Merge pull request #205 from relf/master

Fix MFKPLSK bug

smt/__init__.py

@@ -1 +1 @@
-__version__ = "0.5.2"
+__version__ = "0.5.3"

smt/applications/tests/test_mfkplsk.py

@@ -67,6 +67,10 @@ def test_mfkplsk(self):
  sm.options["xlimits"] = prob.xlimits
  sm.options["print_global"] = False
 
+ # to test some options
+ sm.options["eval_noise"] = True
+ sm.options["optim_var"] = True
+
  # modif MM
  sm.options["n_comp"] = self.n_comp
  sm.options["theta0"] = [1e-2] * self.n_comp

smt/surrogate_models/krg_based.py

@@ -519,18 +519,21 @@ def minus_reduced_likelihood_function(log10t):
  print("fmin_cobyla failed but the best value is retained")
 
  if "KPLSK" in self.name:
+ if self.name == "MFKPLSK" and self.options["eval_noise"]:
+ # best_optimal_theta contains [theta, noise] if eval_noise = True
+ theta = best_optimal_theta[:-1]
+ else:
+ # best_optimal_theta contains [theta] if eval_noise = False
+ theta = best_optimal_theta
 
  if exit_function:
  return best_optimal_rlf_value, best_optimal_par, best_optimal_theta
 
  if self.options["corr"] == "squar_exp":
- self.options["theta0"] = (
- best_optimal_theta * self.coeff_pls ** 2
- ).sum(1)
+ self.options["theta0"] = (theta * self.coeff_pls ** 2).sum(1)
  else:
- self.options["theta0"] = (
- best_optimal_theta * np.abs(self.coeff_pls)
- ).sum(1)
+ self.options["theta0"] = (theta * np.abs(self.coeff_pls)).sum(1)
+
  self.options["n_comp"] = int(self.nx)
  limit = 10 * self.options["n_comp"]
  self.best_iteration_fail = None