def rui_1d():
from kernels import SquaredExponential
from gaussian_process import GaussianProcess
import matplotlib.pyplot as plt
batch_size = 4
new_samples = 1000
n_dim = 1
# Set up the modules for bayesian optimizer
kernel = SquaredExponential(n_dim=n_dim,
init_scale_range=(.1, .5),
init_amp=1.)
gp = GaussianProcess(n_epochs=100,
batch_size=10,
n_dim=n_dim,
kernel=kernel,
noise=0.05,
train_noise=False,
optimizer=tf.train.GradientDescentOptimizer(0.001),
verbose=0)
bo = BayesianOptimizer(gp,
region=np.array([[0., 1.]]),
iters=100,
tries=20,
optimizer=tf.train.GradientDescentOptimizer(0.1),
verbose=1)
X = np.array([1.0, 0.000335462627903, 0.0314978449076,
2980.95798704]).reshape(-1, 1)
X = np.log(X) / 8
y = np.array([0.864695262443, 0.5, 0.860244469176,
0.862691649896]).reshape(-1, 1)
X_old = X
y_old = y
bo.fit(X, y)
x_next, y, z = bo.select()
x = np.linspace(-1, 1).reshape(-1, 1)
y_pred, var = gp.np_predict(x)
ci = 2 * np.sqrt(var)
ci = np.sqrt(var) * 2
plt.plot(x, y_pred)
plt.plot(x, y_pred + ci, 'g--')
plt.plot(x, y_pred - ci, 'g--')
plt.scatter(X_old, y_old)
plt.plot([x_next[0, 0], x_next[0, 0]], plt.ylim(), 'r--')
plt.show()
def main_1d():
from kernels import SquaredExponential
from gaussian_process import GaussianProcess
import matplotlib.pyplot as plt
import time
np.random.seed(10)
tf.set_random_seed(10)
# Settings
n_samples = 3
batch_size = 4
new_samples = 1000
n_dim = 1
# Set up the modules for bayesian optimizer
kernel = SquaredExponential(n_dim=n_dim,
init_scale_range=(.1, .5),
init_amp=1.)
gp = GaussianProcess(n_epochs=100,
batch_size=10,
n_dim=n_dim,
kernel=kernel,
noise=0.01,
train_noise=False,
optimizer=tf.train.GradientDescentOptimizer(0.001),
verbose=0)
bo = BayesianOptimizer(gp,
region=np.array([[0., 1.]]),
iters=100,
tries=2,
optimizer=tf.train.GradientDescentOptimizer(0.1),
verbose=1)
# Define the latent function + noise
def observe(X):
y = np.float32(1 * (-(X - 0.5)**2 + 1) +
np.random.normal(0, .1, [X.shape[0], 1]))
# y = np.float32((np.sin(X.sum(1)).reshape([X.shape[0], 1]) +
# np.random.normal(0,.1, [X.shape[0], 1])))
return y
# Get data
X = np.float32(np.random.uniform(0, 1, [n_samples, n_dim]))
y = observe(X)
plt.axis((-0.1, 1.1, 0, 1.5))
# Fit the gp
bo.fit(X, y)
for i in xrange(5):
# print "Iteration {0:3d}".format(i) + "*"*80
t0 = time.time()
max_acq = -np.inf
# Inner loop to allow for gd with random initializations multiple times
x_next, y_next, acq_next = bo.select()
# Plot the selected point
plt.plot([x_next[0, 0], x_next[0, 0]], plt.ylim(), 'r--')
plt.scatter(x_next, y_next, c='r', linewidths=0, s=50)
plt.scatter(x_next, acq_next, c='g', linewidths=0, s=50)
# Observe and add point to observed data
y_obs = observe(x_next)
X = np.vstack((X, x_next))
y = np.vstack((y, y_obs))
t2 = time.time()
# Fit again
bo.fit(X, y)
print "BOFitDuration: {0:.5f}".format(time.time() - t2)
print "BOTotalDuration: {0:.5f}".format(time.time() - t0)
# Get the final posterior mean and variance for the entire domain space
X_new = np.float32(np.linspace(0, 1, new_samples).reshape(-1, 1))
X_new = np.sort(X_new, axis=0)
y_pred, var = gp.np_predict(X_new)
# Compute the confidence interval
ci = np.sqrt(var) * 2
plt.plot(X_new, y_pred)
plt.plot(X_new, y_pred + ci, 'g--')
plt.plot(X_new, y_pred - ci, 'g--')
plt.scatter(X, y)
plt.show()
