, guess_image, display_image, display_result, clear_text_area
-from PIL import Image
-from genetic import genetic_algorithm
-
-from area.field import createTiles
-
-pygame.init()
-WIN_WIDTH = WIDTH + 300
-WIN = pygame.display.set_mode((WIN_WIDTH, HEIGHT))
+WIN = pygame.display.set_mode((WIDTH, HEIGHT))
 pygame.display.set_caption('Intelligent tractor')
 
 
@@ -28,200 +16,24 @@ def main():
     run = True
     window = drawWindow(WIN)
     pygame.display.update()
-
-
-#Tractor initialization:
-    tractor = Tractor(0*TILE_SIZE, 0*TILE_SIZE, 2, None, None)
-    tractor.rect.x += fieldX  
-    tractor.rect.y += fieldY
-    tractor.tractor_start = ((170, 100))
-    istate = Istate(170, 100, 2)  #initial state
-
-
-#main loop:    
     while run:
         for event in pygame.event.get():
             if event.type == pygame.QUIT:
-                run = False       
-        time.sleep(1)
+                run = False
 
-        #getting coordinates of our "goal tile":
-        tile_index = get_tile_index()
-        tile_x, tile_y = get_tile_coordinates(tile_index)
-        if tile_x is not None and tile_y is not None:
-            print(f"Coordinates of tile {tile_index} are: ({tile_x}, {tile_y})")
-        else: print("Such tile does not exist")
-
-        #mark the goal tile:
-        tiles[tile_index].image = "resources/images/sampling_goal.png"
-        image = pygame.image.load(tiles[tile_index].image).convert()
-        image = pygame.transform.scale(image, (TILE_SIZE, TILE_SIZE))
-        WIN.blit(image, (tiles[tile_index].x, tiles[tile_index].y))
-        pygame.display.flip()
-
-
-        tractor.tractor_end = ((tile_x, tile_y))
-        goaltest = []    #final state (consists of x and y because direction doesnt matter)   
-        goaltest.append(tile_x)
-        goaltest.append(tile_y)
-        goaltest[0] = tile_x
-        goaltest[1]=tile_y        
-       
-        #moves = (graphsearch(istate, succ, goaltest, tractor))     #<-------BFS
-        moves = (a_star(istate, succ_astar, goaltest))
-        print(moves)
-
-
-        # movement based on route-planning:
-        tractor.draw_tractor(WIN)
-        time.sleep(1)
-        if moves != False:
-            do_actions(tractor, WIN, moves)
-
-
-        #guessing the image under the tile:
-        goalTile = tiles[tile_index]
-        image_path = goalTile.photo
-        display_image(WIN, goalTile.photo, (WIDTH-20 , 300))   #displays photo next to the field
-        pygame.display.update()
-
-        image_tensor = load_image(image_path)
-        prediction = guess_image(load_model(), image_tensor)
-        
-        clear_text_area(WIN, WIDTH - 50, 600, 400, 50)
-        display_result(WIN, (WIDTH - 50 , 600), prediction)      #display text under the photo
-        pygame.display.update()
-        print(f"The predicted image is: {prediction}")
-
-
-        p1 = Plant('wheat', 'cereal', random.randint(1,100), random.randint(1,100), random.randint(1,100))
-        goalTile.plant = p1
+        #small test:        
+        time.sleep(2)
+        tile1 = tiles[0]
+        p1 = Plant('wheat', 'cereal', random.randint(1,100), random.randint(1,100), random.randint(1,100))      
         d1 = Dirt(random.randint(1, 100), random.randint(1,100))
         d1.pests_and_weeds()
-        goalTile.ground=d1
-        #getting the name and type of the recognized plant:
-        p1.update_name(prediction)
-        
-        
-#decission tree test:
-        if d1.pest:
-            pe = 1
-        else:
-            pe = 0
-        if d1.weed:
-            we = 1
-        else:
-            we = 0
-        if p1.plant_type == 'cereal':
-            t1 = True
-            t2 = False
-            t3 = False
-            t4 = False
-        else:
-            t1 = False
-            if p1.plant_type == 'fruit':
-                t2 = True
-                t3 = False
-                t4 = False
-            else:
-                t2 = False
-                if p1.plant_type == 'vegetable':
-                    t4 = True
-                    t3 = False
-                else:
-                    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': [a1],
-                'temp': [temp_n],
-                'water': [d1.water_level],
-                'nutri': [d1.nutrients_level],
-                'pests': [pe],
-                'weeds': [we],
-                'ripeness': [p1.growth_level],
-                'season_autumn': [s1], 'season_spring': [s2], 'season_summer': [s3], 'season_winter': [s4],
-                'weather_heavyCloudy': [h1], 'weather_partCloudy': [h2], 'weather_precipitation': [h3],
-                'weather_sunny': [h4],
-                'type_cereal': [t1], 'type_fruit': [t2], 'type_none': [t3], 'type_vegetable': [t4]
-        }
-        
-        df = pd.DataFrame(dane)
-        df.to_csv('model_data.csv', index=False)
-
-        model = joblib.load('model.pkl')
-        nowe_dane = pd.read_csv('model_data.csv')
-        predykcje = model.predict(nowe_dane)
-        print(predykcje)
-
-        #work on field:
-        if predykcje == 'work':
-            tractor.work_on_field(WIN, goalTile, d1, p1)
-
-        #update the initial state for the next target:
-        istate = Istate(tile_x, tile_y, tractor.direction)
-
-        #old goalTile is displayed with a black border - to show that it was an old target:
-        tiles[tile_index].image = "resources/images/sampling_old_goal.png"
-        image = pygame.image.load(tiles[tile_index].image).convert()
-        image = pygame.transform.scale(image, (TILE_SIZE, TILE_SIZE))
-        WIN.blit(image, (tiles[tile_index].x, tiles[tile_index].y))
-        pygame.display.flip()
-        tractor.draw_tractor(WIN)
-
-        time.sleep(2)
+        tile1.ground=d1
+        t1 = Tractor(10, 10)        
+        t1.work_on_field(tile1, d1, p1)
+        time.sleep(3)
         print("\n")
 
 
+#        in loop move tractor
+
 main()

source/model_data.csv

@@ -1,2 +0,0 @@
-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,17,72,73,0,0,60,True,False,False,False,False,False,False,True,False,True,False,False

source/plant.py

@@ -19,19 +19,7 @@ class Plant:
         else:
             print("Unable to grow due to bad condition of the ground")
 
-    def update_name(self, predicted_class):
-        if predicted_class == "Apple":
-            self.name = "apple"
-            self.plant_type = 'fruit'
+#   more properties
 
-        elif predicted_class == "Radish":
-            self.name = "radish"
-            self.plant_type = 'vegetable'
 
-        elif predicted_class == "Cauliflower":
-            self.name = "cauliflower"
-            self.plant_type = 'vegetable'
-
-        elif predicted_class == "Wheat":
-            self.name = "wheat"
-            self.plant_type = 'cereal'
+# add init, getters,setters

source/resources/dataset.csv

@@ -1,301 +0,0 @@
-season,anomalies,weather,temp,water,nutri,pests,weeds,type,ripeness,target_column
-winter,False,precipitation,5,44,17,1,0,vegetable,76,doNothing
-spring,False,heavyCloudy,12,22,83,0,0,cereal,83,work
-summer,False,sunny,25,85,84,0,0,cereal,85,work
-summer,False,precipitation,24,24,67,0,0,none,0,work
-spring,False,sunny,4,44,35,1,0,vegetable,96,work
-summer,False,partCloudy,33,78,54,0,1,cereal,89,work
-winter,False,heavyCloudy,-1,27,87,1,1,cereal,1,doNothing
-autumn,True,sunny,15,45,99,1,1,cereal,49,doNothing
-autumn,False,partCloudy,20,54,81,0,1,none,0,work
-winter,False,heavyCloudy,-5,50,49,0,1,fruit,42,doNothing
-summer,False,heavyCloudy,35,95,31,1,1,none,0,work
-summer,False,partCloudy,36,14,42,0,1,vegetable,96,work
-summer,False,precipitation,31,62,45,1,0,vegetable,3,work
-summer,True,precipitation,35,35,41,1,0,none,0,doNothing
-summer,True,heavyCloudy,35,33,97,0,0,fruit,76,work
-autumn,False,sunny,3,24,62,1,1,vegetable,21,work
-winter,False,partCloudy,-18,80,37,0,0,cereal,24,doNothing
-spring,True,precipitation,21,15,58,1,0,fruit,49,doNothing
-autumn,False,precipitation,16,62,75,1,0,fruit,69,work
-winter,False,heavyCloudy,-11,69,9,0,1,none,0,doNothing
-autumn,False,sunny,21,6,24,0,0,vegetable,99,work
-summer,False,heavyCloudy,23,1,33,0,0,none,0,work
-autumn,False,sunny,18,80,42,0,0,vegetable,47,work
-summer,False,partCloudy,35,69,38,1,0,vegetable,40,work
-summer,False,heavyCloudy,27,98,34,0,1,vegetable,20,work
-winter,False,heavyCloudy,-3,9,3,0,1,none,0,doNothing
-summer,False,heavyCloudy,26,79,28,1,0,fruit,20,work
-spring,False,precipitation,3,9,53,0,1,vegetable,30,doNothing
-autumn,False,sunny,6,31,1,1,0,fruit,31,work
-autumn,False,sunny,8,2,70,1,0,vegetable,54,work
-spring,False,precipitation,8,21,1,1,0,fruit,92,doNothing
-autumn,False,heavyCloudy,11,4,32,1,1,fruit,63,work
-winter,True,precipitation,-10,57,67,1,1,cereal,43,doNothing
-winter,False,precipitation,-13,90,14,1,1,fruit,29,doNothing
-autumn,True,precipitation,10,68,46,0,0,fruit,18,doNothing
-spring,False,heavyCloudy,8,41,55,1,1,fruit,12,work
-winter,False,heavyCloudy,-10,24,25,0,0,vegetable,32,doNothing
-spring,False,heavyCloudy,0,40,6,0,0,none,0,work
-autumn,False,sunny,22,7,88,0,1,vegetable,71,work
-spring,True,precipitation,0,56,21,0,0,cereal,46,doNothing
-autumn,False,sunny,13,37,49,0,1,vegetable,94,work
-summer,False,precipitation,20,14,39,0,0,cereal,97,work
-autumn,False,heavyCloudy,18,74,38,1,0,vegetable,32,work
-autumn,False,heavyCloudy,2,100,70,0,0,none,0,work
-autumn,False,precipitation,19,30,16,0,1,cereal,17,work
-summer,False,sunny,26,94,83,1,1,fruit,25,work
-autumn,False,heavyCloudy,14,91,62,0,0,vegetable,25,work
-autumn,False,partCloudy,12,34,98,0,0,none,0,work
-spring,True,heavyCloudy,5,64,50,0,0,vegetable,31,doNothing
-summer,True,heavyCloudy,30,60,55,0,0,none,0,doNothing
-summer,False,sunny,22,35,2,0,1,vegetable,20,work
-spring,False,sunny,21,87,22,0,1,cereal,45,work
-winter,False,heavyCloudy,-2,48,83,0,1,vegetable,3,doNothing
-winter,False,heavyCloudy,-15,55,6,0,1,none,0,doNothing
-summer,False,heavyCloudy,35,5,68,1,0,vegetable,79,work
-summer,True,heavyCloudy,35,99,35,1,0,fruit,79,doNothing
-winter,False,sunny,-1,35,57,1,0,vegetable,80,doNothing
-summer,True,precipitation,31,55,73,0,1,vegetable,24,doNothing
-autumn,True,precipitation,21,26,84,0,0,vegetable,49,doNothing
-winter,False,precipitation,-8,13,30,1,0,none,0,doNothing
-autumn,False,heavyCloudy,20,58,15,1,0,cereal,56,work
-spring,False,partCloudy,12,25,61,1,1,vegetable,11,work
-winter,False,partCloudy,-14,58,54,0,1,none,0,doNothing
-autumn,False,sunny,6,4,96,1,0,cereal,42,work
-summer,False,sunny,29,74,98,0,1,vegetable,22,work
-summer,False,sunny,32,83,90,1,0,none,0,work
-summer,True,sunny,33,11,11,0,1,fruit,45,work
-spring,False,sunny,3,54,13,1,1,none,0,work
-autumn,False,heavyCloudy,20,67,97,0,0,cereal,68,doNothing
-spring,False,sunny,5,35,54,0,0,vegetable,37,work
-summer,False,precipitation,31,44,68,1,0,vegetable,9,work
-winter,False,heavyCloudy,-11,59,18,1,1,cereal,11,doNothing
-summer,False,precipitation,30,32,57,1,1,fruit,18,work
-summer,False,sunny,27,31,54,0,1,vegetable,67,work
-summer,False,precipitation,29,70,73,1,0,fruit,71,work
-spring,False,sunny,18,67,22,1,0,fruit,64,work
-spring,False,partCloudy,13,40,18,1,0,fruit,7,work
-summer,False,precipitation,23,24,6,1,0,vegetable,90,work
-autumn,False,heavyCloudy,1,59,84,0,0,none,0,work
-spring,False,precipitation,2,47,99,0,0,vegetable,83,doNothing
-summer,False,precipitation,32,9,94,1,0,cereal,18,work
-winter,False,partCloudy,-14,56,74,1,1,fruit,24,doNothing
-winter,False,sunny,-5,100,55,0,0,cereal,57,doNothing
-summer,False,sunny,30,99,99,1,1,cereal,31,work
-winter,True,precipitation,-13,56,58,1,0,none,0,doNothing
-spring,False,precipitation,1,24,21,1,1,none,0,work
-spring,False,heavyCloudy,2,38,56,0,0,fruit,9,work
-winter,False,partCloudy,-7,60,71,0,0,cereal,70,doNothing
-autumn,False,precipitation,2,71,2,0,1,cereal,26,doNothing
-autumn,False,sunny,19,99,7,1,1,none,0,work
-spring,False,sunny,8,41,35,0,1,vegetable,89,work
-winter,False,sunny,-14,58,94,0,1,vegetable,29,doNothing
-summer,True,precipitation,30,3,58,1,0,none,0,doNothing
-winter,False,precipitation,8,17,76,1,0,vegetable,47,doNothing
-spring,True,precipitation,20,38,65,0,1,cereal,49,doNothing
-winter,False,partCloudy,-4,8,10,0,1,cereal,74,doNothing
-autumn,False,sunny,2,100,7,1,1,none,0,work
-autumn,False,partCloudy,17,88,89,0,1,vegetable,17,work
-autumn,False,partCloudy,16,76,54,1,0,vegetable,98,work
-spring,False,sunny,2,14,25,0,1,vegetable,8,work
-spring,False,precipitation,5,34,1,1,0,none,0,work
-autumn,False,heavyCloudy,9,20,28,0,0,cereal,88,work
-autumn,False,heavyCloudy,20,17,84,0,0,cereal,15,work
-winter,False,heavyCloudy,-18,43,33,0,1,cereal,31,doNothing
-autumn,False,partCloudy,2,1,3,1,1,cereal,86,work
-summer,False,heavyCloudy,34,58,23,0,0,cereal,27,work
-summer,False,precipitation,31,22,14,0,1,fruit,33,work
-winter,False,sunny,-5,98,51,1,0,none,0,doNothing
-winter,False,precipitation,-17,74,78,1,0,none,0,doNothing
-summer,False,precipitation,29,3,54,0,0,none,0,work
-autumn,False,precipitation,7,45,29,1,1,cereal,52,doNothing
-spring,False,sunny,7,21,30,0,1,fruit,38,work
-summer,False,heavyCloudy,28,73,75,1,1,none,0,work
-summer,False,heavyCloudy,32,49,43,0,0,vegetable,74,work
-autumn,True,precipitation,0,99,66,0,0,none,0,doNothing
-summer,True,partCloudy,32,46,99,0,1,none,0,work
-autumn,False,heavyCloudy,21,26,44,1,0,none,0,work
-summer,True,precipitation,30,28,89,1,1,cereal,65,doNothing
-autumn,False,heavyCloudy,20,100,46,1,0,fruit,25,work
-winter,False,partCloudy,-3,78,63,1,0,vegetable,18,doNothing
-spring,False,partCloudy,22,8,86,1,1,none,0,work
-winter,False,partCloudy,2,34,30,1,0,fruit,95,doNothing
-summer,True,partCloudy,31,35,26,0,0,fruit,3,work
-winter,False,partCloudy,9,80,24,1,1,vegetable,67,doNothing
-winter,False,heavyCloudy,-19,85,14,1,1,vegetable,33,doNothing
-autumn,False,partCloudy,3,15,6,0,0,vegetable,11,work
-winter,False,partCloudy,10,20,62,1,1,fruit,90,doNothing
-autumn,False,precipitation,11,93,90,1,0,vegetable,87,work
-summer,False,partCloudy,22,79,60,1,1,none,0,work
-winter,True,precipitation,-6,55,88,1,0,vegetable,64,doNothing
-winter,False,precipitation,-13,31,62,0,1,fruit,70,doNothing
-autumn,False,precipitation,20,90,2,1,1,fruit,73,work
-winter,False,heavyCloudy,9,100,76,1,1,cereal,41,doNothing
-summer,False,partCloudy,30,8,90,1,1,cereal,15,work
-winter,False,heavyCloudy,4,27,77,1,1,vegetable,99,doNothing
-summer,False,partCloudy,33,67,34,0,0,cereal,44,work
-summer,True,precipitation,34,28,100,1,0,vegetable,64,work
-winter,True,precipitation,-9,34,88,1,1,cereal,5,doNothing
-spring,False,heavyCloudy,10,33,54,1,0,fruit,63,work
-spring,False,precipitation,0,77,32,0,0,fruit,100,doNothing
-autumn,False,sunny,3,7,59,1,0,cereal,62,work
-winter,False,heavyCloudy,-2,50,37,0,0,vegetable,51,doNothing
-summer,False,partCloudy,33,74,88,1,1,fruit,49,work
-spring,False,sunny,14,14,29,1,1,none,0,work
-summer,False,heavyCloudy,24,98,18,1,0,vegetable,99,work
-winter,False,heavyCloudy,-15,52,38,0,1,vegetable,66,doNothing
-spring,True,partCloudy,9,62,24,0,1,fruit,67,doNothing
-spring,False,precipitation,2,87,100,1,0,vegetable,39,doNothing
-spring,False,precipitation,16,94,45,0,0,vegetable,35,work
-winter,False,precipitation,5,41,82,0,1,fruit,80,doNothing
-spring,False,partCloudy,13,49,4,1,0,cereal,97,work
-winter,False,partCloudy,-17,24,42,1,1,fruit,71,doNothing
-spring,False,heavyCloudy,20,45,71,1,1,vegetable,58,work
-winter,False,precipitation,-8,78,81,1,1,fruit,8,doNothing
-spring,False,partCloudy,5,31,82,0,1,fruit,18,work
-autumn,False,sunny,6,75,85,0,0,fruit,18,doNothing
-spring,False,precipitation,14,9,1,1,0,none,0,work
-winter,False,heavyCloudy,2,37,5,1,1,cereal,1,doNothing
-summer,False,precipitation,20,44,28,1,1,none,0,work
-winter,False,partCloudy,-4,97,90,1,1,vegetable,94,doNothing
-summer,True,precipitation,26,46,55,1,0,cereal,91,doNothing
-spring,False,sunny,15,97,67,1,1,none,0,work
-summer,False,partCloudy,33,40,14,1,0,none,0,work
-spring,False,partCloudy,4,23,51,0,0,vegetable,8,work
-summer,False,sunny,20,66,76,1,1,vegetable,71,work
-winter,False,sunny,-11,2,98,0,1,vegetable,29,doNothing
-spring,False,sunny,9,67,74,0,0,none,0,work
-autumn,False,precipitation,17,23,94,1,1,none,0,work
-autumn,False,heavyCloudy,22,65,69,0,1,fruit,79,work
-winter,False,heavyCloudy,-2,35,82,1,1,cereal,20,doNothing
-winter,True,heavyCloudy,-10,1,73,1,0,fruit,77,doNothing
-autumn,False,sunny,4,58,71,1,1,fruit,94,work
-summer,False,sunny,32,44,20,1,1,vegetable,48,work
-summer,False,heavyCloudy,31,68,37,1,0,cereal,42,work
-winter,False,heavyCloudy,-1,69,34,0,0,none,0,doNothing
-spring,False,heavyCloudy,10,27,43,1,1,vegetable,49,work
-summer,False,partCloudy,26,86,12,0,1,none,0,work
-winter,True,precipitation,5,62,62,1,1,cereal,85,doNothing
-winter,False,partCloudy,9,29,83,1,0,vegetable,14,doNothing
-spring,False,sunny,8,22,33,0,1,cereal,23,work
-summer,False,partCloudy,34,38,2,1,1,fruit,29,work
-autumn,False,heavyCloudy,2,65,91,1,1,vegetable,49,work
-spring,False,sunny,3,73,32,0,0,fruit,61,work
-spring,False,precipitation,7,68,70,0,0,none,0,work
-winter,False,partCloudy,10,68,2,0,0,cereal,80,doNothing
-winter,False,precipitation,-16,89,7,0,0,fruit,92,doNothing
-winter,False,precipitation,0,31,49,0,1,cereal,18,doNothing
-spring,False,heavyCloudy,7,44,86,1,1,none,0,work
-summer,False,sunny,24,1,75,0,1,none,0,work
-summer,True,heavyCloudy,39,3,100,1,0,cereal,30,work
-winter,False,heavyCloudy,2,88,69,1,1,cereal,79,doNothing
-spring,False,heavyCloudy,16,100,21,0,1,cereal,57,work
-autumn,True,heavyCloudy,1,17,62,0,1,cereal,92,doNothing
-autumn,False,sunny,1,69,54,0,0,cereal,62,work
-autumn,False,sunny,17,62,84,0,1,vegetable,18,work
-summer,False,heavyCloudy,24,57,84,0,1,vegetable,1,work
-spring,False,partCloudy,8,80,61,0,0,none,0,work
-autumn,False,heavyCloudy,1,93,79,0,0,fruit,62,work
-winter,False,sunny,-4,51,33,1,0,none,0,doNothing
-autumn,False,precipitation,4,51,73,1,1,fruit,57,doNothing
-autumn,True,heavyCloudy,5,88,81,0,1,fruit,25,doNothing
-summer,False,precipitation,30,26,45,0,0,none,0,work
-winter,False,partCloudy,3,88,46,1,0,none,0,doNothing
-autumn,False,heavyCloudy,8,20,34,0,1,fruit,62,work
-spring,False,partCloudy,15,34,33,0,0,vegetable,16,work
-winter,False,heavyCloudy,-20,80,25,1,0,vegetable,45,doNothing
-spring,False,precipitation,6,34,78,1,0,cereal,6,doNothing
-spring,True,heavyCloudy,3,97,65,1,0,fruit,21,doNothing
-summer,False,precipitation,34,49,65,1,0,vegetable,10,work
-summer,True,precipitation,26,29,2,0,0,cereal,56,doNothing
-autumn,False,partCloudy,10,3,19,0,0,vegetable,67,work
-spring,False,sunny,19,60,71,0,1,cereal,5,work
-winter,False,partCloudy,5,11,66,0,0,none,0,doNothing
-winter,False,heavyCloudy,-13,86,29,1,1,cereal,98,doNothing
-summer,False,precipitation,33,19,2,1,1,cereal,78,work
-spring,False,precipitation,13,6,40,0,0,cereal,49,work
-summer,False,partCloudy,31,15,70,1,0,cereal,100,work
-spring,False,heavyCloudy,8,40,1,0,0,cereal,83,work
-spring,False,partCloudy,10,77,95,1,1,fruit,85,work
-spring,False,partCloudy,3,96,32,1,1,fruit,16,work
-winter,False,sunny,-11,24,5,1,0,none,0,doNothing
-summer,True,heavyCloudy,34,14,11,0,0,vegetable,36,doNothing
-summer,True,precipitation,34,69,54,1,0,none,0,doNothing
-spring,True,precipitation,21,92,44,0,0,fruit,34,doNothing
-spring,False,sunny,10,58,14,1,1,none,0,work
-spring,False,partCloudy,9,22,68,0,0,vegetable,85,work
-winter,False,precipitation,-20,62,18,0,1,vegetable,78,doNothing
-spring,False,sunny,10,51,7,1,1,none,0,work
-autumn,True,heavyCloudy,19,89,2,1,0,cereal,6,doNothing
-winter,False,heavyCloudy,-6,99,10,0,1,fruit,70,doNothing
-spring,False,sunny,2,84,49,1,0,none,0,work
-summer,False,heavyCloudy,27,11,32,0,1,cereal,22,work
-summer,False,partCloudy,27,55,39,1,1,none,0,work
-winter,False,heavyCloudy,-13,53,1,1,1,none,0,doNothing
-summer,False,partCloudy,27,15,78,1,1,cereal,43,work
-winter,False,partCloudy,-4,79,5,0,0,vegetable,1,doNothing
-winter,False,sunny,-1,6,31,0,0,fruit,13,doNothing
-summer,True,precipitation,31,86,32,0,1,fruit,98,doNothing
-winter,False,precipitation,-2,96,89,1,1,cereal,57,doNothing
-spring,False,sunny,18,28,92,1,0,fruit,20,work
-summer,False,precipitation,27,90,37,1,0,cereal,55,work
-summer,False,heavyCloudy,32,41,3,0,0,cereal,35,work
-autumn,False,precipitation,2,31,90,1,0,cereal,7,doNothing
-winter,False,partCloudy,-9,74,28,1,1,fruit,72,doNothing
-summer,False,partCloudy,23,8,99,0,1,none,0,work
-spring,False,heavyCloudy,8,85,29,0,0,none,0,work
-winter,False,sunny,-6,67,100,1,1,fruit,25,doNothing
-winter,False,partCloudy,10,88,85,1,1,cereal,68,doNothing
-spring,False,sunny,20,99,73,0,0,vegetable,18,work
-spring,False,sunny,12,52,22,0,0,none,0,work
-winter,False,sunny,-18,16,57,0,0,vegetable,69,doNothing
-winter,False,partCloudy,-13,17,10,0,0,vegetable,93,doNothing
-summer,False,partCloudy,23,18,24,0,1,fruit,7,work
-spring,False,partCloudy,2,50,95,1,1,vegetable,49,work
-summer,False,partCloudy,23,51,40,1,0,none,0,work
-summer,False,partCloudy,28,61,28,1,0,cereal,61,work
-summer,False,precipitation,25,37,20,1,0,vegetable,18,work
-spring,False,sunny,11,48,62,0,0,fruit,94,work
-spring,True,heavyCloudy,1,85,91,0,0,none,0,doNothing
-autumn,False,partCloudy,21,13,92,1,0,vegetable,66,work
-autumn,False,precipitation,2,25,95,0,0,cereal,18,doNothing
-autumn,False,precipitation,0,50,32,0,1,vegetable,92,doNothing
-winter,False,partCloudy,-18,27,2,0,1,none,0,doNothing
-autumn,False,precipitation,7,49,6,0,0,fruit,92,doNothing
-autumn,False,precipitation,19,21,48,1,0,cereal,93,work
-autumn,False,precipitation,7,54,12,0,0,none,0,work
-spring,False,sunny,1,25,30,1,0,fruit,20,work
-autumn,False,partCloudy,9,80,44,0,0,vegetable,49,work
-summer,False,heavyCloudy,20,95,36,0,1,fruit,11,work
-winter,False,partCloudy,-17,3,9,1,1,vegetable,94,doNothing
-winter,False,partCloudy,-15,62,35,0,1,fruit,51,doNothing
-summer,False,sunny,35,6,24,0,0,cereal,94,work
-summer,False,partCloudy,34,95,37,1,0,vegetable,86,work
-summer,True,precipitation,36,25,83,1,0,fruit,87,work
-spring,False,heavyCloudy,12,29,32,0,0,cereal,86,work
-winter,False,sunny,-15,44,70,0,0,fruit,82,doNothing
-winter,False,heavyCloudy,3,23,18,1,1,fruit,60,doNothing
-summer,False,precipitation,20,71,73,1,0,cereal,3,work
-winter,False,precipitation,-4,90,10,1,1,cereal,51,doNothing
-autumn,False,sunny,4,70,73,1,1,cereal,15,work
-winter,False,heavyCloudy,10,81,76,1,1,vegetable,80,doNothing
-spring,False,partCloudy,2,2,22,1,1,vegetable,21,work
-spring,False,precipitation,17,4,67,1,0,cereal,69,work
-spring,False,heavyCloudy,10,48,55,0,1,cereal,100,work
-autumn,False,precipitation,3,29,41,0,1,none,0,work
-spring,False,precipitation,13,58,27,0,1,fruit,16,work
-summer,False,partCloudy,28,85,14,0,1,none,0,work
-summer,True,heavyCloudy,30,19,15,0,1,vegetable,49,doNothing
-summer,True,heavyCloudy,30,74,21,1,0,cereal,16,doNothing
-summer,False,heavyCloudy,33,38,35,0,0,vegetable,91,work
-spring,False,partCloudy,1,68,59,1,1,vegetable,14,work
-summer,False,precipitation,22,41,14,0,1,cereal,48,work
-autumn,False,precipitation,8,66,44,0,0,cereal,65,doNothing
-spring,False,sunny,7,35,72,1,0,fruit,92,work
-summer,False,heavyCloudy,24,36,81,0,1,cereal,6,work
-winter,False,heavyCloudy,-5,26,27,0,0,cereal,78,doNothing
-summer,False,sunny,27,45,40,1,0,fruit,57,work
-spring,False,precipitation,8,30,79,1,1,none,0,work
-winter,False,sunny,7,76,71,0,1,fruit,75,doNothing
-winter,False,precipitation,9,49,5,1,0,fruit,7,doNothing