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drzewo decyzyjne

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s452730 2022-06-07 03:05:19 +02:00
parent 8599caa78b
commit 994f613eb9
4 changed files with 176 additions and 13 deletions
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data.xlsx Normal file
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9
main.py
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@ -4,6 +4,7 @@ from queue import PriorityQueue
from path_algorithms.a_star import a_star
# from path_algorithms.bfs import bfs
from rubbish import *
from tree import evaluate_values, trash_selection
from truck import Truck
from surface import *
@ -49,6 +50,7 @@ for i in range(15):
rubbish_list.append(Rubbish(screen, j * 60, i * 60))
path = []
run = 1
while True:
pygame.time.delay(500)
@ -58,6 +60,13 @@ while True:
i.draw_rubbish()
truck.draw_truck()
if run == 1:
# func(rubbish_list[0])
data = rubbish_list[0].data_for_decision_tree()
test = trash_selection(evaluate_values(data))
print(test)
run = 0
if rubbish_list and not path:
start = (truck.y / 60, truck.x / 60)
direction = truck.direction

29
rubbish.py
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@ -13,49 +13,52 @@ class Rubbish:
self.image = pygame.image.load('images/garbage.png')
self.surface_rect = self.image.get_rect()
self.surface_rect.center = (self.x + 30, self.y + 30)
self.weight = random.randint(0, 10)
self.density = random.randint(0, 10)
self.fragility = random.randint(0, 10)
self.dirty = random.randint(0, 10)
self.size = random.randint(0, 10)
self.degradability = random.randint(0, 10)
self.renewability = random.randint(0, 10)
self.weight = random.choice([10, 15, 20, 30, 40])
self.density = random.choice([True, False])
self.fragility = random.randint(1, 10)
self.material = random.choice(["plastic", "wood", "metal", "glass", "paper"])
self.size = random.choice(["little", "medium", "huge", "large"])
self.degradability = random.randint(1, 10)
self.renewability = random.randint(1, 10)
def draw_rubbish(self):
self.screen.blit(self.image, self.surface_rect)
def data_for_decision_tree(self):
return [self.weight, self.density, self.fragility, self.material, self.size, self.degradability, self.renewability]
class Paper_waste(Rubbish):
class PaperWaste(Rubbish):
def __init__(self, screen, x, y):
super().__init__(screen, x, y)
class Organic_waste(Rubbish):
class OrganicWaste(Rubbish):
def __init__(self, screen, x, y):
super().__init__(screen, x, y)
class Glass_waste(Rubbish):
class GlassWaste(Rubbish):
def __init__(self, screen, x, y):
super().__init__(screen, x, y)
class Plastic_waste(Rubbish):
class PlasticWaste(Rubbish):
def __init__(self, screen, x, y):
super().__init__(screen, x, y)
class E_waste(Rubbish):
class EWaste(Rubbish):
def __init__(self, screen, x, y):
super().__init__(screen, x, y)
class Metal_waste(Rubbish):
class MetalWaste(Rubbish):
def __init__(self, screen, x, y):
super().__init__(screen, x, y)

151
tree.py Normal file
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@ -0,0 +1,151 @@
import random
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
def evaluate_values(values):
data = []
if values[0] == 10:
data.append(10)
elif values[0] == 15:
data.append(15)
elif values[0] == 20:
data.append(20)
elif values[0] == 30:
data.append(30)
elif values[0] == 40:
data.append(40)
else:
data.append(random.choice([10, 15, 20, 30, 40]))
if values[1] is True:
data.append(1)
elif values[1] is False:
data.append(0)
else:
data.append(random.choice([1, 0]))
if values[2] == 1:
data.append(1)
elif values[2] == 2:
data.append(2)
elif values[2] == 3:
data.append(3)
elif values[2] == 4:
data.append(4)
elif values[2] == 5:
data.append(5)
elif values[2] == 6:
data.append(6)
elif values[2] == 7:
data.append(7)
elif values[2] == 8:
data.append(8)
elif values[2] == 9:
data.append(9)
elif values[2] == 10:
data.append(10)
else:
data.append(random.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]))
if values[3] == 'plastic':
data.append(5)
elif values[3] == 'wood':
data.append(2)
elif values[3] == 'metal':
data.append(4)
elif values[3] == 'glass':
data.append(3)
elif values[3] == 'paper':
data.append(1)
else:
data.append(random.choice([1, 2, 3, 4, 5]))
if values[4] == 'little':
data.append(1)
elif values[4] == 'medium':
data.append(2)
elif values[4] == 'huge':
data.append(3)
elif values[4] == 'large':
data.append(4)
else:
data.append(random.choice([1, 2, 3, 4]))
if values[5] == 1:
data.append(1)
elif values[5] == 2:
data.append(2)
elif values[5] == 3:
data.append(3)
elif values[5] == 4:
data.append(4)
elif values[5] == 5:
data.append(5)
elif values[5] == 6:
data.append(6)
elif values[5] == 7:
data.append(7)
elif values[5] == 8:
data.append(8)
elif values[5] == 9:
data.append(9)
elif values[5] == 10:
data.append(10)
else:
data.append(random.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]))
if values[6] == 1:
data.append(1)
elif values[6] == 2:
data.append(2)
elif values[6] == 3:
data.append(3)
elif values[6] == 4:
data.append(4)
elif values[6] == 5:
data.append(5)
elif values[6] == 6:
data.append(6)
elif values[6] == 7:
data.append(7)
elif values[6] == 8:
data.append(8)
elif values[6] == 9:
data.append(9)
elif values[6] == 10:
data.append(10)
else:
data.append(random.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]))
return data
def trash_selection(prefer):
df = pd.read_excel('data.xlsx', sheet_name='list1')
# print(df)
d = {'paper': 1, 'wood': 2, 'glass': 3, 'metal': 4, 'plastic': 5}
df['material'] = df['material'].map(d)
d = {'little': 1, 'medium': 2, 'huge': 3, 'large': 4}
df['size'] = df['size'].map(d)
d = {'leave': 0, 'pick up': 1}
df['what to do'] = df['what to do'].map(d)
features = ['weight', 'density', 'fragility', 'material', 'size', 'degradability', 'renewability']
x = df[features]
y = df['what to do']
x_train, x_test, y_train, y_test = train_test_split(x, y)
clf = DecisionTreeClassifier(criterion='entropy')
# model = clf.fit(X, y)
# text_representation = tree.export_text(clf)
# print(text_representation)
clf = clf.fit(x_train, y_train)
return clf.predict([prefer])