def test_OBJECT(self):
dictionary = {"Producator":"Mercedes","Caroserie":"Sedan","Model":"C180","AnProductie":1999,"VolumMotor":1800,
"Pret":8000,"Carburant":"Benzina","Link":"http://www.google.ro"}
object = CarObject.carObject(dictionary)
self.assertItemsEqual(dictionary.values(),[object._Producator,object._Caroserie,
object._Model,object._AnProductie,object._VolumMotor
,object._Pret,object._Carburant,object._Link])
def test_client(self):
request = 'GET /AutomobileVanzare?Producator=Mercedes&Model=C180&Caroserie=hatchback'
db = DatabaseManager('localhost')
# Get SQL Query from REST
sqlQuery = DatabaseManager.getSQLqueryFromREST(restQuery)
# Get the result from SQL Server
carObjects = db.SelectFromDatabase(sqlQuery)
#the object which i expect to receive is
dictionary = {"Producator":"Mercedes","Caroserie":"Sedan","Model":"C180","AnProductie":1999,"VolumMotor":1800,
"Pret":8000,"Carburant":"Benzina","Link":"http://www.google.ro"}
object = CarObject.carObject(dictionary)
self.assertEqual(dictionary.values(),carObjects[0].values())
def SelectFromDatabase(self,query):
logging.warning("DatabaseManager - Querrying database with "+query)
try:
#execute the db query
self.cursor.execute(query)
#get the answers into a list
ListOfCars = self.cursor.fetchall()
except:
logging.error('DatabaseManager - Error Executing SQL query '+query)
#retunrn the list of cars
#answer is a list of car objects
answer = []
for carArray in ListOfCars:
dictionary= {"Caroserie":carArray[1],"Model":carArray[3],"Producator":carArray[2],
"AnProductie":carArray[4],"VolumMotor":carArray[5],
"Pret":carArray[6],"Carburant":carArray[7],
"Link":carArray[8]}
answer.append(CarObject.carObject(dictionary))
return answer
import pygame
import math
import time
from CarImage import CarImage
from CarObject import *
import time
import numpy
#Initiate pygame, and a screen for simulation.
pygame.init()
screen = pygame.display.set_mode((CarImage.scale*sizeX,CarImage.scale*sizeY))
#Create a CarImage, which contains all the car data, and draws it on the screen.
#Also initialise it with a bunch of values.
#The car is initially positioned in the middle of the screen, close to the bottom.
car = CarImage(CarObject(500,300,0),screen)
car.carObject.v=40
car.carObject.wheelAngle=0
car.carObject.angle = numpy.random.uniform(0, 2*math.pi)
done=False
while not done:
for event in pygame.event.get():
if event.type == pygame.QUIT:
done=True
if not done:
#Change the position of the car, based on the physics programmed in CarObject
car.carObject.changePosition()
#Redraw screen
def run(self, count, generation):
#Start time, and reset score.
start = self.currentTime()
score = 0
#Initiate pygame, and a screen for simulation.
pygame.init()
screen = pygame.display.set_mode((scale*sizeX,scale*sizeY))
#Create a CarImage, which contains all the car data, and draws it on the screen.
#Also initialise it with a bunch of values.
#The car is initially positioned in the middle of the screen, close to the bottom.
car = CarImage(CarObject(300,250,0),screen)
string = "weights" +str(count)+ ".dat"
try:
car.carObject.neuralnet.readWeights(string)
except:
pass
car.carObject.v=40
car.carObject.wheelAngle=0
#alternate the initial orientation of the car with each generation.
#This makes sure the car will learn to steer both left and right.
genParity = generation%2
if genParity == 0:
car.carObject.angle = math.pi/2
else:
car.carObject.angle = -math.pi/2
#The simulation has to end if the car only turns, so keep track of this.
turnCount = 0
maxTurns = 2*math.pi*car.carObject.length/dt/car.carObject.v/math.sin(math.pi/6)
#Game loop.
done=False
while not done:
for event in pygame.event.get():
if event.type == pygame.QUIT:
done=True
if not done:
#Change the position of the car, based on the physics programmed in CarObject
car.carObject.changePosition()
#Redraw screen
screen.fill((0,0,0))
car.show(-car.carObject.angle) #because the y-axis is flipped, angles are reversed.
#Update distances for input in neural network.
car.carObject.calcDistances()
#End simulation if car crashes.
if car.checkBounds():
done = True
#The car has to decide what to do based on its location, which is obtained from calcDistances.
car.carObject.decide()
#The car gets a point of fitness score for each timestep it drives straight
#If it only drives in circles, simulation ends because it wont get points.
if car.carObject.wheelAngle == 0:
score += 1
turnCount = 0
else:
#score += 1
turnCount += 1
if genParity == 0 and car.carObject.wheelAngle < 0:
score -= 6
if genParity == 1 and car.carObject.wheelAngle > 0:
score -= 6
if turnCount > maxTurns:
print("Car got stuck in a loop!")
done = True
#Simulation ends after 10 seconds
if self.currentTime() - start > 10000:
print("Car drove without crashing!")
done = True
#Switch screen buffers, pygame technicality.
pygame.display.flip()
#time.sleep(dt)
#After game loop, quit pygame
pygame.display.quit()
pygame.quit()
#Print current best score, second best score and score of last drive for debug purposes
#print("Score is: {}".format(score))
#print("Best is: {}".format(best))
#print("Second is: {}".format(second))
#Save network weights, so that current best is saved in weights1,
#and second best in weights2.
if score > self.best:
self.second = self.best
try:
file = open("weights1.dat", "r")
secondWeights = [float(line.rstrip('\n')) for line in file]
file.close()
file = open("weights2.dat", "w")
for weight in secondWeights:
file.write("%s\n" % weight)
file.close()
except:
pass
self.best = score
print("New best with score: {}".format(score))
car.carObject.neuralnet.saveWeights("weights1.dat")
elif score > self.second:
self.second = score
print("New second with score: {}".format(score))
car.carObject.neuralnet.saveWeights("weights2.dat")