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alexandre
2019-10-28 11:52:03 +01:00
parent 132cec2445
commit f0580a988c
8 changed files with 213 additions and 178 deletions
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23
brain.py
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@@ -1,8 +1,13 @@
import numpy as np
import random
def mat_mult(A, B):
return [[sum([A[i][m]*B[m][j] for m in range(len(A[0]))]) for j in range(len(B[0]))] for i in range(len(A))]
return [
[sum([A[i][m] * B[m][j] for m in range(len(A[0]))]) for j in range(len(B[0]))]
for i in range(len(A))
]
class Neural_Network(object):
# inspired from https://enlight.nyc/projects/neural-network/
@@ -17,20 +22,28 @@ class Neural_Network(object):
if W1 is not None:
self.W1 = W1
else:
self.W1 = np.random.randn(self.inputSize, self.hiddenSize) # weights from input to hidden layer
self.W1 = np.random.randn(
self.inputSize, self.hiddenSize
) # weights from input to hidden layer
if W2 is not None:
self.W2 = W2
else:
self.W2 = np.random.randn(self.hiddenSize, self.outputSize) # weights from hidden to output layer
self.W2 = np.random.randn(
self.hiddenSize, self.outputSize
) # weights from hidden to output layer
# self.w1 = [[random.random() for i in range(self.hiddenSize)] for i in range(self.inputSize)]
# self.w2 = [[random.random() for i in range(self.outputSize)] for i in range(self.hiddenSize)]
def predict(self, X):
# forward propagation through our network
self.z = np.dot(X, self.W1) # dot product of X (input) and first set of 3x2 weights
self.z = np.dot(
X, self.W1
) # dot product of X (input) and first set of 3x2 weights
self.z2 = self.sigmoid(self.z) # activation function
self.z3 = np.dot(self.z2, self.W2) # dot product of hidden layer (z2) and second set of 3x1 weights
self.z3 = np.dot(
self.z2, self.W2
) # dot product of hidden layer (z2) and second set of 3x1 weights
o = self.sigmoid(self.z3) # final activation function
# self.z = mat_mult(X, self.w1) # dot product of X (input) and first set of 3x2 weights
# self.z2 = self.sigmoid(self.z) # activation function

69
car.py
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@@ -4,7 +4,17 @@ import random
import pygame
from brain import Neural_Network
from params import GY, CAR_MAX_SPEED, CAR_MAX_FITNESS, CAR_SIZE, CAR_STEERING_FACTOR, VISION_LENGTH, VISION_SPAN, THROTTLE_POWER, screen
from params import (
GY,
CAR_MAX_SPEED,
CAR_MAX_FITNESS,
CAR_SIZE,
CAR_STEERING_FACTOR,
VISION_LENGTH,
VISION_SPAN,
THROTTLE_POWER,
screen,
)
from trigo import angle_to_vector, get_line_feats, segments_intersection, distance
IMG = pygame.image.load("car20.png") # .convert()
@@ -53,7 +63,7 @@ class Car(pygame.sprite.Sprite):
def reset_car_pos(self):
self.rect.center = (
75 - int(random.random() * 20) - 10,
GY -50 - int(random.random()*20)-10
GY - 50 - int(random.random() * 20) - 10,
)
self.speed = 1
self.heading = random.random() * 20
@@ -62,29 +72,49 @@ class Car(pygame.sprite.Sprite):
def update_sensors(self):
center = self.rect.center
vc = angle_to_vector(self.heading)
self.center_sensor = [center, (int(self.vision_length * vc[0] + center[0]), int(-self.vision_length * vc[1] + center[1]))]
self.center_sensor = [
center,
(
int(self.vision_length * vc[0] + center[0]),
int(-self.vision_length * vc[1] + center[1]),
),
]
vl = angle_to_vector(self.heading + self.vision_span)
self.left_sensor = [center, (int(self.vision_length * vl[0] + center[0]), int(-self.vision_length * vl[1] + center[1]))]
self.left_sensor = [
center,
(
int(self.vision_length * vl[0] + center[0]),
int(-self.vision_length * vl[1] + center[1]),
),
]
vr = angle_to_vector(self.heading - self.vision_span)
self.right_sensor = [center, (int(self.vision_length * vr[0] + center[0]), int(-self.vision_length * vr[1] + center[1]))]
self.right_sensor = [
center,
(
int(self.vision_length * vr[0] + center[0]),
int(-self.vision_length * vr[1] + center[1]),
),
]
def update_position(self):
vec = angle_to_vector(self.heading)
old_center = self.rect.center
self.rect.center = (self.speed * vec[0] / 2 + old_center[0], -self.speed * vec[1] / 2 + old_center[1])
self.rect.center = (
self.speed * vec[0] / 2 + old_center[0],
-self.speed * vec[1] / 2 + old_center[1],
)
self.update_sensors()
self.distance_run += int(distance(old_center, self.rect.center))
self.brain.fitness = int(math.sqrt(self.distance_run))
def probe_lines_proximity(self, lines):
# print(self.center_sensor, lines[0])
self.probes = [self.vision_length * 2] * 3
for idx,sensor in enumerate([self.left_sensor, self.center_sensor, self.right_sensor]) :
for idx, sensor in enumerate(
[self.left_sensor, self.center_sensor, self.right_sensor]
):
for line in lines:
ip = segments_intersection(sensor, line)
# print(ip)
@@ -103,13 +133,11 @@ class Car(pygame.sprite.Sprite):
# self.probes[idx] = self.vision_length * 2
# print(self.probes)
def probe_brain(self):
res = self.brain.predict(np.array(self.probes))
self.heading_change = res[0] * 15
self.throttle = res[1] * 10
def update(self):
# rotate
old_center = self.rect.center
@@ -119,7 +147,7 @@ class Car(pygame.sprite.Sprite):
self.update_position()
if self.speed < 0.01 or self.brain.fitness > CAR_MAX_FITNESS:
self.run = False
print(f'Car {id(self)} crashed')
print(f"Car {id(self)} crashed")
# print(
# 'id', id(self),
# 'Speed', self.speed,
@@ -143,12 +171,15 @@ class Car(pygame.sprite.Sprite):
super().update()
def show_features(self):
if self.draw_sensors:
pygame.draw.line(screen, (255,0,0), self.center_sensor[0], self.center_sensor[1])
pygame.draw.line(screen, (0,255,0), self.left_sensor[0], self.left_sensor[1])
pygame.draw.line(screen, (0,0,255), self.right_sensor[0], self.right_sensor[1])
pygame.draw.line(
screen, (255, 0, 0), self.center_sensor[0], self.center_sensor[1]
)
pygame.draw.line(
screen, (0, 255, 0), self.left_sensor[0], self.left_sensor[1]
)
pygame.draw.line(
screen, (0, 0, 255), self.right_sensor[0], self.right_sensor[1]
)
pygame.draw.circle(screen, (125, 255, 125), self.rect.center, 4, 2)

19
genetics.py
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@@ -3,6 +3,7 @@ import random
from brain import Neural_Network
from params import MUTATION_RATE, SELECTION_ALG, KWAY_TOURNAMENT_PLAYERS
def kway_selection(brains, exclude=None):
tourn_pool = []
best_play = None
@@ -16,6 +17,7 @@ def kway_selection(brains, exclude=None):
best_play = new_play
return best_play
def genetic_selection(brains):
parents_pool = []
half_pop = int(len(brains) / 2)
@@ -24,12 +26,7 @@ def genetic_selection(brains):
for x in range(half_pop):
p1 = kway_selection(brains)
p2 = kway_selection(brains, exclude=p1)
parents_pool.append([
p1,
p2
])
parents_pool.append([p1, p2])
elif SELECTION_ALG == "roulette":
# does not seem very optimized... TBR
@@ -39,17 +36,13 @@ def genetic_selection(brains):
for b in brains:
wheel += [b] * b.fitness
tot_fitness = len(wheel)
# selection of pool/2 pair of parents to reproduce
for _ in range(half_pop):
idx1 = round(random.random() * tot_fitness - 1)
idx2 = round(random.random() * tot_fitness - 1)
parents_pool.append([
wheel[idx1],
wheel[idx2]
])
parents_pool.append([wheel[idx1], wheel[idx2]])
return parents_pool
@@ -81,7 +74,3 @@ def genetic_reproduction(parents_pool):
new_pop.append(c_brain1)
new_pop.append(c_brain2)
return new_pop

8
main.py
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@@ -10,7 +10,6 @@ from maps import map1
from params import CELL_COLOR, screen
# https://medium.com/intel-student-ambassadors/demystifying-genetic-algorithms-to-enhance-neural-networks-cde902384b6e
clock = pygame.time.Clock()
@@ -18,7 +17,6 @@ clock = pygame.time.Clock()
map_lines = map1
all_cars = pygame.sprite.Group()
for x in range(100):
@@ -49,16 +47,16 @@ def run_round(all_cars):
# for c in all_cars :
# print(f"Car {id(c)} Fitness : {c.brain.fitness})")
print('Collecting brains')
print("Collecting brains")
brains = [c.brain for c in all_cars]
print(f"Max fitness = {max([b.fitness for b in brains])}")
print(f"Avg fitness = {sum([b.fitness for b in brains])/len(brains)}")
print('selecting')
print("selecting")
parents_pool = genetic_selection(brains)
# import ipdb; ipdb.set_trace()
print("breeding")
new_brains = genetic_reproduction(parents_pool)
print(f'building {len(new_brains)} cars with new brains')
print(f"building {len(new_brains)} cars with new brains")
all_cars.empty()
for b in new_brains:
all_cars.add(Car(brain=b))

2
maps.py
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@@ -1,5 +1,6 @@
from params import GX, GY
def generate_map_1():
path = [
(25, int(GY - 25)),
@@ -37,4 +38,5 @@ def generate_map_1() :
lines = lines + lines2
return lines
map1 = generate_map_1()

6
trigo.py
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@@ -1,6 +1,7 @@
#!/usr/bin/env python
import math
def angle_to_vector(angle):
angle = angle * math.pi / 180
return [math.cos(angle), math.sin(angle)]
@@ -25,7 +26,6 @@ def segments_intersection(line1, line2):
if p3[0] == p4[0]:
p3 = (p3[0] + 1, p3[1])
a1, b1 = get_line_feats(p1, p2)
a2, b2 = get_line_feats(p3, p4)
@@ -34,7 +34,9 @@ def segments_intersection(line1, line2):
x = (b2 - b1) / (a1 - a2)
if min(p1[0], p2[0]) <= x <= max (p1[0], p2[0]) and min(p3[0], p4[0]) <= x <= max (p3[0], p4[0]) :
if min(p1[0], p2[0]) <= x <= max(p1[0], p2[0]) and min(p3[0], p4[0]) <= x <= max(
p3[0], p4[0]
):
y = a1 * x + b1
return x, y
else: