resolve conflicts

server/analysis/ddpg/ddpg.py

@@ -3,7 +3,7 @@
 #
 # Copyright (c) 2017-18, Carnegie Mellon University Database Group
 #
-# from: https://github.com/KqSMea8/use_default
+# from: https://github.com/KqSMea8/CDBTune
 # Zhang, Ji, et al. "An end-to-end automatic cloud database tuning system using
 # deep reinforcement learning." Proceedings of the 2019 International Conference
 # on Management of Data. ACM, 2019

server/website/website/settings/constants.py

@@ -52,11 +52,11 @@ DEFAULT_LEARNING_RATE = 0.01
 #  a small bias when using training data points as starting points.
 GPR_EPS = 0.001
 
-DEFAULT_RIDGE = 0.01
+DEFAULT_RIDGE = 1.00
 
 DEFAULT_EPSILON = 1e-6
 
-DEFAULT_SIGMA_MULTIPLIER = 3.0
+DEFAULT_SIGMA_MULTIPLIER = 1.0
 
 DEFAULT_MU_MULTIPLIER = 1.0
 

server/website/website/tasks/async_tasks.py

@@ -670,8 +670,9 @@ def configuration_recommendation(recommendation_input):
                           epsilon=DEFAULT_EPSILON,
                           max_iter=MAX_ITER,
                           sigma_multiplier=DEFAULT_SIGMA_MULTIPLIER,
-                          mu_multiplier=DEFAULT_MU_MULTIPLIER)
-            model.fit(X_scaled, y_scaled, X_min, X_max, ridge=DEFAULT_RIDGE)
+                          mu_multiplier=DEFAULT_MU_MULTIPLIER,
+                          ridge=DEFAULT_RIDGE)
+            model.fit(X_scaled, y_scaled, X_min, X_max)
             res = model.predict(X_samples, constraint_helper=constraint_helper)
 
     best_config_idx = np.argmin(res.minl.ravel())