found typo

pymc3/theanof.py

@@ -356,22 +356,7 @@ def generator(gen, default=None):
     return GeneratorOp(gen, default)()
 
 
-@change_flags(compute_test_value='off')
-def launch_rng(rng):
-    """
-    Helper function for safe launch of theano random generator.
-    If not launched, there will be problems with test_value
-
-    Parameters
-    ----------
-    rng : `theano.sandbox.rng_mrg.MRG_RandomStreams` instance
-    """
-    state = rng.rstate
-    rng.inc_rstate()
-    rng.set_rstate(state)
-
 _tt_rng = MRG_RandomStreams()
-launch_rng(_tt_rng)
 
 
 def tt_rng(random_seed=None):
@@ -380,7 +365,7 @@ def tt_rng(random_seed=None):
 
     Parameters
     ----------
-    seed : int
+    random_seed : int
         If not None
         returns *new* theano random generator without replacing package global one
 
@@ -394,7 +379,6 @@ def tt_rng(random_seed=None):
         return _tt_rng
     else:
         ret = MRG_RandomStreams(random_seed)
-        launch_rng(ret)
         return ret
 
 
@@ -413,7 +397,6 @@ def set_tt_rng(new_rng):
     if isinstance(new_rng, int):
         new_rng = MRG_RandomStreams(new_rng)
     _tt_rng = new_rng
-    launch_rng(_tt_rng)
 
 
 def floatX_array(x):

pymc3/variational/approximations.py

@@ -40,7 +40,7 @@ class MeanField(Approximation):
         Yuhuai Wu, David Duvenaud, 2016) for details
     scale_cost_to_minibatch : `bool` 
         Scale cost to minibatch instead of full dataset, default False
-    seed : None or int
+    random  seed : None or int
         leave None to use package global RandomStream or other
         valid value to create instance specific one
 
@@ -263,7 +263,7 @@ class Empirical(Approximation):
         Scale cost to minibatch instead of full dataset, default False
     model : :class:`Model` 
         PyMC3 model for inference
-    seed : None or int
+    random_seed : None or int
         leave None to use package global RandomStream or other
         valid value to create instance specific one
 
@@ -375,7 +375,7 @@ def from_noise(cls, size, jitter=.01, local_rv=None,
             initial point
         model : :class:`Model`
             PyMC3 model for inference
-        seed : None or int
+        random_seed : None or int
             leave None to use package global RandomStream or other
             valid value to create instance specific one
         kwargs : other kwargs passed to init

pymc3/variational/inference.py

@@ -312,7 +312,7 @@ class ADVI(Inference):
         Yuhuai Wu, David Duvenaud, 2016) for details
     scale_cost_to_minibatch : `bool`
         Scale cost to minibatch instead of full dataset, default False
-    seed : None or int
+    random_seed : None or int
         leave None to use package global RandomStream or other
         valid value to create instance specific one    
     start : `Point`
@@ -560,19 +560,19 @@ def fit(n=10000, local_rv=None, method='advi', model=None, random_seed=None, sta
         Local Vars are used for Autoencoding Variational Bayes
         See (AEVB; Kingma and Welling, 2014) for details
     method : str or :class:`Inference`
-        string name is case insensitive in {'advi', 'fullrank_advi', 'advi->fullrank_advi'}
+        string name is case insensitive in {'advi', 'fullrank_advi', 'advi->fullrank_advi', 'svgd'}
     model : :class:`Model`
         PyMC3 model for inference
-
-    Other Parameters
-    ----------------
-    frac : `float`
-        if method is 'advi->fullrank_advi' represents advi fraction when training
     random_seed : None or int
         leave None to use package global RandomStream or other
         valid value to create instance specific one
     start : `Point`
         starting point for inference
+
+    Other Parameters
+    ----------------
+    frac : `float`
+        if method is 'advi->fullrank_advi' represents advi fraction when training
     kwargs : kwargs for :method:`Inference.fit`
 
     Returns

pymc3/variational/opvi.py

@@ -562,10 +562,10 @@ def seed(self, random_seed=None):
 
         Parameters
         ----------
-        seed : `int`
+        random_seed : `int`
         """
         self._seed = random_seed
-        self._rng.seed(seed)
+        self._rng.seed(random_seed)
 
     @property
     def normalizing_constant(self):