diff --git a/markov_matrix.py b/markov_matrix.py
index 57f7ced..50f19da 100644
--- a/markov_matrix.py
+++ b/markov_matrix.py
@@ -2,14 +2,22 @@
 My idea here is to encode the entire corpus as one giant two-dimensional numpy array of floats where each row is a
 condition word and each column in that row is every other word in the corpus and the probability that the word follows
 the conditional word.
+
+This was an interesting idea, but ultimately not that useful since the resulting numpy array is significantly larger
+than just storing the CFD in a python dictionary. There might be some crazy linear algebra I could run to compress this
+array to make it less sparse. But, I would need to use the same N words for all corpora and I think that the resulting
+compressed arrays would only be really useful for comparing with each other to find things like "closeness" between two
+corpora as defined by the probabilities that some words follow other words in the text. Also, using the same N words
+across all corpora is less awesome because you will miss out on the unique words (names, proper nouns, etc.) present in
+only some corpora.
 """
+import codecs
+import sys
 from collections import OrderedDict
 from itertools import islice
 
-import codecs
 import nltk  # TODO: write/import a tokenizer so I don't need to import this
 import numpy as np
-import sys
 
 
 BEGIN_TOKEN = '__BEGIN__'