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car.py
98 lines (86 loc) · 3.59 KB
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car.py
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import utils
import math
from neuralNetwork import NeuralNetwork
class Car(object):
def __init__(self, (x, y)):
self.neuralNet = NeuralNetwork(7, 2, 1, 5)
self.neuralNet.create()
self.fitness = 0
self.frontWidth = 20
self.sideWidth = 40
self.position = (x, y)
self.direction = 0
self.edgesPoints = [[self.position[0] - self.sideWidth//2, self.position[1] - self.frontWidth//2],
[self.position[0] - self.sideWidth//2, self.position[1] + self.frontWidth//2],
[self.position[0] + self.sideWidth//2, self.position[1] + self.frontWidth//2],
[self.position[0] + self.sideWidth//2, self.position[1] - self.frontWidth//2],
[self.position[0] - self.sideWidth//2, self.position[1] - self.frontWidth//2]]
self.edgesPointsAprox = self.edgesPoints
self.speed = 10
self.isAlive = True
self.rayPoints = [[], [], [], [], [], [], []]
self.inputs = [0, 0, 0, 0, 0, 0, 0]
self.lastsCookies = []
self.cookie = 0
def __getattr__(self, name):
if name == 'frontPoint':
return utils.midpoint(self.edgesPoints[0], self.edgesPoints[1])
elif name == 'leftPoint':
return utils.midpoint(self.edgesPoints[1], self.edgesPoints[2])
elif name == 'backPoint':
return utils.midpoint(self.edgesPoints[2], self.edgesPoints[3])
elif name == 'rightPoint':
return utils.midpoint(self.edgesPoints[3], self.edgesPoints[0])
elif name == 'frontRightPoint':
return self.edgesPoints[0]
elif name == 'frontLeftPoint':
return self.edgesPoints[1]
elif name == 'frontRight2Point':
return utils.midpoint(self.edgesPoints[0], self.rightPoint)
elif name == 'frontLeft2Point':
return utils.midpoint(self.edgesPoints[1], self.leftPoint)
elif name == 'isGoingForward':
if self.speed > 0:
return True
else:
return False
def reset(self, (x, y)):
self.fitness = 0
self.position = (x, y)
self.direction = 0
self.edgesPoints = [[self.position[0] - self.sideWidth//2, self.position[1] - self.frontWidth//2],
[self.position[0] - self.sideWidth//2, self.position[1] + self.frontWidth//2],
[self.position[0] + self.sideWidth//2, self.position[1] + self.frontWidth//2],
[self.position[0] + self.sideWidth//2, self.position[1] - self.frontWidth//2],
[self.position[0] - self.sideWidth//2, self.position[1] - self.frontWidth//2]]
self.edgesPointsAprox = self.edgesPoints
self.speed = 8
self.isAlive = True
self.rayPoints = [[], [], [], [], [], [], []]
self.inputs = [0, 0, 0, 0, 0, 0, 0]
self.lastsCookies = []
self.cookie = 0
def update(self):
if self.isAlive is False:
return
outputs = self.neuralNet.update(self.inputs)
self.direction += (outputs[0] - 0.5) * 20
self.speed = outputs[1] * 10
rad = math.radians(self.direction - 90)
x, y = self.position
x += self.speed*math.sin(rad)
y += self.speed*math.cos(rad)
self.position = (x, y)
self.edgesPoints = [[self.position[0] - self.sideWidth//2, self.position[1] - self.frontWidth//2],
[self.position[0] - self.sideWidth//2, self.position[1] + self.frontWidth//2],
[self.position[0] + self.sideWidth//2, self.position[1] + self.frontWidth//2],
[self.position[0] + self.sideWidth//2, self.position[1] - self.frontWidth//2],
[self.position[0] - self.sideWidth//2, self.position[1] - self.frontWidth//2]]
aux = 0
for p in self.edgesPoints:
self.edgesPoints[aux] = utils.rotate(self.position, p, -self.direction)
self.edgesPointsAprox[aux] = int(round(self.edgesPoints[aux][0])), int(round(self.edgesPoints[aux][1]))
aux += 1
def incrementFitness(self):
self.cookie += 1
self.fitness += self.cookie + (self.cookie*self.speed/10)