def experiment1(): l = CMAES(fitnessFunction, myNetwork.params) l.minimize = True l.verbose = True l.maxLearningSteps = 500 params, fitness = l.learn() myNetwork._setParameters(params) logNet()
def markovSourceDenoise(transition_prob, deletion_rate, weighted=False):
global delta
delta = transition_prob
global rho
rho = deletion_rate
global max_del
max_del = 2
global n
n = 10000
display = 50
global alphabet
alphabet = ['+', '-']
global orig
orig = generateSequence(n, delta)
global noisy
noisy = deletionChannel(orig, rho)
est1 = denoiseSequence2(noisy, k, alphabet, rho, max_del)
#est2 = denoiseSequence3(noisy, k, alphabet, rho)
est4 = denoiseSequence4(noisy, k, alphabet, rho)
est = optimalDenoise(noisy, k, alphabet, rho, delta)
print 'Setting: delta = ', delta, ', rho = ', rho
print 'Original: ', orig[:display], '(length ', len(
orig), ' error ', error(orig, orig) / (n + 0.0), levenshtein(
orig, orig) / (n + 0.0), ')'
print 'Noisy: ', noisy[:display], '(length ', len(noisy), ' error ', error(
noisy, orig) / (n + 0.0), levenshtein(noisy, orig) / (n + 0.0), ')'
print 'Optimal Denoised: ', est[:display], '(length ', len(
est), ' error ', error(est, orig) / (n + 0.0), levenshtein(
est, orig) / (n + 0.0), ' )'
print 'Denoiser 1: ', est1[:display], '(length ', len(
est1), ' error ', error(est1, orig) / (n + 0.0), levenshtein(
est1, orig) / (n + 0.0), ')'
print 'Denoiser 4: ', est4[:display], '(length ', len(
est4), ' error ', error(est4, orig) / (n + 0.0), levenshtein(
est4, orig) / (n + 0.0), ')'
#print 'Denoiser 2: ', est2[:display], '(length ', len(est2), ' error ', error(est2, orig)/(n+0.0), ')'
if weighted:
l1 = CMAES(weightWrapper1, [1])
l1.minimize = True
l1.maxEvaluations = 200
opt_vals1 = l1.learn()
weights1 = opt_vals1[0]
err1 = opt_vals1[1]
#l2 = CMAES(weightWrapper2, [1]*k)
#l2.minimize = True
#l2.maxEvaluations = 200
#opt_vals2 = l2.learn()
#weights2 = opt_vals2[0]
#err2 = opt_vals2[1]
print(weights1)
weighted_est1 = denoiseSequence2(noisy, k, alphabet, rho, max_del,
weights1)
#weighted_est2 = denoiseSequence3(noisy, k, alphabet, rho, k, weights2)
print 'Weighted Denoiser 1: ', weighted_est1[:display], '(length ', len(
weighted_est1), ' error ', err1, ')'
#print 'Weighted Denoiser 2: ', weighted_est2[:display], '(length ', len(weighted_est2), ' error ', err2, ')'
print '\n' * 5
def trainOne(index):
def opt_func(w):
return (np.sum((np.dot(x_train, w)
- y_train2[:, index]) ** 2)
- 1000 * np.sum(np.sign(w) - 1))
l = CMAES(opt_func, np.random.randn(np.shape(x_train)[1]))
l.minimize = True
opt_w = l.learn()[0]
return opt_w
def trainOne(index):
def opt_func(w):
return (np.sum((np.dot(x_train, w)
- y_train2[:, index]) ** 2)
- 1000 * np.sum(np.sign(w) - 1))
l = CMAES(opt_func, np.random.randn(np.shape(x_train)[1]))
l.minimize = True
opt_w = l.learn()[0]
return opt_w
def experiment1(self):
l = CMAES(self.fitnessFunction, self.myNetwork.params[self.indices])
l.minimize = True
l.verbose = True
l.maxLearningSteps = 500
params, fitness = l.learn()
self.myNetwork.params[self.indices] = params
self.metaInfo["numsteps"] = l.maxLearningSteps
self.metaInfo["fitness"] = fitness
# self.myNetwork._setParameters(self.originalWeights)
self.logNet()
def trainNetwork(data, n_classes, buildNet, file, seed, max_evaluations, num_samples):
# The training functions uses the average of the cumulated reward and maximum height as fitness
X_train = data["X_train"]
y_train = data["y_train"]
def objF(params):
nn = buildNet(X_train.shape[1], n_classes)
nn._setParameters(np.array(params))
random_state = np.random.get_state()
np.random.seed(l.numLearningSteps)
sampled_data = np.random.choice(len(X_train), num_samples, replace=False)
np.random.set_state(random_state)
cur_data = X_train[sampled_data]
cur_label = y_train[sampled_data]
cum_correct = 0
for example, cor in zip(cur_data, cur_label):
result = nn.activate(example)
loss_sum = 0
for q, out in enumerate(result):
if q != cor:
loss_sum += max(0, out - result[int(cor)] + 1)
# guess = np.argmax(result)
#if guess == cor:
#cum_correct += 1
cum_correct += loss_sum
nn.reset()
return cum_correct
# Build net for initial random params
n = buildNet(X_train.shape[1], n_classes)
learned = n.params
testNetwork(data, n_classes, learned, buildNet, 0, file, seed)
l = CMAES(objF, learned, verbose=False)
batch_size = l.batchSize
# l._setBatchSize = batch_size
l.maxEvaluations = max_evaluations
l.minimize = True
for i in xrange((max_evaluations/batch_size)):
result = l.learn(additionalLearningSteps=1)
learned = result[0]
testNetwork(data, n_classes, learned, buildNet, num_samples * (i + 1) * batch_size, file, seed)
return learned
def __init__(self, x_learn, y_learn):
_, y = nclass_to_nbinary(y_learn)
# x = np.swapaxes(x_learn, 1, 2)
w = np.random.randn(np.shape(x)[2])
print "x size", x.shape
print "y size", y.shape
func = lambda w: np.sum((np.dot(x, w) - y) ** 2) \
+ 10000 * np.sum(np.float32(w < 0)) \
+ np.sum(w ** 2)
self.weights = np.random.randn(np.shape(x)[2])
print "weights size", self.weights.shape
optimizer = CMAES(func, self.weights)
optimizer.minimize = True
self.weights = optimizer.learn()[0]
def optimize_pose(self, marker_id, save_transform=True):
"""
Find optimized transform for marker relative to master tag
:param marker_id: id for marker
:param save_transform: bool, update optimized marker pose dict or not
:return optimized pose
"""
x0 = np.zeros(7)
self.optimize_id = marker_id
l = CMAES(self.objF, x0)
l.minimize = True
l.maxEvaluations = 1000
pose = l.learn()
print marker_id, pose # DEBUG
if save_transform:
self.optimized_marker_poses[marker_id] = pose[0]
return pose[0]
__author__ = 'estsauver' from numpy import array import pylab def objF(x): return sum(x**2) x0 = array([2.1, -1]) from pybrain.optimization import CMAES l=CMAES(objF,x0) l.minimize = True l.maxEvaluations = 200 print l.learn()
