diff --git a/__pycache__/settings.cpython-310.pyc b/__pycache__/settings.cpython-310.pyc index f119756..f5ea6d4 100644 Binary files a/__pycache__/settings.cpython-310.pyc and b/__pycache__/settings.cpython-310.pyc differ diff --git a/assets/data/train_2.csv b/assets/data/train_2.csv new file mode 100644 index 0000000..c9d93c6 --- /dev/null +++ b/assets/data/train_2.csv @@ -0,0 +1,202 @@ +Wzrost;wilgotnosc;dni_od_nawiezienia;aktualna_pogoda;czy_roslina_robaczywa;typ_rosliny;pojemnosc_ekwipunku;cena_sprzedarzy;czy_zebrac +54;19;15;0;0;0;90;66;0 +64;63;5;0;0;0;16;4;0 +93;0;29;0;1;0;77;73;1 +30;43;23;0;1;0;74;75;1 +48;30;10;0;0;0;39;23;0 +44;86;2;0;1;0;41;64;0 +99;74;8;0;1;0;39;37;0 +70;80;25;0;1;0;11;90;1 +62;35;2;0;1;0;53;57;0 +32;71;29;0;1;0;21;54;1 +59;27;11;0;0;0;71;68;0 +43;97;24;0;0;0;82;70;0 +24;49;1;0;0;0;22;40;0 +60;59;18;0;1;0;29;99;1 +100;87;23;0;1;0;69;55;1 +5;88;24;0;1;0;54;87;1 +35;92;17;0;1;0;30;100;1 +9;89;29;0;0;0;35;24;0 +58;7;11;0;1;0;6;62;1 +98;88;1;0;1;0;100;88;0 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+28;79;11;0;1;0;95;19;1 +20;4;11;0;0;0;96;64;0 +37;80;14;0;1;0;97;64;1 +2;24;23;0;0;0;63;52;0 +54;97;18;0;1;0;20;65;1 +42;44;20;0;0;0;33;20;0 +68;51;12;0;1;0;76;90;1 +93;77;24;0;1;0;41;59;1 +74;94;21;0;1;0;17;39;1 +63;63;26;0;1;0;67;2;1 +65;73;1;0;0;0;74;40;0 +100;39;19;0;0;0;41;9;0 diff --git a/main.py b/main.py index 99686d4..22aa97e 100644 --- a/main.py +++ b/main.py @@ -2,10 +2,15 @@ import pygame import sys 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 +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 pygame.init() @@ -30,18 +35,53 @@ 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() + + # tractor_move = pygame.USEREVENT + 1 pygame.time.set_timer(tractor_move, 800) moves = [] goal_astar = Astar() -destination = (random.randrange(0, 936, 36), random.randrange(0, 900, 36)) +mx=random.randrange(0, 936, 36) +my=random.randrange(0, 936, 36) +destination = (mx, my) print("Destination: ", destination) +mx=int((mx+18)/36) +my=int((my+18)/36) +print("Destination: ", mx,my) + +#ID3 TREE LOADING +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) + #ID3 decision + 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: + # decision = 1 + print('nie zbieramy') + +else: + print('Road, no plant growing') + + moves = goal_astar.search( [tractor.rect.x, tractor.rect.y, directions[tractor.rotation]], destination) @@ -67,6 +107,8 @@ if __name__ == "__main__": step = moves_list.pop() # pop the last element moves = tuple(moves_list) # convert back to tuple tractor.movement(step[0]) + if (tractor.rect.x, tractor.rect.y) == destination and decision == 1: + tractor.collect(plant_group) Tractor.movement_using_keys(tractor) diff --git a/mytree.png b/mytree.png new file mode 100644 index 0000000..7389bda Binary files /dev/null and b/mytree.png differ diff --git a/src/road.py b/output.txt similarity index 100% rename from src/road.py rename to output.txt diff --git a/settings.py b/settings.py index 916f954..edd78b3 100644 --- a/settings.py +++ b/settings.py @@ -1,10 +1,13 @@ from cmath import sqrt +# screen_width=1200 screen_width=936 +# screen_height=1000 screen_height=936 SIZE = (screen_width, screen_height) SPECIES=["carrot","potato","beetroot","wheat"] +WEATHER=['slonce','wiatr','snieg','deszcz'] # size in pixels of one tile = 36px/36px tile = (36, 36) block_size = 36 @@ -12,6 +15,6 @@ road_coords = [0, 5, 10, 15, 20, 25] field_width = 4 field_height = 4 field_size = field_width*field_height -fields_amount = 26 +fields_amount = 25 directions = {0: 'UP', 90: 'RIGHT', 180: 'DOWN', 270: 'LEFT'} \ No newline at end of file diff --git a/src/Field.py b/src/Field.py index fedc537..0967425 100644 --- a/src/Field.py +++ b/src/Field.py @@ -1,8 +1,9 @@ from pygame.sprite import Sprite +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): + fertilizer_degree, development_degree, plant_type, fertilizer_type, to_water, contain): super().__init__() self.type = type self.x = x @@ -18,4 +19,12 @@ class Field(Sprite): self.plant_type = plant_type self.fertilizer_type = fertilizer_type self.to_water = to_water + self.contain = contain + + def getContain(self): + return self.contain + + def setContain(self,newPlant): + self.contain=newPlant + diff --git a/src/ID3.ipynb b/src/ID3.ipynb new file mode 100644 index 0000000..f367de9 --- /dev/null +++ b/src/ID3.ipynb @@ -0,0 +1,90 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn import tree\n", + "import os\n", + "import pickle\n", + "import pydotplus\n", + "import matplotlib.image as pltimg\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# Read the CSV file\n", + "train = pd.read_csv(\"train_3.csv\", delimiter=\";\")\n", + "\n", + "x_train = train.drop('czy_zebrac',axis=1)\n", + "y_train = train['czy_zebrac']\n", + "\n", + "d_tree = DecisionTreeClassifier()\n", + "d_tree = d_tree.fit(x_train,y_train)\n", + "\n", + "pickle.dump(d_tree, open(os.path.join('.','tree.plk'),'wb'))\n", + "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'])\n", + "graph = pydotplus.graph_from_dot_data(data)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Save the graph as a PNG image in the script's folder\n", + "graph.write_png(os.path.join('.', 'mytree.png'))\n", + "\n", + "# Read the PNG image\n", + "img = pltimg.imread(os.path.join('.', 'mytree.png'))\n", + "\n", + "# Display the image\n", + "imgplot = plt.imshow(img)\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/ID3.py b/src/ID3.py new file mode 100644 index 0000000..17f9fc2 --- /dev/null +++ b/src/ID3.py @@ -0,0 +1,44 @@ +import pandas as pd +from sklearn.tree import DecisionTreeClassifier +from sklearn import tree +import os +import pickle +import pydotplus +import matplotlib.image as pltimg +import matplotlib.pyplot as plt + +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 + +def learnTree(): + # Read the CSV file + train = pd.read_csv("./train_3.csv") + + print(f'Shape: {train.shape}') + print(f'Head:\n{train.head()}') + + x_train = train.drop('czy_zebrac',axis=1) + y_train = train['czy_zebrac'] + + d_tree = DecisionTreeClassifier() + d_tree = d_tree.fit(x_train,y_train) + + pickle.dump(d_tree, open(os.path.join('.','tree.plk'),'wb')) #1-4 1 śnieg, 2 deszcz, 3 wiatr, 4 slonce + 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) + + # 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')) + 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(70,85,12,4,0,65,54,1500,dtree) +# print(decision) diff --git a/src/Plant.py b/src/Plant.py index 11e8cc4..3f237a9 100644 --- a/src/Plant.py +++ b/src/Plant.py @@ -2,11 +2,24 @@ import pygame from settings import block_size class Plant(pygame.sprite.Sprite): - def __init__(self,species,is_ill,pos_x,pos_y): + def __init__(self,wzrost, + wilgotnosc, + dni_od_nawiezienia, + aktualna_pogoda, + czy_robaczywa, + cena_sprzedarzy, + species, + pos_x, + pos_y): + super().__init__() self.species=species - self.is_ill=is_ill - + self.wzrost=wzrost + self.wilgotnosc=wilgotnosc + self.dni_od_nawiezienia=dni_od_nawiezienia + self.aktualna_pogoda=aktualna_pogoda + self.czy_robaczywa=czy_robaczywa + self.cena_sprzedarzy=cena_sprzedarzy if species=="carrot": self.growth_time=100 self.weight=50 @@ -47,4 +60,6 @@ class Plant(pygame.sprite.Sprite): self.image = pygame.transform.scale(self.image,(block_size, block_size)) self.rect = self.image.get_rect() self.rect.center = [pos_x,pos_y] - \ No newline at end of file + + def getParameters(self): + return [self.wzrost, self.wilgotnosc, self.dni_od_nawiezienia,self.aktualna_pogoda,self.czy_robaczywa,self.cena_sprzedarzy] \ No newline at end of file diff --git a/src/Road.py b/src/Road.py new file mode 100644 index 0000000..34eda2f --- /dev/null +++ b/src/Road.py @@ -0,0 +1,9 @@ +from pygame.sprite import Sprite + +class Road(Sprite): + def __init__(self,x,y): + super().__init__() + self.x=x + self.y=y + self.type='Road' + self.cost=200 \ No newline at end of file diff --git a/src/Tractor.py b/src/Tractor.py index 21ff6d9..7ebbae8 100644 --- a/src/Tractor.py +++ b/src/Tractor.py @@ -1,5 +1,12 @@ 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): @@ -19,11 +26,20 @@ 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() @@ -47,6 +63,7 @@ class Tractor(pygame.sprite.Sprite): self.rect.y += block_size if self.rect.x > 0 and self.rotation == 270: self.rect.x -= block_size + def move_left(self): self.rotation -= 90 @@ -69,6 +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)) if direction == 'F': self.move_forward() elif direction == 'L': @@ -100,4 +118,8 @@ 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] + \ No newline at end of file diff --git a/src/__pycache__/Field.cpython-310.pyc b/src/__pycache__/Field.cpython-310.pyc new file mode 100644 index 0000000..a0ccddf Binary files /dev/null and b/src/__pycache__/Field.cpython-310.pyc differ diff --git a/src/__pycache__/ID3.cpython-310.pyc b/src/__pycache__/ID3.cpython-310.pyc new file mode 100644 index 0000000..ac80307 Binary files /dev/null and b/src/__pycache__/ID3.cpython-310.pyc differ diff --git a/src/__pycache__/Plant.cpython-310.pyc b/src/__pycache__/Plant.cpython-310.pyc index 9fa6092..7c7a94e 100644 Binary files a/src/__pycache__/Plant.cpython-310.pyc and b/src/__pycache__/Plant.cpython-310.pyc differ diff --git a/src/__pycache__/Road.cpython-310.pyc b/src/__pycache__/Road.cpython-310.pyc new file mode 100644 index 0000000..705dffa Binary files /dev/null and b/src/__pycache__/Road.cpython-310.pyc differ diff --git a/src/__pycache__/Tractor.cpython-310.pyc b/src/__pycache__/Tractor.cpython-310.pyc index bb7eed4..77d9d9f 100644 Binary files a/src/__pycache__/Tractor.cpython-310.pyc and b/src/__pycache__/Tractor.cpython-310.pyc differ diff --git a/src/__pycache__/bfs.cpython-310.pyc b/src/__pycache__/bfs.cpython-310.pyc index 5e7f91c..4a30392 100644 Binary files a/src/__pycache__/bfs.cpython-310.pyc and b/src/__pycache__/bfs.cpython-310.pyc differ diff --git a/src/__pycache__/map.cpython-310.pyc b/src/__pycache__/map.cpython-310.pyc index c8e5bdd..f0731d5 100644 Binary files a/src/__pycache__/map.cpython-310.pyc and b/src/__pycache__/map.cpython-310.pyc differ diff --git a/src/map.py b/src/map.py index 1e479a5..6b7d5ed 100644 --- a/src/map.py +++ b/src/map.py @@ -4,6 +4,8 @@ from settings import screen_height, screen_width, SIZE, SPECIES, block_size, til from src.Plant import Plant import random from src.Field import Field +from src.Road import Road +import csv def get_type_by_position(fields, x, y): @@ -30,18 +32,28 @@ def get_cost_by_type(plant_type): fields = pygame.sprite.Group() + +WORLD_MATRIX = [[0 for _ in range(fields_amount+1)] for _ in range(fields_amount+1)] + def drawRoads(screen): #drawing roads: road = pygame.image.load("assets/road.jpeg") road = pygame.transform.scale(road, tile) for x in road_coords: - for block in range(26): + for block in range(int(fields_amount)+1): screen.blit(road, (x*block_size, block * 36)) - fields.add(Field('road', x*block_size, block * 36, None, get_cost_by_type('road'), None, None, None, None, 'road', None, 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]=tmp_field for y in road_coords: - for block in range(26): - screen.blit(road, (block * 36, y*block_size)) - fields.add(Field('road', block * 36, y*block_size, None, get_cost_by_type('road'), None, None, None, None, 'road', None, None)) + for block in range(int(fields_amount)+1): + screen.blit(road, (block * block_size, y*block_size)) + 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]=tmp_field + barn_img = pygame.image.load('assets/barn.png') barn = pygame.transform.scale(barn_img, tile) @@ -52,17 +64,54 @@ def drawRoads(screen): def seedForFirstTime(): plant_group = pygame.sprite.Group() for field in range(fields_amount): - plant = random.choice(SPECIES) + plant_name = random.choice(SPECIES) blocks_seeded_in_field = 0 while (blocks_seeded_in_field < field_size): + x = (((field%5)*((block_size*(field_width+1)))) + ((blocks_seeded_in_field % field_width)*block_size) + ((3/2)*block_size)) y = ((int(field/5)*((block_size*(field_width+1)))) + ((int(blocks_seeded_in_field/field_height))*block_size) + ((3/2)*block_size)) - new_plant = Plant(plant,0, x, y) + # wzrost;wilgotnosc;dni_od_nawiezienia;aktualna_pogoda;czy_roslina_robaczywa;typ_rosliny;pojemnosc_ekwipunku;cena_sprzedarzy;czy_zebrac + + new_plant = Plant( + wzrost=random.randint(0, 100), + wilgotnosc=random.randint(0, 100), + dni_od_nawiezienia=random.randint(0, 31), + aktualna_pogoda=random.randint(1,4), + czy_robaczywa=random.randint(0,1), + cena_sprzedarzy=random.randint(1000, 2000), + species=plant_name, + pos_x=x, + pos_y=y) + + # BOOSTED PLANTS + # new_plant = Plant( + # wzrost=random.randint(90, 100), + # wilgotnosc=random.randint(0, 50), + # dni_od_nawiezienia=random.randint(15, 31), + # aktualna_pogoda=random.randint(3,4), + # czy_robaczywa=0, + # cena_sprzedarzy=random.randint(1500, 2000), + # species=plant_name, + # pos_x=x, + # pos_y=y) blocks_seeded_in_field = blocks_seeded_in_field + 1 plant_group.add(new_plant) - fields.add(Field('field', x-18, y-18, None, get_cost_by_type(plant), None, None, None, None, plant, None, None)) + 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) + my = int((y-18)/36) + WORLD_MATRIX[mx][my]=tmp_field_plant + + #debug + # print(WORLD_MATRIX) + #end of debug + return plant_group +def put_to_matrix(): + return 0 + def return_fields_list(): return fields diff --git a/src/mytree.png b/src/mytree.png new file mode 100644 index 0000000..0b10e50 Binary files /dev/null and b/src/mytree.png differ diff --git a/src/train_3.csv b/src/train_3.csv new file mode 100644 index 0000000..e617ded --- /dev/null +++ b/src/train_3.csv @@ -0,0 +1,202 @@ +Wzrost,wilgotnosc,dni_od_nawiezienia,aktualna_pogoda,czy_roslina_robaczywa,paliwo,pojemnosc_ekwipunku,cena_sprzedarzy,czy_zebrac +23,78,12,2,1,78,88,1377,0 +39,31,15,4,1,82,37,1969,0 +12,41,21,2,1,62,28,994,0 +2,42,11,1,1,68,96,1291,0 +43,16,20,3,0,91,23,1653,0 +9,8,8,1,1,78,28,1709,0 +47,0,23,2,0,94,46,1702,1 +70,85,12,3,1,65,14,1758,0 +34,76,15,2,0,80,13,1461,1 +22,24,6,3,0,17,19,681,0 +79,9,17,4,0,94,75,1244,1 +56,30,0,3,1,43,55,542,0 +45,34,12,3,1,93,80,1967,0 +49,15,26,1,1,48,76,1095,0 +69,16,13,4,1,10,13,1558,0 +74,97,9,1,1,97,25,1940,0 +37,40,19,4,0,65,81,693,0 +83,26,3,2,0,91,62,1676,1 +52,37,0,3,0,93,4,1756,0 +67,68,30,4,0,34,3,626,1 +96,93,18,4,0,42,5,961,0 +90,22,11,3,1,59,69,824,1 +38,10,17,4,1,57,33,1479,0 +62,51,23,3,1,55,60,587,0 +64,44,5,2,1,92,85,1058,0 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