Compare commits
11 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| a7152dff3c | |||
| d9a5f26c02 | |||
| f3ddde84e3 | |||
| 2a1724a4b7 | |||
| 71dc3e81a2 | |||
| a73862b48b | |||
| 7958ec4a7e | |||
| cf203978c6 | |||
| 21b2f6328b | |||
| 74e6baecfa | |||
| 08def183f7 |
354
AI_projecty.py
354
AI_projecty.py
@ -1,354 +0,0 @@
|
||||
import pandas as pd
|
||||
import pygame
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.preprocessing import LabelEncoder
|
||||
from sklearn.tree import DecisionTreeClassifier
|
||||
from sklearn.metrics import accuracy_score
|
||||
from sklearn.metrics import confusion_matrix
|
||||
from pygame.locals import *
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from sklearn import tree
|
||||
import random
|
||||
from textpygame import get_key, ask, display_box
|
||||
|
||||
|
||||
|
||||
#read csv file
|
||||
dataset= pd.read_csv("veganism.csv")
|
||||
|
||||
#show shape of dataset
|
||||
print(dataset.shape)
|
||||
|
||||
#create a new dataset
|
||||
newdataset = pd.DataFrame(dataset, columns=['ethnicity', 'gender', 'appearence', 'vegan'])
|
||||
|
||||
# creating instance of labelencoder
|
||||
labelencoder = LabelEncoder()
|
||||
# Assigning numerical values and storing in another column
|
||||
newdataset['ethnicity_no'] = labelencoder.fit_transform(newdataset['ethnicity'])
|
||||
newdataset['gender_no']= labelencoder.fit_transform(newdataset['gender'])
|
||||
newdataset['appearence_no']= labelencoder.fit_transform(newdataset['appearence'])
|
||||
print(newdataset)
|
||||
|
||||
# for x values drop unimportant columns, axis=1 specifies that we want the columns not rows
|
||||
Y=newdataset['vegan']
|
||||
X=newdataset.drop(newdataset.columns[0:4], axis=1)
|
||||
print(X,Y)
|
||||
|
||||
#test 14% of the data
|
||||
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.15)
|
||||
classifier = DecisionTreeClassifier()
|
||||
classifier.fit(X_train, Y_train)
|
||||
y_pred = classifier.predict(X_test)
|
||||
|
||||
#ignores minority groups which results in 0 for prediction. to solve this i have to use smthing else
|
||||
print(pd.DataFrame(confusion_matrix(Y_test, y_pred),columns=['Predicted not vegan', 'Predicted vegan'], index=['Actually not vegan', 'Actually vegan']))
|
||||
|
||||
#accuracy score is high as expected
|
||||
print(accuracy_score(Y_test, y_pred))
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
fn=['ethnicity','gender','appearence']
|
||||
cn=['yes', 'no']
|
||||
|
||||
# Setting dpi = 300 to make image clearer than default
|
||||
fig, axes = plt.subplots(nrows = 1,ncols = 1,figsize = (4,4), dpi=300)
|
||||
|
||||
tree.plot_tree(classifier,
|
||||
feature_names = fn,
|
||||
class_names=cn,
|
||||
filled = True);
|
||||
|
||||
fig.savefig('imagenamenew.png')
|
||||
|
||||
|
||||
|
||||
#using the my_grid3 program
|
||||
|
||||
# Colors:
|
||||
# Define some colors
|
||||
BLACK = (0, 0, 0)
|
||||
WHITE = (255, 255, 255)
|
||||
GREEN = (0, 255, 0)
|
||||
RED = (255, 0, 0)
|
||||
BLUE = (0, 0, 240)
|
||||
MAGENTA=(255, 0, 255)
|
||||
# Width and Height of each square:
|
||||
WIDTH = 20
|
||||
HEIGHT = 20
|
||||
|
||||
# Margin:
|
||||
MARGIN = 5
|
||||
grid = [[0 for x in range(16)] for y in range(16)]
|
||||
|
||||
|
||||
def change_value(i, j, width, n):
|
||||
for r in range(i, i + width):
|
||||
for c in range(j, j + width):
|
||||
grid[r][c] = n
|
||||
if grid[r][c]==5:
|
||||
break
|
||||
|
||||
|
||||
class Table:
|
||||
def __init__(self, coordinate_i, coordinate_j):
|
||||
self.coordinate_i = coordinate_i
|
||||
self.coordinate_j = coordinate_j
|
||||
change_value(coordinate_i, coordinate_j, 2, 1)
|
||||
|
||||
|
||||
|
||||
|
||||
class Kitchen:
|
||||
def __init__(self, coordinate_i, coordinate_j):
|
||||
self.coordinate_i = coordinate_i
|
||||
self.coordinate_j = coordinate_j
|
||||
change_value(coordinate_i, coordinate_j, 3, 2)
|
||||
|
||||
|
||||
class Agent:
|
||||
def __init__(self, orig_coordinate_i, orig_coordinate_j):
|
||||
self.orig_coordinate_i = orig_coordinate_i
|
||||
self.orig_coordinate_j = orig_coordinate_j
|
||||
self.state = np.array([1, 2])
|
||||
change_value(orig_coordinate_j, orig_coordinate_j, 1, 3)
|
||||
self.state_update(orig_coordinate_i, orig_coordinate_j)
|
||||
|
||||
def state_update(self, c1, c2):
|
||||
self.state[0] = c1
|
||||
self.state[1] = c2
|
||||
print(self.state)
|
||||
|
||||
def leave(self):
|
||||
change_value(self.state[0], self.state[1], 1, 0)
|
||||
|
||||
def move_up(self):
|
||||
self.leave()
|
||||
self.state_update(x - 1, y)
|
||||
change_value(self.state[0], self.state[1], 1, 3)
|
||||
|
||||
def move_down(self):
|
||||
self.leave()
|
||||
change_value(self.state[0], self.state[1], 1, 0)
|
||||
self.state_update(x + 1, y)
|
||||
change_value(self.state[0], self.state[1], 1, 3)
|
||||
|
||||
def move_right(self):
|
||||
self.leave()
|
||||
self.state_update(x, y + 1)
|
||||
change_value(self.state[0], self.state[1], 1, 3)
|
||||
|
||||
def move_left(self):
|
||||
self.leave()
|
||||
self.state_update(x, y - 1)
|
||||
change_value(self.state[0], self.state[1], 1, 3)
|
||||
class Customer:
|
||||
def __init__(self, coordinate_i, coordinate_j):
|
||||
self.coordinate_i = coordinate_i
|
||||
self.coordinate_j = coordinate_j
|
||||
change_value(coordinate_i, coordinate_j,1 , 4)
|
||||
class CustomerPlace:
|
||||
def __init__(self, coordinate_i, coordinate_j):
|
||||
self.coordinate_i = coordinate_i
|
||||
self.coordinate_j = coordinate_j
|
||||
change_value(coordinate_i, coordinate_j,1 , 5)
|
||||
|
||||
|
||||
## default positions of the agent:
|
||||
x = 11
|
||||
y = 11
|
||||
agent = Agent(x, y)
|
||||
|
||||
table1 = Table(2, 2)
|
||||
table2 = Table(2, 7)
|
||||
table3 = Table(2, 12)
|
||||
table4 = Table(7, 2)
|
||||
table5 = Table(7, 7)
|
||||
table6 = Table(7, 12)
|
||||
table7 = Table(12, 2)
|
||||
table8 = Table(12, 7)
|
||||
|
||||
|
||||
pygame.init()
|
||||
# create a font object.
|
||||
# 1st parameter is the font file
|
||||
# which is present in pygame.
|
||||
# 2nd parameter is size of the font
|
||||
font = pygame.font.Font('freesansbold.ttf', 14)
|
||||
X = 400
|
||||
Y = 400
|
||||
# create a text suface object,
|
||||
# on which text is drawn on it.
|
||||
text = font.render('waiter: hello, let me help you with your order.', True, WHITE, BLACK)
|
||||
userText=font.render('user: ', True, BLUE, BLACK)
|
||||
# create a rectangular object for the
|
||||
# text surface object
|
||||
textRect = text.get_rect()
|
||||
inputRect = userText.get_rect()
|
||||
# set the center of the rectangular object.
|
||||
textRect.center= (200, 340)
|
||||
inputRect.center=(200,370)
|
||||
# class Kitchen:
|
||||
kitchen = Kitchen(13, 13)
|
||||
|
||||
x=[2,7,12]
|
||||
y=[2,7]
|
||||
|
||||
random_customer_seat_x=random.choice(x)
|
||||
random_customer_seat_y=random.choice(y)
|
||||
print(random_customer_seat_x,random_customer_seat_y)
|
||||
seat=Customer(random_customer_seat_x,random_customer_seat_y)
|
||||
|
||||
next_to=CustomerPlace(random_customer_seat_x,random_customer_seat_y-1)
|
||||
|
||||
WINDOW_SIZE = [405, 405]
|
||||
screen = pygame.display.set_mode(WINDOW_SIZE)
|
||||
|
||||
pygame.display.set_caption("Waiter_Grid3")
|
||||
|
||||
done = False
|
||||
print(random_customer_seat_x,random_customer_seat_y-1)
|
||||
clock = pygame.time.Clock()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
#updating the drawing
|
||||
def updateDraw():
|
||||
x = agent.state[0]
|
||||
y = agent.state[1]
|
||||
screen.fill(BLACK) # Background color
|
||||
for row in range(16): # Drawing the grid
|
||||
for column in range(16):
|
||||
color = WHITE
|
||||
if grid[row][column] == 1:
|
||||
color = GREEN
|
||||
if grid[row][column] == 2:
|
||||
color = RED
|
||||
if grid[row][column] == 3:
|
||||
color = BLUE
|
||||
if grid[row][column] == 4:
|
||||
color = MAGENTA
|
||||
surface = pygame.draw.rect(screen,
|
||||
color,
|
||||
|
||||
[(MARGIN + WIDTH) * column + MARGIN,
|
||||
(MARGIN + HEIGHT) * row + MARGIN,
|
||||
WIDTH,
|
||||
HEIGHT])
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def customer():
|
||||
if x == random_customer_seat_x and y == random_customer_seat_y - 1:
|
||||
screen.blit(text, textRect)
|
||||
ethnicity3= ask(screen, question="ethnicity ")
|
||||
gender3= ask(screen,question="gender ")
|
||||
appearence3=ask(screen, question="appearence ")
|
||||
if ethnicity3 == "black":
|
||||
ethnicity3 = 1
|
||||
elif ethnicity3 == "asian":
|
||||
ethnicity3 = 0
|
||||
else:
|
||||
ethnicity3 = 2
|
||||
|
||||
if gender3 == "male":
|
||||
gender3 = 0
|
||||
else:
|
||||
gender3 = 1
|
||||
|
||||
if appearence3 == "hippie":
|
||||
appearence3= 0
|
||||
else:
|
||||
appearence3 = 1
|
||||
prediction = classifier.predict([[ethnicity3, gender3, appearence3]])
|
||||
pygame.quit()
|
||||
|
||||
if prediction == [0]:
|
||||
print("You're probably not vegan. Would you like a regular menu?")
|
||||
else:
|
||||
print("It seems like youre vegan. Would you like a vegan menu?")
|
||||
exit()
|
||||
|
||||
|
||||
|
||||
running=True
|
||||
|
||||
# pick a font you have and set its size
|
||||
text2 = 'this text is editable'
|
||||
font2 = pygame.font.SysFont(None, 48)
|
||||
img2 = font.render(text2, True, RED)
|
||||
|
||||
rect2 = img2.get_rect()
|
||||
rect2.topleft = (20, 20)
|
||||
|
||||
|
||||
background = BLACK
|
||||
# put the label object on the screen at point x=100, y=100
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
cursor = Rect(rect2.topright, (3, rect2.height))
|
||||
while running:
|
||||
|
||||
x = agent.state[0]
|
||||
y = agent.state[1]
|
||||
pygame.time.delay(100)
|
||||
for event in pygame.event.get(): # Checking for the event
|
||||
if event.type == pygame.QUIT: # If the program is closed:
|
||||
done = True # To exit the loop
|
||||
|
||||
keys = pygame.key.get_pressed() # Moving the agent:
|
||||
|
||||
if keys[pygame.K_LEFT]: # Left:
|
||||
# Checking if not a table, a kitchen, or a wall:
|
||||
if y - 1 < 0 or grid[x][y - 1] == 1:
|
||||
continue
|
||||
else:
|
||||
agent.move_left() # If okay, the move
|
||||
if keys[pygame.K_RIGHT]: # The same procedure with right:
|
||||
if y + 1 > 15 or grid[x][y + 1] == 1 or grid[x][y + 1] == 2:
|
||||
continue
|
||||
else:
|
||||
agent.move_right()
|
||||
if keys[pygame.K_UP]: # The same procedure with up:
|
||||
if x - 1 < 0 or grid[x - 1][y] == 1 or grid[x - 1][y] == 2:
|
||||
continue
|
||||
|
||||
else:
|
||||
agent.move_up()
|
||||
if keys[pygame.K_DOWN]: # The same procedure with down
|
||||
if x + 1 > 15 or grid[x + 1][y] == 1 or grid[x + 1][y] == 2:
|
||||
continue
|
||||
else:
|
||||
agent.move_down()
|
||||
|
||||
updateDraw()
|
||||
customer()
|
||||
clock.tick(60) # Limit to 60 frames per second
|
||||
pygame.display.flip() # Updating the screen
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,29 +0,0 @@
|
||||
# Final Evaluation - Waiter Project
|
||||
Authors: Tao Sen Chang, Martyna Druminska, Weronika Skowronska.
|
||||
|
||||
The project aim is to simulate waiter's behaviour in a restaurant
|
||||
# Features
|
||||
|
||||
The waiter is able to move from table to table, choosing the most optimal way; It can decide whether the customer wants vegan menu or not, and evaluate the customer's plate as empty (waiting to order) full (eating) or dirty (and waiting for receipt)
|
||||
|
||||
# Route choosing
|
||||
Author: Tao Sen Chang
|
||||
|
||||
At beginning of our project, we use reinforcement learning to get the shortest route for our agent to traversal specific tables. Reinforcement learning is how software agent ought to take actions in an environment in order to maximize the notion of cumulative reward. The agent makes a sequence of decisions, and learns to perform the best actions every step. After tons of training loops, the agent could get the optimal strategy which might collect maximal reward. In the case of our program, the “maximal reward”, would be to find the minimum distance to the customer, which in turn would save time and effort.
|
||||
|
||||
# Plate evaluation
|
||||
Author: Weronika Skowronska
|
||||
|
||||
For this part, we used CNNs to classify the plate on the table as empty, full or dirty. CNN is a kind of Neural Networks, used to work with pictures - it's good in extracting features from a photo, and therefore it demands less computations than a fully connected layer used for the same task.
|
||||
As it is often hard to say, if the client has done eating - if the waiter is not sure, he asks.
|
||||
The state of the table is then passed to the table object, and is available for further actions.
|
||||
|
||||
# Client evaluation
|
||||
Author: Martyna Druminska
|
||||
|
||||
If the plate is classified as empty - which means that the client hasn't ordered yet - we want to propose him a menu. We used decision trees to make a prediction on whether the person would like a vegan menu or not, based on the “customers” appearance (answers in the console). The decision tree algorithm learns off of a dataset with a diverse set of variables to make the best prediction. The program generates a flow-like structure that represents the decisions, where each node is a “test” on an attribute. The decision tree finishes when there are only leaf nodes left.
|
||||
|
||||
# Conclusions
|
||||
|
||||
We tried to perform an action as similar as possible to a real-life situation. In our opinion, we did pretty well - we managed to create a program that runs efficiently and cohesively to satisfy the needs of the customer.
|
||||
One remark is that our program has little to do about clients' orders and the paying process - we think that one more subproject could solve this problem.
|
||||
@ -1,67 +0,0 @@
|
||||
import pygame
|
||||
import pygame.font
|
||||
import pygame.event
|
||||
import pygame.draw
|
||||
import os
|
||||
import sys
|
||||
from pygame.locals import *
|
||||
|
||||
|
||||
bad_words_file = os.path.os.path.dirname(os.path.realpath(sys.argv[0])) \
|
||||
+ '/bad_words.txt'
|
||||
|
||||
def get_key():
|
||||
while 1:
|
||||
event = pygame.event.poll()
|
||||
if event.type == KEYDOWN:
|
||||
return event.key
|
||||
else:
|
||||
pass
|
||||
|
||||
|
||||
def display_box(screen, message):
|
||||
"Print a message in a box in the middle of the screen"
|
||||
fontobject = pygame.font.Font(None, 18)
|
||||
pygame.draw.rect(screen, (0, 0, 0),
|
||||
((screen.get_width() / 2) - 100,
|
||||
(screen.get_height() / 2) - 10,
|
||||
200, 20), 0)
|
||||
pygame.draw.rect(screen, (255, 255, 255),
|
||||
((screen.get_width() / 2) - 102,
|
||||
(screen.get_height() / 2) - 12,
|
||||
204, 24), 1)
|
||||
if len(message) != 0:
|
||||
screen.blit(fontobject.render(message, 1, (255, 255, 255)),
|
||||
((screen.get_width() / 2) - 100, (screen.get_height() / 2) - 10))
|
||||
pygame.display.flip()
|
||||
|
||||
|
||||
def ask(screen, question):
|
||||
"ask(screen, question) -> answer"
|
||||
pygame.font.init()
|
||||
current_string = []
|
||||
display_box(screen, question + ": " + "".join(current_string))
|
||||
while 1:
|
||||
inkey = get_key()
|
||||
if inkey == K_BACKSPACE:
|
||||
current_string = current_string[0:-1]
|
||||
elif inkey == K_RETURN:
|
||||
file = open(bad_words_file, 'r').readlines()
|
||||
if "".join(current_string) in [thing[:-1] for thing in file]:
|
||||
current_string = []
|
||||
else:
|
||||
break
|
||||
elif inkey == K_MINUS:
|
||||
current_string.append("_")
|
||||
elif inkey <= 127:
|
||||
current_string.append(chr(inkey))
|
||||
display_box(screen, question + ": " + "".join(current_string))
|
||||
return "".join(current_string)
|
||||
|
||||
|
||||
def main():
|
||||
screen = pygame.display.set_mode((320, 240))
|
||||
print(ask(screen, "Name") + " was entered")
|
||||
|
||||
|
||||
if __name__ == '__main__': main()
|
||||
751
veganism.csv
751
veganism.csv
@ -1,751 +0,0 @@
|
||||
ethnicity,gender,appearence,vegan
|
||||
asian,male,hippie,0
|
||||
asian,male,hippie,1
|
||||
asian,male,hippie,1
|
||||
asian,male,hippie,0
|
||||
asian,male,hippie,1
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,1
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,male,regular,0
|
||||
asian,female,hippie,1
|
||||
asian,female,hippie,1
|
||||
asian,female,hippie,1
|
||||
asian,female,hippie,1
|
||||
asian,female,hippie,1
|
||||
asian,female,hippie,1
|
||||
asian,female,hippie,1
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
asian,female,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,1
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,male,regular,0
|
||||
black,female,hippie,1
|
||||
black,female,hippie,1
|
||||
black,female,hippie,1
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,1
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
black,female,regular,0
|
||||
white,male,hippie,1
|
||||
white,male,hippie,1
|
||||
white,male,hippie,1
|
||||
white,male,hippie,1
|
||||
white,male,hippie,1
|
||||
white,male,hippie,1
|
||||
white,male,hippie,1
|
||||
white,male,hippie,1
|
||||
white,male,hippie,0
|
||||
white,male,hippie,1
|
||||
white,male,hippie,1
|
||||
white,male,hippie,1
|
||||
white,male,hippie,0
|
||||
white,male,hippie,1
|
||||
white,male,hippie,1
|
||||
white,male,hippie,1
|
||||
white,male,hippie,1
|
||||
white,male,hippie,1
|
||||
white,male,hippie,1
|
||||
white,male,hippie,1
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,male,regular,0
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,0
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,0
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,0
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,0
|
||||
white,female,hippie,0
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,0
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,0
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,0
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,0
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,0
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,0
|
||||
white,female,hippie,1
|
||||
white,female,hippie,0
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,hippie,1
|
||||
white,female,regular,0
|
||||
white,female,regular,1
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,1
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
white,female,regular,0
|
||||
|
478
waiter1406.py
478
waiter1406.py
@ -1,478 +0,0 @@
|
||||
#### MD #######
|
||||
|
||||
import pandas as pd
|
||||
import pygame
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.preprocessing import LabelEncoder
|
||||
from sklearn.tree import DecisionTreeClassifier
|
||||
from sklearn.metrics import accuracy_score
|
||||
from sklearn.metrics import confusion_matrix
|
||||
from pygame.locals import *
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from sklearn import tree
|
||||
import random
|
||||
from textpygame import get_key, ask, display_box
|
||||
###### /MD ######
|
||||
|
||||
|
||||
import math
|
||||
import pickle
|
||||
|
||||
|
||||
########################
|
||||
### WS ###
|
||||
########################
|
||||
# For CNN:
|
||||
|
||||
import keras
|
||||
from keras.preprocessing import image
|
||||
from keras.models import Sequential
|
||||
from keras.layers import Convolution2D
|
||||
from keras.layers import MaxPooling2D
|
||||
from keras.layers import Flatten
|
||||
from keras.layers import Dense
|
||||
|
||||
|
||||
#initializing:
|
||||
cnn_model = Sequential()
|
||||
#Convolution:
|
||||
cnn_model.add(Convolution2D(32, (3, 3), input_shape =(256, 256, 3), activation = "relu"))
|
||||
#Pooling:
|
||||
cnn_model.add(MaxPooling2D(pool_size = (2,2)))
|
||||
|
||||
# Adding a second convolutional layer
|
||||
cnn_model.add(Convolution2D(32, 3, 3, activation = 'relu'))
|
||||
cnn_model.add(MaxPooling2D(pool_size = (2, 2)))
|
||||
|
||||
#Flattening:
|
||||
cnn_model.add(Flatten())
|
||||
|
||||
#Fully connected layers::
|
||||
cnn_model.add(Dense(units = 128, activation = "relu"))
|
||||
cnn_model.add(Dense(units = 3, activation = "softmax"))
|
||||
|
||||
# loading weigjts:
|
||||
cnn_model.load_weights('s444523/best_model_weights2.h5')
|
||||
#Making CNN:
|
||||
cnn_model.compile(optimizer = "adam", loss = "categorical_crossentropy", metrics = ["accuracy"])
|
||||
|
||||
########################
|
||||
### /WS ###
|
||||
########################
|
||||
|
||||
|
||||
|
||||
#### MD#####
|
||||
|
||||
#read csv file
|
||||
dataset= pd.read_csv("veganism.csv")
|
||||
|
||||
|
||||
#create a new dataset
|
||||
newdataset = pd.DataFrame(dataset, columns=['ethnicity', 'gender', 'appearence', 'vegan'])
|
||||
|
||||
# creating instance of labelencoder
|
||||
labelencoder = LabelEncoder()
|
||||
# Assigning numerical values and storing in another column
|
||||
newdataset['ethnicity_no'] = labelencoder.fit_transform(newdataset['ethnicity'])
|
||||
newdataset['gender_no']= labelencoder.fit_transform(newdataset['gender'])
|
||||
newdataset['appearence_no']= labelencoder.fit_transform(newdataset['appearence'])
|
||||
|
||||
|
||||
# for x values drop unimportant columns, axis=1 specifies that we want the columns not rows
|
||||
Y=newdataset['vegan']
|
||||
X=newdataset.drop(newdataset.columns[0:4], axis=1)
|
||||
|
||||
|
||||
#test 14% of the data
|
||||
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.15)
|
||||
classifier = DecisionTreeClassifier()
|
||||
classifier.fit(X_train, Y_train)
|
||||
y_pred = classifier.predict(X_test)
|
||||
|
||||
|
||||
|
||||
fn=['ethnicity','gender','appearence']
|
||||
cn=['yes', 'no']
|
||||
|
||||
# Setting dpi = 300 to make image clearer than default (for the decision tree visualisation)
|
||||
fig, axes = plt.subplots(nrows = 1,ncols = 1,figsize = (4,4), dpi=300)
|
||||
|
||||
tree.plot_tree(classifier,
|
||||
feature_names = fn,
|
||||
class_names=cn,
|
||||
filled = True);
|
||||
|
||||
fig.savefig('imagenamenew.png')
|
||||
|
||||
|
||||
class Customer:
|
||||
def __init__(self, coordinate_i, coordinate_j):
|
||||
self.coordinate_i = coordinate_i
|
||||
self.coordinate_j = coordinate_j
|
||||
change_value(coordinate_i, coordinate_j,1 , 4)
|
||||
class CustomerPlace:
|
||||
def __init__(self, coordinate_i, coordinate_j):
|
||||
self.coordinate_i = coordinate_i
|
||||
self.coordinate_j = coordinate_j
|
||||
change_value(coordinate_i, coordinate_j,1 , 5)
|
||||
|
||||
|
||||
|
||||
###### /MD #######
|
||||
# Colors:
|
||||
# Define some colors
|
||||
BLACK = (0, 0, 0)
|
||||
WHITE = (255, 255, 255)
|
||||
GREEN = (0, 255, 0)
|
||||
RED = (255, 0, 0)
|
||||
BLUE = (0, 0, 240)
|
||||
YELLOW = (255, 255, 0)
|
||||
#Width and Height of each square:
|
||||
WIDTH = 20
|
||||
HEIGHT = 20
|
||||
|
||||
#Margin:
|
||||
MARGIN = 5
|
||||
grid = [[0 for x in range(16)] for y in range(16)]
|
||||
|
||||
def change_value(i, j, width, n):
|
||||
for r in range (i, i+width):
|
||||
for c in range (j, j+width):
|
||||
grid[r][c] = n
|
||||
|
||||
class Table:
|
||||
def __init__(self, coordinate_i, coordinate_j):
|
||||
self.coordinate_i = coordinate_i
|
||||
self.coordinate_j = coordinate_j
|
||||
change_value(coordinate_i, coordinate_j, 2, 1)
|
||||
def get_destination_coor(self):
|
||||
return [self.coordinate_i+2, self.coordinate_j+2]
|
||||
|
||||
########################
|
||||
### WS ###
|
||||
########################
|
||||
|
||||
# The finction "state of meal" chooses a photo of a plate at the given table.
|
||||
def state_of_meal(self):
|
||||
## !!!!!!###
|
||||
num = np.random.randint(67, 100)
|
||||
## !!!!!!###
|
||||
|
||||
if num<=67:
|
||||
img_name = 'plates/{}.png'.format(num)
|
||||
else:
|
||||
img_name = 'plates/{}.jpg'.format(num)
|
||||
return img_name
|
||||
|
||||
# The function "change state" changes the value of the state variable.
|
||||
# It informs, whether the client are waiting for the waiter to make an order
|
||||
# (0 - empty plates) are eating (2 - full plates) or are waiting for the
|
||||
# waiter to get a recipt (1 - dirty plates)
|
||||
|
||||
def change_state(self, st):
|
||||
self.state = st
|
||||
|
||||
########################
|
||||
### /WS ###
|
||||
########################
|
||||
|
||||
class Kitchen:
|
||||
def __init__(self, coordinate_i, coordinate_j):
|
||||
self.coordinate_i = coordinate_i
|
||||
self.coordinate_j = coordinate_j
|
||||
change_value(coordinate_i, coordinate_j, 3, 2)
|
||||
|
||||
class Agent:
|
||||
def __init__(self,orig_coordinate_i, orig_coordinate_j):
|
||||
self.orig_coordinate_i = orig_coordinate_i
|
||||
self.orig_coordinate_j = orig_coordinate_j
|
||||
self.state = np.array([orig_coordinate_i,orig_coordinate_j])
|
||||
change_value(orig_coordinate_j, orig_coordinate_j, 1, 3)
|
||||
self.state_update(orig_coordinate_i, orig_coordinate_j)
|
||||
|
||||
def state_update(self, c1, c2):
|
||||
self.state[0] = c1
|
||||
self.state[1] = c2
|
||||
|
||||
def leave(self):
|
||||
change_value(self.state[0], self.state[1], 1, 0)
|
||||
|
||||
|
||||
def move_to(self, nextPos):
|
||||
self.leave()
|
||||
nextPos_x, nextPos_y = nextPos
|
||||
self.state_update(nextPos_x, nextPos_y)
|
||||
change_value(self.state[0], self.state[1], 1, 3)
|
||||
|
||||
########################
|
||||
### WS ###
|
||||
########################
|
||||
|
||||
#The function "define_table" serches for the apropriate table in the
|
||||
# table_dict (to enable the usage of class attributes and methods)
|
||||
def define_table(self, t_num):
|
||||
t_num = 'table{}'.format(t_num)
|
||||
t_num = table_dict[t_num]
|
||||
return t_num
|
||||
|
||||
# The function "check_plates" uses the pretrained CNN model to classify
|
||||
# the plate on the table as empty(0), full(2) or dirty(1)
|
||||
def check_plates(self, table_number):
|
||||
table = self.define_table(table_number)
|
||||
plate = table.state_of_meal()
|
||||
plate= image.load_img(plate, target_size = (256, 256))
|
||||
plate = image.img_to_array(plate)
|
||||
#plate = plate.reshape((256, 256))
|
||||
plate = np.expand_dims(plate, axis = 0)
|
||||
|
||||
result = cnn_model.predict(plate)[0]
|
||||
print(result)
|
||||
if result[1] == 1:
|
||||
result[1] = 0
|
||||
x = int(input("Excuse me, have You done eating? 1=Yes, 2 = No \n"))
|
||||
result[x] = 1
|
||||
for i, x in enumerate(result):
|
||||
if result[i] == 1:
|
||||
table.change_state(i)
|
||||
return i
|
||||
########################
|
||||
### /WS ###
|
||||
########################
|
||||
|
||||
|
||||
def check_done():
|
||||
for event in pygame.event.get(): # Checking for the event
|
||||
if event.type == pygame.QUIT: # If the program is closed:
|
||||
return True # To exit the loop
|
||||
|
||||
def draw_grid():
|
||||
for row in range(16): # Drawing the grid
|
||||
for column in range(16):
|
||||
color = WHITE
|
||||
if grid[row][column] == 1:
|
||||
color = GREEN
|
||||
if grid[row][column] == 2:
|
||||
color = RED
|
||||
if grid[row][column] == 3:
|
||||
color = BLUE
|
||||
pygame.draw.rect(screen,
|
||||
color,
|
||||
[(MARGIN + WIDTH) * column + MARGIN,
|
||||
(MARGIN + HEIGHT) * row + MARGIN,
|
||||
WIDTH,
|
||||
HEIGHT])
|
||||
|
||||
|
||||
## default positions of the agent:
|
||||
x = 12
|
||||
y = 12
|
||||
agent = Agent(x, y)
|
||||
table1 = Table(2, 2)
|
||||
table2 = Table (2,7)
|
||||
table3 = Table(2, 12)
|
||||
table4 = Table(7, 2)
|
||||
table5 = Table(7, 7)
|
||||
table6 = Table(7, 12)
|
||||
table7 = Table(12, 2)
|
||||
table8 = Table(12, 7)
|
||||
|
||||
|
||||
################### WS #####################
|
||||
# I added the dict to loop through tables.
|
||||
table_dict = {"table1":table1, "table2":table2, "table3":table3,"table4":table4,
|
||||
"table5":table5,"table6":table6,"table7":table7,"table8":table8
|
||||
}
|
||||
################### WS #####################
|
||||
|
||||
pygame.init()
|
||||
|
||||
|
||||
####MD ####
|
||||
'''
|
||||
# create a font object.
|
||||
# 1st parameter is the font file
|
||||
# which is present in pygame.
|
||||
# 2nd parameter is size of the font
|
||||
font = pygame.font.Font('freesansbold.ttf', 14)
|
||||
X = 400
|
||||
Y = 400
|
||||
# create a text suface object,
|
||||
# on which text is drawn on it.
|
||||
text = font.render('waiter: hello, let me help you with your order.', True, WHITE, BLACK)
|
||||
userText=font.render('user: ', True, BLUE, BLACK)
|
||||
# create a rectangular object for the
|
||||
# text surface object
|
||||
textRect = text.get_rect()
|
||||
inputRect = userText.get_rect()
|
||||
# set the center of the rectangular object.
|
||||
textRect.center= (200, 340)
|
||||
inputRect.center=(200,370)
|
||||
|
||||
'''
|
||||
#### /MD ####
|
||||
|
||||
|
||||
#class Kitchen:
|
||||
kitchen = Kitchen(13, 13)
|
||||
|
||||
WINDOW_SIZE = [405, 405]
|
||||
screen = pygame.display.set_mode(WINDOW_SIZE)
|
||||
|
||||
pygame.display.set_caption("Waiter_Grid3")
|
||||
|
||||
done = False
|
||||
|
||||
clock = pygame.time.Clock()
|
||||
|
||||
with open('res_targets_4-1.data', 'rb') as filehandle:
|
||||
# read the data as binary data stream
|
||||
trained_route = pickle.load(filehandle)
|
||||
|
||||
destination = (4, 14)
|
||||
trained_route.append(destination)
|
||||
|
||||
table_targets = [(9, 4),(4, 4),(4, 9),(4, 14)]
|
||||
|
||||
|
||||
destination_tables = []
|
||||
|
||||
###### MD /########3
|
||||
x=[2,7,12]
|
||||
y=[2,7]
|
||||
|
||||
random_customer_seat_x=random.choice(x)
|
||||
random_customer_seat_y=random.choice(y)
|
||||
print(random_customer_seat_x,random_customer_seat_y)
|
||||
seat=Customer(random_customer_seat_x,random_customer_seat_y)
|
||||
|
||||
next_to=CustomerPlace(random_customer_seat_x,random_customer_seat_y-1)
|
||||
|
||||
WINDOW_SIZE = [405, 405]
|
||||
screen = pygame.display.set_mode(WINDOW_SIZE)
|
||||
|
||||
pygame.display.set_caption("Waiter_Grid3")
|
||||
|
||||
done = False
|
||||
print(random_customer_seat_x,random_customer_seat_y-1)
|
||||
clock = pygame.time.Clock()
|
||||
|
||||
|
||||
|
||||
#updating the drawing
|
||||
def updateDraw():
|
||||
x = agent.state[0]
|
||||
y = agent.state[1]
|
||||
screen.fill(BLACK) # Background color
|
||||
for row in range(16): # Drawing the grid
|
||||
for column in range(16):
|
||||
color = WHITE
|
||||
if grid[row][column] == 1:
|
||||
color = GREEN
|
||||
if grid[row][column] == 2:
|
||||
color = RED
|
||||
if grid[row][column] == 3:
|
||||
color = BLUE
|
||||
if grid[row][column] == 4:
|
||||
color = MAGENTA
|
||||
surface = pygame.draw.rect(screen,
|
||||
color,
|
||||
|
||||
[(MARGIN + WIDTH) * column + MARGIN,
|
||||
(MARGIN + HEIGHT) * row + MARGIN,
|
||||
WIDTH,
|
||||
HEIGHT])
|
||||
|
||||
def customer():
|
||||
screen.blit(text, textRect)
|
||||
ethnicity3 = input("Excuse me, What's you're ethnicity? <black>, <asian>, <white>\n")
|
||||
gender3= input("Excuse me, What's you're gender? <male>, <female>, <other> \n")
|
||||
appearence3=input("Excuse me, What's you're appearance? <hippie>, <other> \n")
|
||||
if (ethnicity3 =="black"):
|
||||
ethnicity3 = 1
|
||||
elif ethnicity3 == "asian":
|
||||
ethnicity3 = 0
|
||||
else:
|
||||
ethnicity3 = 2
|
||||
|
||||
if gender3 == "male":
|
||||
gender3 = 0
|
||||
else:
|
||||
gender3 = 1
|
||||
|
||||
if appearence3 == "hippie":
|
||||
appearence3= 0
|
||||
else:
|
||||
appearence3 = 1
|
||||
prediction = classifier.predict([[ethnicity3, gender3, appearence3]])
|
||||
#pygame.quit()
|
||||
|
||||
if prediction == [0]:
|
||||
print("You're probably not vegan. Would you like a regular menu?")
|
||||
else:
|
||||
print("It seems like you're vegan. Would you like a vegan menu?")
|
||||
#exit()
|
||||
###### /MD ###
|
||||
# -------- Main Program Loop -----------
|
||||
exit_counter = 0
|
||||
while not done:
|
||||
screen.fill(BLACK) # Background color
|
||||
draw_grid()
|
||||
done = check_done()
|
||||
new_route = trained_route[:]
|
||||
for value in table_dict.values():
|
||||
destination_tables.append(value.get_destination_coor())
|
||||
num_of_table = 1
|
||||
while len(new_route) != 0:
|
||||
# move to next grid
|
||||
agent.move_to(new_route[0])
|
||||
|
||||
# update the grid
|
||||
pygame.time.delay(150)
|
||||
screen.fill(BLACK)
|
||||
draw_grid()
|
||||
# Drawing the grid
|
||||
clock.tick(100) # Limit to 60 frames per second
|
||||
pygame.display.flip() # Updating the screen
|
||||
|
||||
# get current grid coordinate
|
||||
x = agent.state[0]
|
||||
y = agent.state[1]
|
||||
# if reached the table, ask to collect the plates
|
||||
if [x, y] in destination_tables:
|
||||
########################
|
||||
### WS ###
|
||||
########################
|
||||
#pygame.time.delay(100)
|
||||
print("I'm at a table no. {}".format(num_of_table))
|
||||
## Checking at what state are the plates:
|
||||
state_of_table = agent.check_plates(num_of_table)
|
||||
num_of_table +=1
|
||||
|
||||
if state_of_table == 0:
|
||||
customer()
|
||||
|
||||
# Early stopping (after 10 rounds)
|
||||
exit_counter += 1
|
||||
print("exit_counter", exit_counter)
|
||||
if exit_counter == 10:
|
||||
play_again = 1
|
||||
play_again = int(input("Exit? 0=No, 1=Yes \n"))
|
||||
if play_again:
|
||||
pygame.quit()
|
||||
else:
|
||||
exit_counter = 0
|
||||
|
||||
########################
|
||||
### /WS ###
|
||||
########################
|
||||
destination_tables = destination_tables[1:]
|
||||
|
||||
new_route = new_route[1:]
|
||||
|
||||
# After each fool loop, we can quit the program:.
|
||||
|
||||
|
||||
|
||||
pygame.quit()
|
||||
Loading…
Reference in New Issue
Block a user