Fixes to kmeans and gmm

• Allows the setting of k-means' oversampling_factor.
• Changes the naming of GMM map parameters.
• Adds a test for GMMMachine.load().
• Applies a threshold to n in ml_gmm_m_step.
• Computes values as float instead of array in GMMStats.

bob/learn/em/test/test_gmm.py

@@ -202,6 +202,7 @@ def test_GMMMachine():
     with tempfile.NamedTemporaryFile(suffix=".hdf5") as f:
         filename = f.name
         gmm.save(HDF5File(filename, "w"))
+        # Using from_hdf5
         gmm1 = GMMMachine.from_hdf5(HDF5File(filename, "r"))
         assert type(gmm1.n_gaussians) is np.int64
         assert type(gmm1.update_means) is np.bool_
@@ -210,6 +211,17 @@ def test_GMMMachine():
         assert type(gmm1.trainer) is str
         assert gmm1.ubm is None
         assert gmm == gmm1
+        # Using load
+        gmm1 = GMMMachine(n_gaussians=gmm.n_gaussians)
+        gmm1.load(HDF5File(filename, "r"))
+        assert type(gmm1.n_gaussians) is np.int64
+        assert type(gmm1.update_means) is np.bool_
+        assert type(gmm1.update_variances) is np.bool_
+        assert type(gmm1.update_weights) is np.bool_
+        assert type(gmm1.trainer) is str
+        assert gmm1.ubm is None
+        assert gmm == gmm1
+
     with tempfile.NamedTemporaryFile(suffix=".hdf5") as f:
         filename = f.name
         gmm.save(filename)
@@ -923,7 +935,7 @@ def test_gmm_MAP_3():
         update_variances=False,
         update_weights=False,
         mean_var_update_threshold=accuracy,
-        relevance_factor=None,
+        map_relevance_factor=None,
     )
     gmm.variance_thresholds = threshold
 
@@ -1071,7 +1083,7 @@ def test_gmm_MAP_dask():
         update_variances=False,
         update_weights=False,
         mean_var_update_threshold=accuracy,
-        relevance_factor=None,
+        map_relevance_factor=None,
     )
     gmm.variance_thresholds = threshold