From a6e2a44283c7b7d0f25a5d000b24bbd2c2436d6b Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal@gmail.com>
Date: Sun, 27 Sep 2015 18:17:41 +1000
Subject: [PATCH] * Add clarifying comment

---
 examples/matcher_example.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/examples/matcher_example.py b/examples/matcher_example.py
index 2e3efadf2..041b98a9a 100644
--- a/examples/matcher_example.py
+++ b/examples/matcher_example.py
@@ -97,6 +97,14 @@ def main():
     # sydney True
     # SYDNEY True
     #
+    # The key thing to note here is that we're setting these attributes once,
+    # over the vocabulary --- and then reusing them at run-time. This means the
+    # amortized complexity of anything we do this way is going to be O(1). You
+    # can match over expressions that need to have sets with tens of thousands
+    # of values, e.g. "all the street names in Germany", and you'll still have
+    # O(1) complexity. Most regular expression algorithms don't scale well to
+    # this sort of problem.
+    #
     # Now, let's use this in a pattern
     nlp.matcher.add("AuCitySportsTeam", "ORG", {},
         [