Update README.md

Author: asmith26

README.md

@@ -17,33 +17,33 @@ In a python shell:
 ```
 >>> from vose_sampler import VoseAlias
 >>> # Create the required probability distribution (here we use the example of a weighted coin with probability H:=Heads=0.2 and T:=Tail=0.8)
->>> dist = { "H":0.2, "T":0.8 }
+>>> dist = {"H":0.2, "T":0.8}
 >>> # Create probability and alias tables from the probability distribution, for sampling via Vose's alias method
->>> VA = VoseAlias( dist )
+>>> VA = VoseAlias(dist)
 >>> # Generate n random outcomes (here n=10)
->>> VA.sample_n( size=10 )
-
-Generating 10 random samples:
-
-T
-T
-T
-T
-H
-T
-T
-T
-T
-H
+>>> VA.sample_n(size=10)
+['T', 'T', 'H', 'T', 'T', 'T', 'T', 'H', 'T', 'T']
 ```
 
 ### Unigram language model example
 To create a [unigram language model](https://en.wikipedia.org/wiki/Language_model#Unigram_models) for [Alice in Wonderland](http://www.gutenberg.org/cache/epub/11/pg11.txt) and sample 10 words from this, run the main script from the command line with options:
 
-`$ vose-sampler -p data/Alice.txt -n 10`
+```
+$ vose-sampler -p data/Alice.txt -n 10  # or: python vose_sampler/vose_sampler.py -p data/Alice.txt -n 10
 
-(equally `$ python vose_sampler/vose_sampler.py -p data/Alice.txt -n 10`)
+Generating 10 random samples:
 
+the
+more
+she
+Rabbit,
+say
+suddenly
+at
+soon
+thing
+solemn
+```
 
 [Note, this is intended to illustrate how Vose's alias method could be used. Thus I have not included any preprocessing steps that would make the language model more realistic; for example, we could add handling of upper vs. lower case words (so that e.g. "The" and "the" are not considered distinct), as well as handling of punctuation (e.g. so "the" and "the." are considered the same).
 
@@ -73,4 +73,4 @@ username = asmith26
 password = some_harder_password
 ```
 
-- Assuming everything looks good `$ twine upload dist/*`
+- Assuming everything looks good `$ twine upload dist/*`