Added name

bob/learn/em/factor_analysis.py

@@ -1072,16 +1072,11 @@ class FactorAnalysisBase(BaseEstimator):
 
         UcT = np.transpose(Uc, axes=(0, 2, 1))
 
-        sigma_c = np.reshape(
-            self.variance_supervector,
-            (self.ubm.n_gaussians, self.feature_dimension),
-        )
+        sigma_c = self.ubm.variances[:, np.newaxis]
 
         n_i_c = np.expand_dims(X_i.n[:, np.newaxis], axis=2)
 
-        id_plus_us_prod_inv = I + (
-            ((UcT / sigma_c[:, np.newaxis]) @ Uc) * n_i_c
-        ).sum(axis=0)
+        id_plus_us_prod_inv = I + (((UcT / sigma_c) @ Uc) * n_i_c).sum(axis=0)
         id_plus_us_prod_inv = np.linalg.inv(id_plus_us_prod_inv)
 
         return id_plus_us_prod_inv
@@ -1135,7 +1130,7 @@ class ISVMachine(FactorAnalysisBase):
 
     """
 
-    def __init__(self, ubm, r_U, em_iterations, relevance_factor=4.0, seed=0):
+    def __init__(self, ubm, r_U, em_iterations=10, relevance_factor=4.0, seed=0):
         super(ISVMachine, self).__init__(
             ubm,
             r_U=r_U,
@@ -1320,7 +1315,7 @@ class JFAMachine(FactorAnalysisBase):
     """
 
     def __init__(
-        self, ubm, r_U, r_V, em_iterations, relevance_factor=4.0, seed=0
+        self, ubm, r_U, r_V, em_iterations=10, relevance_factor=4.0, seed=0
     ):
         super(JFAMachine, self).__init__(
             ubm,

bob/learn/em/wccn.py

+#!/usr/bin/env python
+# @author: Tiago de Freitas Pereira
+
 import dask
 
 # Dask doesn't have an implementation for `pinv`

bob/learn/em/whitening.py

+#!/usr/bin/env python
+# @author: Tiago de Freitas Pereira
+
 import dask
 
 from scipy.linalg import pinv