Ejemplo n.º 1
0
def bubbleSort(data):
    data_length = len(data)
    for i in range(data_length - 1):
        for j in range(data_length - i - 1):
            if data[j] > data[j + 1]:
                data[j], data[j + 1] = data[j + 1], data[j]
                draw(j + 1, data)
Ejemplo n.º 2
0
def selectionSort(data):
    data_length = len(data)
    for i in range(data_length - 1):
        least = i
        for j in range(i + 1, data_length):
            if data[j] < data[least]:
                least = j
        if i != least:
            data[i], data[least] = data[least], data[i]
            draw(i + 1, data)
Ejemplo n.º 3
0
def insertionSort(data):
    data_length = len(data)
    for i in range(1, data_length):
        j = i - 1
        key = data[i]
        while data[j] > key and j >= 0:
            data[j + 1] = data[j]
            j = j - 1
        data[j + 1] = key
        draw(j + 1, data)
Ejemplo n.º 4
0
def heapify(data, index, size):
    largest = index
    left = 2 * index + 1
    right = 2 * index + 2
    if left < size and data[left] > data[largest]:
        largest = left
    if right < size and data[right] > data[largest]:
        largest = right
    if largest != index:
        data[largest], data[index] = data[index], data[largest]
        heapify(data, largest, size)
        draw(largest, data)
Ejemplo n.º 5
0
def testQLearning(gamma, epsilon, surprise):
    global state_sequence
    print '** Testing Q-Learning algorithm **'
    print 'Gamma: ', gamma
    print 'Epsilon: ', epsilon
    print 'Suprise: ', surprise

    qLearning(gamma, epsilon, surprise)

    l = len(state_sequence)
    print '\tState sequence ', state_sequence[l - 20:]
    animate.draw(state_sequence[l - 20:])
Ejemplo n.º 6
0
def testQLearning(gamma, epsilon, surprise):
	global state_sequence
	print '** Testing Q-Learning algorithm **'
	print 'Gamma: ', gamma
	print 'Epsilon: ', epsilon
	print 'Suprise: ', surprise

	qLearning(gamma, epsilon, surprise)
	
	l = len(state_sequence)
	print '\tState sequence ', state_sequence[l-20:]
	animate.draw(state_sequence[l-20:])
Ejemplo n.º 7
0
def main():
    # Get the variables used to do reinforcement learning
    gamma = 0.7
    T = 10000
    epsilon = 0.2
    s = 12
    n = 0.1
    e = Environment(s)

    Q = calculate_route(T, n, gamma, e, s, epsilon)
    print("calculation done")

    draw(e.get_loop(Q, s))
Ejemplo n.º 8
0
def main():
    # Get the variables used to do reinforcement learning
    gamma = 0.7
    T = 10000
    epsilon = 0.2
    s = 12
    n = 0.1
    e = Environment(s)

    Q = calculate_route(T, n, gamma, e, s, epsilon)
    print("calculation done")

    draw(e.get_loop(Q, s))
Ejemplo n.º 9
0
def testPolicyIteration(rew, gamma, iterations):
	print '** Testing policy iteration algorithm **'
	print 'Gamma: ', gamma
	print 'Iterations: ', iterations
	policyIteration(rew, gamma, iterations)

	print '\tOptimal policy: ', policy
	optimal_state_map = [ trans[a][policy[a]] for a in range(len(policy)) ]    
	print '\tOptimal state map', optimal_state_map
	
	current_state = int(random.random()*len(policy))
	path = [current_state]
	for i in range(20):
		current_state = optimal_state_map[current_state]
		path += [current_state]

	print '\t(Hopefully) correct walking path', path
	
	animate.draw(path)
Ejemplo n.º 10
0
def testPolicyIteration(rew, gamma, iterations):
    print '** Testing policy iteration algorithm **'
    print 'Gamma: ', gamma
    print 'Iterations: ', iterations
    policyIteration(rew, gamma, iterations)

    print '\tOptimal policy: ', policy
    optimal_state_map = [trans[a][policy[a]] for a in range(len(policy))]
    print '\tOptimal state map', optimal_state_map

    current_state = int(random.random() * len(policy))
    path = [current_state]
    for i in range(20):
        current_state = optimal_state_map[current_state]
        path += [current_state]

    print '\t(Hopefully) correct walking path', path

    animate.draw(path)
Ejemplo n.º 11
0
Archivo: main.py Proyecto: kimseo/ml13
            a = policy[s]

            print str(rew[s][a]) + "= " + str(s) + "|" + str(a)
            print str(rew[s][a]) + " + " + str(gamma) + " * " + str(value[trans[s][a]])
            value[s] = rew[s][a] + gamma * value[trans[s][a]]

def move(state, steps):
    ret = [state]
    for s in range(steps):
        state = trans[state][policy[state]]
        ret.append(state)
    return ret


print rew

print "=== before "
print policy
print value

pol_iter(100)

print "=== after "
print policy
print value

result = move(0, 20)

a.draw(result)

Ejemplo n.º 12
0
          pylab.imread('step5.png'),
          pylab.imread('step6.png'),
          pylab.imread('step7.png'),
          pylab.imread('step8.png'),
          pylab.imread('step9.png'),
          pylab.imread('step10.png'),
          pylab.imread('step11.png'),
          pylab.imread('step12.png'),
          pylab.imread('step13.png'),
          pylab.imread('step14.png'),
          pylab.imread('step15.png'),
          pylab.imread('step16.png'))

#comic = numpy.concatenate([images[i] for i in result], axis=1)

#pylab.imshow(comic)
#pylab.show()
env = Environment()
epsilon = 0.01
eta = 0.1
gamma = 0.9
q = qlearn(env, 200000, epsilon, eta, gamma)
print(q)
moves= qMove(0, 20, q)
print(moves)
a.draw(moves)
comic = numpy.concatenate([images[i] for i in moves], axis=1)

pylab.imshow(comic)
pylab.show()
Ejemplo n.º 13
0
          pylab.imread('step9.png'),
          pylab.imread('step10.png'),
          pylab.imread('step11.png'),
          pylab.imread('step12.png'),
          pylab.imread('step13.png'),
          pylab.imread('step14.png'),
          pylab.imread('step15.png'),
          pylab.imread('step16.png'))

#visualization of the robot walk

#comic = numpy.concatenate([images[i] for i in sequence], axis=1)

#pylab.imshow(comic)
#pylab.show()
animate.draw(sequence)



class Environment :
	'''
	Representation of the environment for the Q-learning algorithm
	'''
	def __init__(self, state=0):
		self.state = state
		self.trans = ((1, 3 ,4 ,12),
		 			  (0, 2, 5, 13),
		 			  (3, 1, 6, 14),
		 			  (2, 0, 7, 15),
		 			  (5, 7, 0, 8),
		 			  (4, 6, 1, 9),