def test_dbscan_clustering_with_tweets(self):
from_date = datetime.datetime(2011, 1, 25, 12, 0, 0)
to_date = datetime.datetime(2011, 1, 26, 12, 30, 0)
items = ws.get_documents_by_date(from_date, to_date, limit=100)
epsilon = 0.02
min_pts = 2
dbscan = DBSCANClusterer(filter_terms=False)
dbscan.add_documents(items)
clusters = dbscan.run(epsilon, min_pts, pca=True)
dbscan.dump_clusters_to_file("dbscan_with_tweets")
#=======================================================================
dbscan.plot_scatter()
#dbscan.plot_growth_timeline(cumulative=False, plot_method="matplotlib")
# dbscan.plot_growth_timeline(cumulative=True)
dbscan.plot_growth_timeline(cumulative=True)
points = []
points.append([1,1])
points.append([1.5,1])
points.append([1.8,1.5])
points.append([2.1,1])
points.append([3.1,2])
points.append([4.1,2])
points.append([5.1,2])
points.append([10,10])
points.append([11,10.5])
points.append([9.5,11])
points.append([9.9,11.4])
points.append([15.0, 17.0])
points.append([15.0, 17.0])
points.append([7.5, -5.0])
dbscan = DBSCANClusterer()
#Small hacks..in normal usage never set td_matrix by urself
#and never populate a dummy document_dict
dbscan.td_matrix = points
dbscan.document_dict = OrderedDict( [('0','dummy'), ('1', 'dummy'), ('2', 'dummy'),('3', 'dummy'),('4', 'dummy'),('5', 'dummy'),
('6', 'dummy'),('7', 'dummy'),('8', 'dummy'),('9', 'dummy'),('10', 'dummy'),('11', 'dummy'),('12', 'dummy'),('13', 'dummy')])
class Test_Dbscan_clustering(unittest.TestCase):
def test_dbscan_cluster(self):
clusters = dbscan.run(epsilon, min_pts)
print '\n========== Results of Clustering ============='
for cluster, members in clusters.iteritems():
print '\n--------Cluster %d---------' % cluster
for point in members:
print point
