create the UBM at init

bob/bio/gmm/algorithm/GMM.py

@@ -139,7 +139,25 @@ class GMM(BioAlgorithm, BaseEstimator):
 
         self.scoring_function = scoring_function
 
-        self.ubm = None
+        self.ubm = GMMMachine(
+            n_gaussians=self.number_of_gaussians,
+            trainer="ml",
+            max_fitting_steps=self.ubm_training_iterations,
+            convergence_threshold=self.training_threshold,
+            update_means=self.update_means,
+            update_variances=self.update_variances,
+            update_weights=self.update_weights,
+            mean_var_update_threshold=self.variance_threshold,
+            k_means_trainer=KMeansMachine(
+                self.number_of_gaussians,
+                convergence_threshold=self.training_threshold,
+                max_iter=self.kmeans_training_iterations,
+                init_method="k-means||",
+                init_max_iter=self.kmeans_init_iterations,
+                random_state=self.init_seed,
+                oversampling_factor=self.kmeans_oversampling_factor,
+            ),
+        )
 
     def save_model(self, ubm_file):
         """Saves the projector (UBM) to file."""
@@ -277,26 +295,6 @@ class GMM(BioAlgorithm, BaseEstimator):
             f"Creating UBM machine with {self.number_of_gaussians} gaussians and {len(array)} samples"
         )
 
-        self.ubm = GMMMachine(
-            n_gaussians=self.number_of_gaussians,
-            trainer="ml",
-            max_fitting_steps=self.ubm_training_iterations,
-            convergence_threshold=self.training_threshold,
-            update_means=self.update_means,
-            update_variances=self.update_variances,
-            update_weights=self.update_weights,
-            mean_var_update_threshold=self.variance_threshold,
-            k_means_trainer=KMeansMachine(
-                self.number_of_gaussians,
-                convergence_threshold=self.training_threshold,
-                max_iter=self.kmeans_training_iterations,
-                init_method="k-means||",
-                init_max_iter=self.kmeans_init_iterations,
-                random_state=self.init_seed,
-                oversampling_factor=self.kmeans_oversampling_factor,
-            ),
-        )
-
         # Train the GMM
         logger.info("Training UBM GMM")