def __do_one_iteration(self,remaining_nodes,cutoff):
(node,len_neigh) = self.condensed_matrix.choose_node_with_higher_cardinality(remaining_nodes,cutoff) #@UnusedVariable
neighbours = self.condensed_matrix.get_neighbors_for_node(node,remaining_nodes,cutoff)
cluster_tuple = (node, neighbours)
cluster = cluster_from_tuple(cluster_tuple)
eliminate_cluster_from_node_list(remaining_nodes,cluster)
return cluster
def test_eliminate_cluster(self):
cluster = cluster_from_tuple((4,[2,3,5,7,9]))
nodes = range(15)
nodes_left = [0,1,6,8,10,11,12,13,14]
eliminate_cluster_from_node_list(nodes, cluster)
nodes_left.sort()
for i in range(len(nodes)):
self.assertEqual(nodes[i],nodes_left[i])
def __do_one_iteration(self, remaining_nodes, cutoff):
(node, len_neigh
) = self.condensed_matrix.choose_node_with_higher_cardinality(
remaining_nodes, cutoff) #@UnusedVariable
neighbours = self.condensed_matrix.get_neighbors_for_node(
node, remaining_nodes, cutoff)
cluster_tuple = (node, neighbours)
cluster = cluster_from_tuple(cluster_tuple)
eliminate_cluster_from_node_list(remaining_nodes, cluster)
return cluster
def __do_one_iteration(self,remaining_nodes,cutoff):
"""
IMPORTANT: Prototype and medoid may not be the same element (ex @ http://www.jstatsoft.org/v01/i04/paper).
"""
(node,len_neigh) = self.condensed_matrix.choose_node_with_higher_cardinality(remaining_nodes,cutoff) #@UnusedVariable
neighbours = self.condensed_matrix.get_neighbors_for_node(node,remaining_nodes,cutoff)
cluster_tuple = (node, neighbours)
cluster = cluster_from_tuple(cluster_tuple)
eliminate_cluster_from_node_list(remaining_nodes,cluster)
return cluster
def __do_one_iteration(self, remaining_nodes, cutoff):
"""
IMPORTANT: Prototype and medoid may not be the same element (ex @ http://www.jstatsoft.org/v01/i04/paper).
"""
(node, len_neigh
) = self.condensed_matrix.choose_node_with_higher_cardinality(
remaining_nodes, cutoff) #@UnusedVariable
neighbours = self.condensed_matrix.get_neighbors_for_node(
node, remaining_nodes, cutoff)
cluster_tuple = (node, neighbours)
cluster = cluster_from_tuple(cluster_tuple)
eliminate_cluster_from_node_list(remaining_nodes, cluster)
return cluster
def test_cluster_from_tuple(self):
cluster_tuple = (1,[16,17,18])
expected_cluster = Cluster(1,[1,16,17,18])
cluster = cluster_from_tuple(cluster_tuple)
self.assertEquals(expected_cluster,cluster)
def test_get_sizes(self):
myclusters = []
for c in test_data.clusters:
myclusters.append(cluster_from_tuple(c))
sizes = [5,4,4,4,3]
numpy.testing.assert_array_equal(sizes, get_cluster_sizes(myclusters)[1], "Cluster sizes are different")
