def init_spectral(x, y, M, Q, kern, n_inits=10, minibatch_size=256, noise_var=10.0, ARD=True, likelihood=None):
print('Initializing a spectral kernel...')
best_loglik = -np.inf
best_m = None
N, input_dim = x.shape
for k in range(n_inits):
try:
#gpflow.reset_default_graph_and_session()
with gpflow.defer_build():
Z = random_Z(x, N, M)
dists = pdist(Z, 'euclidean').ravel()
max_freq = min(10.0, 1./np.min(dists[dists > 0.0]))
max_len = min(5.0, np.max(dists) * (2*np.pi))
k = kern(input_dim=input_dim, max_freq=max_freq, Q=Q, ARD=ARD, max_len=max_len)
if likelihood is not None:
likhood = likelihood
else:
likhood = gpflow.likelihoods.Gaussian(noise_var)
likhood.variance.prior = gpflow.priors.LogNormal(mu=0, var=1)
model = SVGP(X=x, Y=y, Z=Z, kern=k, likelihood=likhood,
minibatch_size=minibatch_size)
model.feature.Z.prior = gpflow.priors.Gaussian(0, 1)
model.compile()
loglik = model.compute_log_likelihood()
if loglik > best_loglik:
best_loglik = loglik
best_m = model
#best_dir = tempfile.TemporaryDirectory()
#gpflow.saver.Saver().save(best_dir.name + 'model.gpflow', best_m)
del model
gc.collect()
except tf.errors.InvalidArgumentError: # cholesky fails sometimes (with really bad init?)
pass
print('Best init: %f' % best_loglik)
print(best_m)
#gpflow.reset_default_graph_and_session()
#best_m = gpflow.saver.Saver().load(best_dir.name + 'model.gpflow')
#best_m.compile()
#print(best_m)
return best_m
def init_neural(x, y, M, Q, n_inits=1, minibatch_size=256, noise_var=0.1, likelihood=None, hidden_sizes=None):
print('Initializing neural spectral kernel...')
best_loglik = -np.inf
best_m = None
N, input_dim = x.shape
for k in range(n_inits):
try:
# gpflow.reset_default_graph_and_session()
with gpflow.defer_build():
Z = random_Z(x, N, M)
k = NeuralSpectralKernel(input_dim=input_dim, Q=Q, hidden_sizes=hidden_sizes)
if likelihood is not None:
likhood = likelihood
else:
likhood = gpflow.likelihoods.Gaussian(noise_var)
likhood.variance.prior = gpflow.priors.LogNormal(mu=0, var=1)
model = SVGP(X=x, Y=y, Z=Z, kern=k, likelihood=likhood,
minibatch_size=minibatch_size)
model.feature.Z.prior = gpflow.priors.Gaussian(0, 1)
model.compile()
loglik = model.compute_log_likelihood()
if loglik > best_loglik:
best_loglik = loglik
best_m = model
# best_dir = tempfile.TemporaryDirectory()
# gpflow.saver.Saver().save(best_dir.name + 'model.gpflow', best_m)
del model
gc.collect()
except tf.errors.InvalidArgumentError: # cholesky fails sometimes (with really bad init?)
pass
print('Best init: %f' % best_loglik)
print(best_m)
# gpflow.reset_default_graph_and_session()
# best_m = gpflow.saver.Saver().load(best_dir.name + 'model.gpflow')
# best_m.compile()
# print(best_m)
return best_m
likelihood = gp.likelihoods.Gaussian()
# Define the underlying GP mean and kernel
mean = gp.mean_functions.Zero()
kernel = gp.kernels.RBF(1)
# Create the HGP (note the slightly different order from SVGP)
model = SVGP(X,
Y,
kernel,
likelihood,
mean_function=mean,
minibatch_size=100,
num_latent=1,
num_data=None,
whiten=False,
Z=Z)
model.compile()
run_with_adam(model, 1e-3, iterations, PrintAction(model, "Adam"))
# Predictions uses stochastic sampling and produces
# [num_samples,N,D] shape output
ystar, varstar = model.predict_y(X)
# In[5]:
plt.figure(figsize=(4, 4))
plt.plot(X[:, 0], ystar, alpha=1, c='r', label='vanilla-inferred')
plt.fill_between(X[:, 0],
np.squeeze(ystar + np.sqrt(varstar)),
np.squeeze(ystar - np.sqrt(varstar)),
alpha=0.5)
plt.plot(X[:, 0], Y[:, 0], c='b', alpha=0.5, label='data')
