Test with euclidian distance.

2 files changed, 8 insertions, 1 deletions

diff --git a/source/clusters.py b/source/clusters.py
index 02766bdf..e53fac9b 100755
--- a/source/clusters.py
+++ b/source/clusters.py
@@ -1,6 +1,7 @@
 #! /usr/bin/env python

 #-*- coding: utf-8 -*-

 

+import math

 import random

 

 from math import sqrt

@@ -51,6 +52,12 @@ def tanimoto(v1, v2):
             shr += 1 # in both

     return 1.0 - (float(shr) / (c1 + c2 - shr))

 

+def euclidian(v1, v2):

+    d = 0.0

+    for i in range(len(v1)):

+        d += (v1[i] - v2[i])**2

+    return math.sqrt(d)

+

 def kcluster(rows,distance=pearson,k=4):

   # Determine the minimum and maximum values for each point

   ranges=[(min([row[i] for row in rows]),max([row[i] for row in rows])) 

diff --git a/source/testclusters.py b/source/testclusters.py
index 728e9c1b..6feabfc8 100644
--- a/source/testclusters.py
+++ b/source/testclusters.py
@@ -10,7 +10,7 @@ blognames,words,data = clusters.readfile("blogdata1.txt")
 coords = clusters.scaledown(data)
 
 print "Generating clusters..."
-kclust = clusters.kcluster(data, k=K, distance=clusters.pearson)
+kclust = clusters.kcluster(data, k=K, distance=clusters.euclidian)
 print
 print "Clusters:"
 for i in range(K):