accelerate simulation; scale beta

server/analysis/gpr/gpr_models.py

@@ -109,7 +109,7 @@ class BasicGP(BaseModel):
         return [
             {
                 'input_dim': X_dim,
-                'ARD': True
+                'ARD': False
             },
             {
                 'input_dim': X_dim,

server/analysis/gpr/optimize.py

@@ -45,7 +45,7 @@ def tf_optimize(model, Xnew_arr, learning_rate=0.01, maxiter=100, ucb_beta=3.,
     beta_t = tf.constant(ucb_beta, name='ucb_beta', dtype=settings.float_type)
     y_mean_var = model.likelihood.predict_mean_and_var(*model._build_predict(Xin))
     loss = tf.subtract(y_mean_var[0], tf.multiply(beta_t, y_mean_var[1]), name='loss_fn')
-    opt = tf.train.AdamOptimizer(learning_rate)
+    opt = tf.train.AdamOptimizer(learning_rate, epsilon=1e-6)
     train_op = opt.minimize(loss)
     variables = opt.variables()
     init_op = tf.variables_initializer([Xnew] + variables)

server/analysis/gpr/ucb.py

@@ -20,11 +20,12 @@ _UCB_MAP = {
 }
 
 
-def get_ucb_beta(ucb_beta, **kwargs):
+def get_ucb_beta(ucb_beta, scale = 1., **kwargs):
     check_valid(ucb_beta)
     if not isinstance(ucb_beta, float):
         ucb_beta = _UCB_MAP[ucb_beta](**kwargs)
     assert isinstance(ucb_beta, float), type(ucb_beta)
+    ucb_beta *= scale
     assert ucb_beta >= 0.0
     return ucb_beta