corrected typo. removed code duplication.

gensim/models/normmodel.py

@@ -30,7 +30,7 @@ class Normmodel(interfaces.TransformationABC):
     """
     def __init__(self, corpus=None, norm='l2'):
         """
-        Compute the 'l0', 'l1' or 'l2' normalization by normalizing seperately
+        Compute the 'l0', 'l1' or 'l2' normalization by normalizing separately
         for each doc in a corpus.
         Formula for 'l0' norm for term 'i' in document 'j' in a corpus of 'D' documents is::
 
@@ -44,9 +44,7 @@ def __init__(self, corpus=None, norm='l2'):
 
           norml2_{i, j} = (i / sqrt(sum(square(values in j))))
         """
-        if norm not in ('l0', 'l1', 'l2'):
-            raise ValueError("%s is not a supported norm" % norm)
-        self.norm = norm
+        self.norm = matutils.isnorm(norm)
         if corpus is not None:
             self.calc_norm(corpus)
         else:

gensim/similarities/docsim.py

@@ -195,10 +195,7 @@ def __init__(self, output_prefix, corpus, num_features, num_best=None, chunksize
         logger.info("starting similarity index under %s", self.output_prefix)
         self.num_features = num_features
         self.num_best = num_best
-        if norm not in ('l0', 'l1', 'l2'):
-            raise ValueError("'%s' is not a supported norm" % norm)
-        else:
-            self.normalize = norm
+        self.normalize = matutils.isnorm(norm)
         self.chunksize = int(chunksize)
         self.shardsize = shardsize
         self.shards = []