Started with PreferenceExpectedImprovement.py

# Conflicts:
#	AcquistionFunctions/PreferenceExpectedImprovement.py

AcquistionFunctions/PreferenceExpectedImprovement.py

@@ -43,20 +43,21 @@ class PreferenceExpectedImprovement:
         pass
 
     def update_user_preference_model(self, preferred_input, preference_array):
-    # Update mean to reflect preferred input
-    self.user_model_mean = preferred_input
+        # Update mean to reflect preferred input
+        self.user_model_mean = preferred_input
 
-    initial_variance = np.ones((self.nr_dims, )) * self.initial_variance
-    reduced_variance = initial_variance / 10.0
-    variances = np.where(preference_array, reduced_variance, initial_variance)
-    self.user_model_covariance = np.diag(variances)
+        initial_variance = np.ones((self.nr_dims, )) * self.initial_variance
+        reduced_variance = initial_variance / 10.0
+        variances = np.where(preference_array, reduced_variance, initial_variance)
+        self.user_model_covariance = np.diag(variances)
 
     def update_proposal_model(self, alpha=0.5):
-    # Update proposal model to be a weighted average of the current proposal model and the user model
-    self.proposal_model_mean = alpha * self.proposal_model_mean + (1 - alpha) * self.user_model_mean
-    self.proposal_model_covariance = alpha * self.proposal_model_covariance + (1 - alpha) * self.user_model_covariance
+        # Update proposal model to be a weighted average of the current proposal model and the user model
+        self.proposal_model_mean = alpha * self.proposal_model_mean + (1 - alpha) * self.user_model_mean
+        self.proposal_model_covariance = alpha * self.proposal_model_covariance + (1 - alpha) * self.user_model_covariance
 
 
 if __name__ == '__main__':
     acquisition = PreferenceExpectedImprovement(10, 2, -1.0, 1.0)
 
+