Fixed a number of typos in doc reported by eneftci

doc/library/scan.txt

@@ -295,25 +295,26 @@ the following:
 
 .. code-block:: python
 
- W = theano.shared ( W_values ) # we assume that ``W_values`` contains the
+ W = theano.shared(W_values) # we assume that ``W_values`` contains the
                              # initial values of your weight matrix
 
- bvis = theano.shared( bvis_values)
- bhid = theano.shared( bhid_values)
+ bvis = theano.shared(bvis_values)
+ bhid = theano.shared(bhid_values)
 
  trng = T.shared_randomstreams.RandomStreams(1234)
 
- def OneStep( vsample) :
-    hmean   = T.nnet.sigmoid( theano.dot( vsample, W) + bhid)
-    hsample = trng.binomial( size = hmean.shape, n = 1, prob = hmean)
-    vmean   = T.nnet.sigmoid( theano.dot( hsample. W.T) + bvis)
-    return trng.binomial( size = vsample.shape, n = 1, prob = vsample)
+ def OneStep(vsample) :
+    hmean = T.nnet.sigmoid(theano.dot(vsample, W) + bhid)
+    hsample = trng.binomial(size=hmean.shape, n=1, p=hmean)
+    vmean = T.nnet.sigmoid(theano.dot(hsample, W.T) + bvis)
+    return trng.binomial(size=vsample.shape, n=1, p=vmean,
+                         dtype=theano.config.floatX)
 
  sample = theano.tensor.vector()
 
- values, updates = theano.scan( OneStep, outputs_info = sample, n_steps = 10 )
+ values, updates = theano.scan(OneStep, outputs_info=sample, n_steps=10)
 
- gibbs10 = theano.function([sample], values[-1], updates = updates)
+ gibbs10 = theano.function([sample], values[-1], updates=updates)
 
 
 Note that if we use shared variables ( ``W``, ``bvis``, ``bhid``) but
@@ -335,7 +336,7 @@ afterwards. Look at this example :
 .. code-block:: python
 
  a = theano.shared(1)
- values,updates = theano.scan( lambda : {a:a+1}, n_steps = 10 )
+ values, updates = theano.scan(lambda: {a: a+1}, n_steps=10)
 
 In this case the lambda expression does not require any input parameters
 and returns an update dictionary which tells how ``a`` should be updated
@@ -343,9 +344,9 @@ after each step of scan. If we write :
 
 .. code-block:: python
 
-  b = a+1
+  b = a + 1
   c = updates[a] + 1
-  f = theano.function([], [b,c], updates = updates)
+  f = theano.function([], [b, c], updates=updates)
 
   print b
   print c

theano/scan_module/scan_op.py

@@ -228,7 +228,7 @@ class Scan(PureOp):
                    )
         err_msg2 = ('When compiling the inner function of scan the '
                     'following error has been encountered: The '
-                    'initial state (outputs_info in scan nomenclature)'
+                    'initial state (outputs_info in scan nomenclature) '
                     'of variable %s (argument number %d)'
                     ' has dtype %s and %d dimension(s), while the result '
                     'of the inner function for this output has dtype %s '