Fix naming

mlpp_lib/probabilistic_layers.py

@@ -810,7 +810,7 @@ def __init__(
         validate_args=False,
         **kwargs
     ):
-        """Initialize the `MixtureOf2Normal` layer.
+        """Initialize the `MixtureTruncatedNormal` layer.
         Args:
             event_shape: integer vector `Tensor` representing the shape of single
                 draw from this distribution.
@@ -833,8 +833,8 @@ def __init__(
         # positional argument.
         kwargs.pop("make_distribution_fn", None)
 
-        super(MixtureOf2Normal, self).__init__(
-            lambda t: MixtureOf2Normal.new(t, event_shape, validate_args),
+        super(MixtureTruncatedNormal, self).__init__(
+            lambda t: MixtureTruncatedNormal.new(t, event_shape, validate_args),
             convert_to_tensor_fn,
             **kwargs
         )
@@ -846,7 +846,7 @@ def __init__(
     @staticmethod
     def new(params, event_shape=(), validate_args=False, name=None):
         """Create the distribution instance from a `params` vector."""
-        with tf.name_scope(name or "MixtureOf2Normal"):
+        with tf.name_scope(name or "MixtureTruncatedNormal"):
             params = tf.convert_to_tensor(params, name="params")
 
             event_shape = dist_util.expand_to_vector(
@@ -928,12 +928,12 @@ def _mean(self):
     @staticmethod
     def params_size(event_shape=(), name=None):
         """The number of `params` needed to create a single distribution."""
-        with tf.name_scope(name or "MixtureOf2Normal_params_size"):
+        with tf.name_scope(name or "MixtureTruncatedNormal_params_size"):
             event_shape = tf.convert_to_tensor(
                 event_shape, name="event_shape", dtype_hint=tf.int32
             )
             return np.int32(5) * _event_size(
-                event_shape, name=name or "MixtureOf2Normal_params_size"
+                event_shape, name=name or "MixtureTruncatedNormal_params_size"
             )
 
     def get_config(self):
@@ -952,13 +952,13 @@ def get_config(self):
             "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
             "validate_args": self._validate_args,
         }
-        base_config = super(MixtureOf2Normal, self).get_config()
+        base_config = super(MixtureTruncatedNormal, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
 
     @property
     def output(self):
         """This allows the use of this layer with the shap package."""
-        return super(MixtureOf2Normal, self).output[0]
+        return super(MixtureTruncatedNormal, self).output[0]
 
 
 @tf.keras.saving.register_keras_serializable()