fix docstrings, thanks @fonnesbeck

Author: ferrine

pymc3/data.py

@@ -124,8 +124,8 @@ class Minibatch(tt.TensorVariable):
 
     if we want 1d slice of size 10 we do
     >>> x = Minibatch(data, batch_size=10)
-    
-    Note, that your data is casted to `floatX` if it is not integer type
+
+    Note, that your data is cast to `floatX` if it is not integer type
     But you still can add dtype kwarg for :class:`Minibatch` 
 
     in case we want 10 sampled rows and columns
@@ -158,15 +158,15 @@ class Minibatch(tt.TensorVariable):
     it directly affects `x.shared`.
     the same thing would be but less convenient
     >>> x.shared.set_value(pm.floatX(np.random.laplace(size=(100, 100))))
-    
-    programmatic way to change storage is as following
+
+    programmatic way to change storage is as follows
     I import `partial` for simplicity
     >>> from functools import partial
     >>> datagen = partial(np.random.laplace, size=(100, 100))
     >>> x = Minibatch(datagen(), batch_size=100, update_shared_f=datagen)
     >>> x.update_shared()
 
-    To be more precise of how we get minibatch, here is a demo
+    To be more concrete about how we get minibatch, here is a demo
     1) create shared variable 
     >>> shared = theano.shared(data)
 
@@ -179,11 +179,16 @@ class Minibatch(tt.TensorVariable):
     That's done. So if you'll need some replacements in the graph 
     >>> testdata = pm.floatX(np.random.laplace(size=(1000, 10)))
 
-    you are free to use a kind of this one as `x` is regular Theano Tensor
+    To change minibatch with static data you can create a dict with replacements
     >>> replacements = {x: testdata}
     >>> node = x ** 2  # arbitrary expressions
     >>> rnode = theano.clone(node, replacements)
     >>> assert (testdata ** 2 == rnode.eval()).all()
+    
+    To replace minibatch with it's shared variable 
+    instead of static `np.array` you should do
+    >>> replacements = {x.minibatch: x.shared}
+    >>> rnode = theano.clone(node, replacements)
 
     For more complex slices some more code is needed that can seem not so clear
     They are
@@ -205,7 +210,6 @@ class Minibatch(tt.TensorVariable):
     3) mixing that all, total_size = (10, None, 30, Ellipsis, 50)
     >>> x = Minibatch(moredata, [2, None, 20, Ellipsis, 10])
     >>> assert x.eval().shape == (2, 20, 20, 40, 10)
-
     """
     @theano.configparser.change_flags(compute_test_value='raise')
     def __init__(self, data, batch_size=128, in_memory_size=None,