Fix for issue #10

KMeansClustering now accepts an optional equality function. This is useful
when using numpy arrays as inputs.

cluster.py

@@ -680,7 +680,7 @@ class KMeansClustering:
       >>> clusters = cl.getclusters(2)
     """
 
-    def __init__(self, data, distance=None):
+    def __init__(self, data, distance=None, equality=None):
         """
         Constructor
 
@@ -690,11 +690,14 @@ def __init__(self, data, distance=None):
                        Default: It assumes the tuples contain numeric values
                                 and appiles a generalised form of the
                                 euclidian-distance algorithm on them.
+            equality - A function to test equality of items. By default the
+                       standard python equality operator (``==``) is applied.
         """
         self.__clusters = []
         self.__data = data
         self.distance = distance
         self.__initial_length = len(data)
+        self.equality = equality
 
         # test if each item is of same dimensions
         if len(data) > 1 and isinstance(data[0], TupleType):
@@ -768,7 +771,7 @@ def assign_item(self, item, origin):
                     centroid(closest_cluster)):
                 closest_cluster = cluster
 
-        if closest_cluster != origin:
+        if id(closest_cluster) != id(origin):
             self.move_item(item, origin, closest_cluster)
             return True
         else:
@@ -784,7 +787,16 @@ def move_item(self, item, origin, destination):
             origin      - the originating cluster
             destination - the target cluster
         """
-        destination.append(origin.pop(origin.index(item)))
+        if self.equality:
+            item_index = 0
+            for i, element in enumerate(origin):
+                if self.equality(element, item):
+                    item_index = i
+                    break
+        else:
+            item_index = origin.index(item)
+
+        destination.append(origin.pop(item_index))
 
     def initialise_clusters(self, input_, clustercount):
         """

test.py

@@ -202,15 +202,41 @@ def testLostFunctionReference(self):
                 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)
+
+
+class NumpyTests(unittest.TestCase):
+
+    def testNumpyRandom(self):
+        from cluster import KMeansClustering
+        from numpy import random as rnd
+        data = rnd.rand(500, 2)
+        cl = KMeansClustering(data, lambda p0, p1: (
+            p0[0] - p1[0]) ** 2 + (p0[1] - p1[1]) ** 2, numpy.array_equal)
+        cl.getclusters(10)
+
 
 if __name__ == '__main__':
-    unittest.TextTestRunner(verbosity=2).run(
-            unittest.TestSuite((
-                    unittest.makeSuite(HClusterSmallListTestCase),
-                    unittest.makeSuite(HClusterIntegerTestCase),
-                    unittest.makeSuite(HClusterStringTestCase),
-                    unittest.makeSuite(KClusterSmallListTestCase),
-                    unittest.makeSuite(KCluster2DTestCase),
-                    unittest.makeSuite(KClusterSFBugs),
-                ))
-            )
+    suite = unittest.TestSuite((
+            unittest.makeSuite(HClusterSmallListTestCase),
+            unittest.makeSuite(HClusterIntegerTestCase),
+            unittest.makeSuite(HClusterStringTestCase),
+            unittest.makeSuite(KClusterSmallListTestCase),
+            unittest.makeSuite(KCluster2DTestCase),
+            unittest.makeSuite(KClusterSFBugs)))
+
+    try:
+        import numpy  # NOQA
+        tests = unittest.makeSuite(NumpyTests)
+        suite.addTests(tests)
+    except ImportError:
+        print "numpy not available. Associated test will not be loaded!"
+
+    unittest.TextTestRunner(verbosity=2).run(suite)