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#
# This is part of "python-cluster". A library to group similar items together.
# Copyright (C) 2006 Michel Albert
#
# This library is free software; you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the Free
# Software Foundation; either version 2.1 of the License, or (at your option)
# any later version.
# This library is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
# details.
# You should have received a copy of the GNU Lesser General Public License
# along with this library; if not, write to the Free Software Foundation, Inc.,
# 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#
from cluster import (KMeansClustering, ClusteringError)
import unittest
def compare_list(x, y):
"""
Compare lists by content. Ordering does not matter.
Returns True if both lists contain the same items (and are of identical
length)
"""
cmpx = [set(cluster) for cluster in x]
cmpy = [set(cluster) for cluster in y]
all_ok = True
for cset in cmpx:
all_ok &= cset in cmpy
for cset in cmpy:
all_ok &= cset in cmpx
return all_ok
class KClusterSmallListTestCase(unittest.TestCase):
def testClusterLen1(self):
"Testing that a search space of length 1 returns only one cluster"
cl = KMeansClustering([876])
self.assertEqual([876], cl.getclusters(2))
self.assertEqual([876], cl.getclusters(5))
def testClusterLen0(self):
"Testing if clustering an empty set, returns an empty set"
cl = KMeansClustering([])
self.assertEqual([], cl.getclusters(2))
self.assertEqual([], cl.getclusters(7))
class KCluster2DTestCase(unittest.TestCase):
def testClusterCount(self):
"Test that asking for less than 2 clusters raises an error"
cl = KMeansClustering([876, 123, 344, 676],
distance=lambda x, y: abs(x - y))
self.assertRaises(ClusteringError, cl.getclusters, 0)
self.assertRaises(ClusteringError, cl.getclusters, 1)
def testNonsenseCluster(self):
"""
Test that asking for more clusters than data-items available raises an
error
"""
cl = KMeansClustering([876, 123], distance=lambda x, y: abs(x - y))
self.assertRaises(ClusteringError, cl.getclusters, 5)
def testUniformLength(self):
"""
Test if there is an item in the cluster that has a different
cardinality
"""
data = [(1, 5), (2, 5), (2, 6), (3, 4), (3, 5), (3, 6, 7), (7, 3),
(8, 1), (8, 2), (8), (9, 2), (9, 3)]
self.assertRaises(ValueError, KMeansClustering, data)
def testPointDoubling(self):
"test for bug #1604868"
data = [(18, 13), (15, 12), (17, 12), (18, 12), (19, 12), (16, 11),
(18, 11), (19, 10), (0, 0), (1, 4), (1, 2), (2, 3), (4, 1),
(4, 3), (5, 2), (6, 1)]
cl = KMeansClustering(data)
clusters = cl.getclusters(2)
expected = [[(18, 13), (15, 12), (17, 12), (18, 12), (19, 12),
(16, 11), (18, 11), (19, 10)],
[(0, 0), (1, 4), (1, 2), (2, 3), (4, 1),
(5, 2), (6, 1), (4, 3)]]
self.assertTrue(compare_list(
clusters,
expected),
"Elements differ!\n%s\n%s" % (clusters, expected))
def testClustering(self):
"Basic clustering test"
data = [(8, 2), (7, 3), (2, 6), (3, 5), (3, 6), (1, 5), (8, 1),
(3, 4), (8, 3), (9, 2), (2, 5), (9, 3)]
cl = KMeansClustering(data)
self.assertEqual(
cl.getclusters(2),
[[(8, 2), (8, 1), (8, 3), (7, 3), (9, 2), (9, 3)],
[(3, 5), (1, 5), (3, 4), (2, 6), (2, 5), (3, 6)]])
def testUnmodifiedData(self):
"Basic clustering test"
data = [(8, 2), (7, 3), (2, 6), (3, 5), (3, 6), (1, 5), (8, 1),
(3, 4), (8, 3), (9, 2), (2, 5), (9, 3)]
cl = KMeansClustering(data)
new_data = []
[new_data.extend(_) for _ in cl.getclusters(2)]
self.assertEqual(sorted(new_data), sorted(data))
class KClusterSFBugs(unittest.TestCase):
def testLostFunctionReference(self):
"test for bug #1727558"
cl = KMeansClustering([(1, 1), (20, 40), (20, 41)],
lambda x, y: x + y)
clusters = cl.getclusters(3)
expected = [(1, 1), (20, 40), (20, 41)]
self.assertTrue(compare_list(
clusters,
expected),
"Elements differ!\n%s\n%s" % (clusters, expected))
def testMultidimArray(self):
from random import random
data = []
for _ in range(200):
data.append([random(), random()])
cl = KMeansClustering(data, lambda p0, p1: (
p0[0] - p1[0]) ** 2 + (p0[1] - p1[1]) ** 2)
cl.getclusters(10)
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