#!/usr/bin/python
import random
from textcluster2 import cluster, dump_clusters
nclusters = 3
data = [
"pacific ocean", "pacific", "the pacific ocean", "pacific ocean",
"atlantic", "atlantic ocean", "ocean", "sea", "indian ocean",
"pacific ocean", "indian", "atlantic", "southern ocean", "lake",
"lake baikal", "crater lake"
]
random.shuffle(data)
print "data: " + str(data)
nclusters, centroids, cids = cluster(data, nclusters)
print
dump_clusters(nclusters, centroids, data, cids)
import sys
import json
import io
from textcluster2 import cluster, dump_clusters
# def fetch_tweets():
# tweets=[]
# uhandle=urllib2.urlopen('http://api.twitter.com/1/statuses/public_timeline.json')
# jstr=uhandle.read()
# jarray=json.loads(jstr)
# for jobj in jarray:
# tweets.append(jobj['text'])
# return tweets
def read_tweets(fnames):
tweets = []
for fname in fnames:
with io.open(fname, 'r') as f:
jstr = f.read()
jarray = json.loads(jstr)
for jobj in jarray:
tweets.append(jobj['text'])
return tweets
tweets = read_tweets(sys.argv[1:])
nclusters, centroids, cids = cluster(tweets, 8)
print
dump_clusters(nclusters, centroids, tweets, cids)
import sys
import json
import io
from textcluster2 import cluster, dump_clusters
# def fetch_tweets():
# tweets=[]
# uhandle=urllib2.urlopen('http://api.twitter.com/1/statuses/public_timeline.json')
# jstr=uhandle.read()
# jarray=json.loads(jstr)
# for jobj in jarray:
# tweets.append(jobj['text'])
# return tweets
def read_tweets(fnames):
tweets=[]
for fname in fnames:
with io.open(fname, 'r') as f:
jstr=f.read()
jarray=json.loads(jstr)
for jobj in jarray:
tweets.append(jobj['text'])
return tweets
tweets=read_tweets(sys.argv[1:])
nclusters,centroids,cids=cluster(tweets, 8)
print
dump_clusters(nclusters, centroids, tweets, cids)
import random
from textcluster2 import cluster, dump_clusters
nclusters=3
data=["pacific ocean",
"pacific",
"the pacific ocean",
"pacific ocean",
"atlantic",
"atlantic ocean",
"ocean",
"sea",
"indian ocean",
"pacific ocean",
"indian",
"atlantic",
"southern ocean",
"lake",
"lake baikal",
"crater lake"
]
random.shuffle(data)
print "data: " + str(data)
nclusters, centroids, cids=cluster(data, nclusters)
print
dump_clusters(nclusters, centroids, data, cids)