def setUpClass(cls):
super(TestSimpleFull, cls).setUpClass()
likelihood = lik.Gaussian(1.0)
kernel = cov.SquaredExponential(input_dim=1,
length_scale=[1.0],
std_dev=[1.0],
white=[0.0])
# In most of our unit test, we will replace this value with something else.
inducing_inputs = np.array([[1.0]])
cls.inf = inf.VariationalInference(num_samples=10,
lik_func=likelihood,
cov_func=kernel,
num_components=1)
cls.model = autogp.GaussianProcess(lik_func=likelihood,
cov_func=kernel,
inducing_inputs=inducing_inputs,
num_components=1,
diag_post=False,
inf_func=cls.inf)
cls.session.run(tf.global_variables_initializer())
def setUpClass(cls):
super(TestMultiFull, cls).setUpClass()
likelihood = lik.Softmax()
kernel = cov.SquaredExponential(input_dim=2,
output_dim=2,
length_scale=1.,
std_dev=1.,
white=0.)
inducing_inputs = np.array([[1.0, 2.0, 3.0, 4.0]])
cls.inf = inf.VariationalInference(num_samples=10,
lik_func=likelihood,
cov_func=kernel,
num_components=2)
cls.model = autogp.GaussianProcess(lik_func=likelihood,
cov_func=kernel,
inducing_inputs=inducing_inputs,
num_components=2,
diag_post=False,
inf_func=cls.inf)
cls.session.run(tf.global_variables_initializer())
if os.path.exists(
TRAIN_PATH
) is False: # directory does not exist, download the data
get_sarcos_data()
d = sarcos_all_joints_data()
data = datasets.DataSet(d['train_inputs'].astype(np.float32),
d['train_outputs'].astype(np.float32))
test = datasets.DataSet(d['test_inputs'].astype(np.float32),
d['test_outputs'].astype(np.float32))
# Setup initial values for the model.
likelihood = lik.RegressionNetwork(7, 0.1)
kern = [
cov.SquaredExponential(data.X.shape[1],
length_scale=8.0,
input_scaling=IS_ARD) for i in range(8)
]
# kern = [kernels.ArcCosine(data.X.shape[1], 1, 3, 5.0, 1.0, input_scaling=True) for i in range(10)]
Z = init_z(data.X, NUM_INDUCING)
m = autogp.GaussianProcess(likelihood, kern, Z, num_samples=NUM_SAMPLES)
# setting up loss to be reported during training
error_rate = None # losses.StandardizedMeanSqError(d['train_outputs'].astype(np.float32), data.Dout)
import time
o = tf.train.RMSPropOptimizer(LEARNING_RATE)
start = time.time()
m.fit(data,
o,
Z = init_z(data.X, NUM_INDUCING)
likelihood = lik.Logistic() # Setup initial values for the model.
if KERNEL == 'arccosine':
kern = [
cov.ArcCosine(data.X.shape[1],
degree=DEGREE,
depth=DEPTH,
lengthscale=LENGTHSCALE,
std_dev=1.0,
input_scaling=IS_ARD) for i in range(1)
]
else:
kern = [
cov.SquaredExponential(data.X.shape[1],
length_scale=LENGTHSCALE,
input_scaling=IS_ARD) for i in range(1)
]
print(f"Using Kernel {KERNEL}")
m = autogp.GaussianProcess(likelihood,
kern,
Z,
num_samples=NUM_SAMPLES,
num_components=NUM_COMPONENTS)
error_rate = losses.ZeroOneLoss(data.Dout)
o = tf.train.AdamOptimizer(LEARNING_RATE)
m.fit(data,
o,
loo_steps=LOOCV_STEPS,
FLAGS = util.util.get_flags()
BATCH_SIZE = FLAGS.batch_size
LEARNING_RATE = FLAGS.learning_rate
DISPLAY_STEP = FLAGS.display_step
EPOCHS = FLAGS.n_epochs
NUM_SAMPLES = FLAGS.mc_train
NUM_INDUCING = FLAGS.n_inducing
IS_ARD = FLAGS.is_ard
data, test, _ = datasets.import_mnist()
# Setup initial values for the model.
likelihood = lik.Softmax()
kern = cov.SquaredExponential(data.X.shape[1],
output_dim=10,
length_scale=10,
std_dev=1,
white=.01,
input_scaling=IS_ARD)
# kern = [kernels.ArcCosine(X.shape[1], 2, 3, 5.0, 1.0, input_scaling=True) for i in range(10)]
# RadialBasis(X.shape[1], input_scaling=True) for i in xrange(10)]
Z = init_z(data.X, NUM_INDUCING)
inf = inf.VariationalInference(kern, likelihood, num_samples=NUM_SAMPLES)
m = autogp.GaussianProcess(Z, kern, inf, likelihood)
# setting up loss to be reported during training
error_rate = losses.ZeroOneLoss(data.Dout)
import time
otime = time.time()
o = tf.train.RMSPropOptimizer(LEARNING_RATE)
FLAGS = util.util.get_flags()
BATCH_SIZE = FLAGS.batch_size
LEARNING_RATE = FLAGS.learning_rate
DISPLAY_STEP = FLAGS.display_step
EPOCHS = FLAGS.n_epochs
NUM_SAMPLES = FLAGS.mc_train
NUM_INDUCING = FLAGS.n_inducing
IS_ARD = FLAGS.is_ard
data, test = import_mnist()
# Setup initial values for the model.
likelihood = lik.Softmax()
kern = cov.SquaredExponential(data.X.shape[1],
length_scale=[10] * 10,
std_dev=[1] * 10,
white=[.01] * 10,
input_scaling=IS_ARD)
# kern = [kernels.ArcCosine(X.shape[1], 2, 3, 5.0, 1.0, input_scaling=True) for i in range(10)]
#RadialBasis(X.shape[1], input_scaling=True) for i in range(10)]
Z = init_z(data.X, NUM_INDUCING)
m = autogp.GaussianProcess(likelihood, kern, Z, num_samples=NUM_SAMPLES)
# setting up loss to be reported during training
error_rate = losses.ZeroOneLoss(data.Dout)
o = tf.train.RMSPropOptimizer(LEARNING_RATE)
m.fit(data,
o,
loo_steps=50,