def main():
m = readMatrix('matrix_games/5x5.txt')
numR, numC = m.shape
Population.initPopulation = initPopulation
Population.evolve = evolve
p = Population(30, 2)
p.initPopulation(numC, numR)
p.evolve(m)
def main():
pygame.init()
window = pygame.display.set_mode((d_width, d_height))
circles = getCircles()
for circle in circles:
pygame.draw.circle(window, (255, 255, 255), circle[0], circle[1], 1)
Population.initPopulation = initPopulation
Population.evolve = evolve
p = Population(80, 3)
p.initPopulation()
p.evolve(window, circles)
#pygame.display.flip()
while True:
for event in pygame.event.get():
if event.type == pygame.QUIT:
sys.exit(0)
def testNeuralNetWithGA():
net = NeuralNet(2, 2, 1)
t_model = utils.readTrainModel(os.path.join(utils.getResourcesPath(), 'logic_gates/NAND.txt'))
Population.initPopulation = initPopulation
Population.evolve = evolve
p = Population(70, 9)
p.initPopulation()
p.evolve(net, t_model)
print(net.getOutputs([0, 0]))
print(net.getOutputs([0, 1]))
print(net.getOutputs([1, 0]))
print(net.getOutputs([1, 1]))
print(net.getError(t_model))
#testPerceptron()
#testNeuralNet()
#testNeuralNetWithGA()
#numberRecognition()
def main():
pygame.init()
maze = readMaze('mazes/9x15.txt')
numR, numC = maze.shape
window = pygame.display.set_mode((numC * square_l, numR * square_l))
renderMaze(window, maze)
Population.initPopulation = initPopulation
Population.evolve = evolve
p = Population(50, 30)
p.mutation_rate = 0.1
p.elites_num = 5
p.initPopulation()
p.evolve(window, maze)
print("done")
while True:
for event in pygame.event.get():
if event.type == pygame.QUIT:
sys.exit(0)
def main():
global fitnessHistory
Population.initPopulation = initPopulation
Population.evolve = evolve
p = Population(30, 1)
#fig = plt.figure()
for i in range(1):
colors = ['b', 'r', 'g']
p.initPopulation()
p.evolve()
#ax = fig.add_subplot(111)
#ax.plot(np.arange(len(fitnessHistory)), fitnessHistory, c=colors[i])
fitnessHistory = []
h = np.array(history)
h = np.rot90(h, 1)
print(h)
plt.imshow(h, cmap='Greys', aspect='auto', interpolation='nearest')
plt.show()