Add decision tree #20

Merged
s473593 merged 1 commits from decision_tree into main 2023-05-12 22:23:28 +02:00
12 changed files with 86 additions and 5 deletions

22
decisionTree/data.csv Normal file
View File

@ -0,0 +1,22 @@
1-2-3-4-5;1-green 2-yellow 3-orange 4-black 5-while 6-blue;in dB 0-100;0-24;0/1;in cm;in C;0/1
Size;Color;Sound;Time;Smell;Height;Temperature;ToRemove
1;2;0;16;1;10;25;1
2;1;0;12;0;50;24;0
2;3;30;13;1;38;38;0
1;4;0;7;1;5;27;1
1;2;0;16;1;10;25;1
2;1;0;12;0;50;24;0
2;3;30;13;1;38;38;0
1;4;0;7;1;5;27;1
1;2;0;16;1;10;25;1
2;1;0;12;0;50;24;0
2;3;30;13;1;38;38;0
1;4;0;7;1;5;27;1
1;2;0;16;1;10;25;1
2;1;0;12;0;50;24;0
2;3;30;13;1;38;38;0
1;4;0;7;1;5;27;1
1;2;0;16;1;10;25;1
2;1;0;12;0;50;24;0
2;3;30;13;1;38;38;0
1;4;0;7;1;5;27;1
1 1-2-3-4-5 1-green 2-yellow 3-orange 4-black 5-while 6-blue in dB 0-100 0-24 0/1 in cm in C 0/1
2 Size Color Sound Time Smell Height Temperature ToRemove
3 1 2 0 16 1 10 25 1
4 2 1 0 12 0 50 24 0
5 2 3 30 13 1 38 38 0
6 1 4 0 7 1 5 27 1
7 1 2 0 16 1 10 25 1
8 2 1 0 12 0 50 24 0
9 2 3 30 13 1 38 38 0
10 1 4 0 7 1 5 27 1
11 1 2 0 16 1 10 25 1
12 2 1 0 12 0 50 24 0
13 2 3 30 13 1 38 38 0
14 1 4 0 7 1 5 27 1
15 1 2 0 16 1 10 25 1
16 2 1 0 12 0 50 24 0
17 2 3 30 13 1 38 38 0
18 1 4 0 7 1 5 27 1
19 1 2 0 16 1 10 25 1
20 2 1 0 12 0 50 24 0
21 2 3 30 13 1 38 38 0
22 1 4 0 7 1 5 27 1

Binary file not shown.

9
decisionTree/evaluate.py Normal file
View File

@ -0,0 +1,9 @@
import joblib
def evaluate(data):
# Load the model
clf = joblib.load('decisionTree/decision_tree_model.pkl')
# Make a prediction
prediction = clf.predict(data)
return prediction

21
decisionTree/prepare.py Normal file
View File

@ -0,0 +1,21 @@
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn import metrics
import joblib
pima = pd.read_csv("data.csv", header=1, delimiter=';')
feature_cols = ['Size', 'Color', 'Sound', 'Time','Smell', 'Height','Temperature']
X = pima[feature_cols]
y = pima.ToRemove
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
clf = DecisionTreeClassifier()
clf = clf.fit(X_train,y_train)
joblib.dump(clf, 'decision_tree_model.pkl')
y_pred = clf.predict(X_test)
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

View File

@ -13,3 +13,5 @@ class Cat(Entity):
self.busy = False
self.sleeping = False
self.direction = 0
self.props = [1,2,0,16,1,10,25]

View File

@ -0,0 +1,8 @@
from domain.entities.entity import Entity
from domain.world import World
class Earring(Entity):
def __init__(self, x: int, y: int):
super().__init__(x, y, "EARRING")
self.props = [2,1,0,12,0,50,24]

View File

@ -1,11 +1,11 @@
from domain.entities.entity import Entity
from domain.world import World
class Garbage(Entity):
def __init__(self, x: int, y: int):
super().__init__(x, y, "GARBAGE")
super().__init__(x, y, "PEEL")
self.wet = False
self.size = 0
self.props = [1,2,0,16,1,10,25]
# TODO GARBAGE: add more properties

View File

@ -1,3 +1,4 @@
from decisionTree.evaluate import evaluate
from domain.entities.entity import Entity
@ -15,6 +16,8 @@ class World:
def add_entity(self, entity: Entity):
if entity.type == "PEEL":
self.dust[entity.x][entity.y].append(entity)
elif entity.type == "EARRING":
self.dust[entity.x][entity.y].append(entity)
elif entity.type == "VACUUM":
self.vacuum = entity
elif entity.type == "DOC_STATION":
@ -29,7 +32,10 @@ class World:
return bool(self.obstacles[x][y])
def is_garbage_at(self, x: int, y: int) -> bool:
return bool(self.dust[x][y])
if len(self.dust[x][y]) == 0:
return False
tmp = evaluate([self.dust[x][y][0].props])
return bool(tmp[0])
def is_docking_station_at(self, x: int, y: int) -> bool:
return bool(self.doc_station.x == x and self.doc_station.y == y)

View File

@ -8,6 +8,8 @@ from domain.commands.vacuum_move_command import VacuumMoveCommand
from domain.entities.cat import Cat
from domain.entities.entity import Entity
from domain.entities.vacuum import Vacuum
from domain.entities.garbage import Garbage
from domain.entities.earring import Earring
from domain.entities.docking_station import Doc_Station
from domain.world import World
from view.renderer import Renderer
@ -135,7 +137,7 @@ def generate_world(tiles_x: int, tiles_y: int) -> World:
for _ in range(10):
temp_x = randint(0, tiles_x - 1)
temp_y = randint(0, tiles_y - 1)
world.add_entity(Entity(temp_x, temp_y, "PEEL"))
world.add_entity(Garbage(temp_x, temp_y))
world.vacuum = Vacuum(1, 1)
world.doc_station = Doc_Station(9, 8)
if config.getboolean("APP", "cat"):
@ -146,6 +148,7 @@ def generate_world(tiles_x: int, tiles_y: int) -> World:
world.add_entity(Entity(3, 4, "PLANT2"))
world.add_entity(Entity(8, 8, "PLANT2"))
world.add_entity(Entity(9, 3, "PLANT3"))
world.add_entity(Earring(5, 5))
return world

BIN
media/sprites/earrings.webp Normal file

Binary file not shown.

After

Width:  |  Height:  |  Size: 5.3 KiB

View File

@ -1,3 +1,6 @@
pygame
configparser
formaFormatting: Provider - black
pandas
scikit-learn
joblib
# formaFormatting: Provider - black

View File

@ -94,6 +94,13 @@ class Renderer:
self.tile_height + self.tile_height / 4,
),
),
"EARRING": pygame.transform.scale(
pygame.image.load("media/sprites/earrings.webp"),
(
self.tile_width + self.tile_width / 4,
self.tile_height + self.tile_height / 4,
),
),
}
self.cat_direction_sprite = {