import tensorflow as tf
import numpy as np
from TTGP.covariance import SE_multidim, BinaryKernel
from TTGP.projectors import LinearProjector, Identity
from TTGP.gpstruct_runner import GPStructRunner
with tf.Graph().as_default():
data_dir = 'data_struct/'
n_inputs = 10
mu_ranks = 10
# D = 6438
# P = np.load('P.npy')
# projector = LinearProjector(D=D, d=5, trainable=True)
D = 5
projector = Identity(D=D)
n_labels = 3
cov = SE_multidim(n_labels, 0.7, 0.2, 0.1, projector)
bin_cov = BinaryKernel(n_labels, alpha=1.)
lr = 5e-4
decay = (10, 0.2)
n_epoch = 30
batch_size = 100
log_dir = None
save_dir = None
model_dir = save_dir
load_model = False
runner = GPStructRunner(data_dir, n_inputs, mu_ranks, cov, bin_cov,
lr=lr, decay=decay, n_epoch=n_epoch, batch_size=batch_size,
noise = 0.1
X, y = make_classification(n_samples=N_samples,
n_features=N_dim,
n_classes=2,
n_clusters_per_class=2,
n_informative=N_dim,
n_redundant=0)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
batch_size = 100
n_epochs = 111
y_p, m = 0, 0
with tf.Graph().as_default():
data_dir = "data_class/"
n_inputs = 30
mu_ranks = 10
projector = Identity(D=N_dim)
C = 2
cov = SE_multidim(C, 0.7, 1.0, 0.1, projector)
# cov = SE(0.7, 0.2, 0.1,projector)
lr = 1e-2
runner = GPCRunner(n_inputs,
mu_ranks,
cov,
X=X,
X_test=X_test,
y=y.reshape(-1, 1),
y_test=y_test,
lr=lr,
n_epoch=n_epochs,
batch_size=batch_size,
batch_test=False)