"""Test the minimum spanning tree function""" from __future__ import division, print_function, absolute_import import numpy as np from numpy.testing import assert_ import numpy.testing as npt from scipy.sparse import csr_matrix from scipy.sparse.csgraph import minimum_spanning_tree def test_minimum_spanning_tree(): # Create a graph with two connected components. graph = [[0,1,0,0,0], [1,0,0,0,0], [0,0,0,8,5], [0,0,8,0,1], [0,0,5,1,0]] graph = np.asarray(graph) # Create the expected spanning tree. expected = [[0,1,0,0,0], [0,0,0,0,0], [0,0,0,0,5], [0,0,0,0,1], [0,0,0,0,0]] expected = np.asarray(expected) # Ensure minimum spanning tree code gives this expected output. csgraph = csr_matrix(graph) mintree = minimum_spanning_tree(csgraph) npt.assert_array_equal(mintree.todense(), expected, 'Incorrect spanning tree found.') # Ensure that the original graph was not modified. npt.assert_array_equal(csgraph.todense(), graph, 'Original graph was modified.') # Now let the algorithm modify the csgraph in place. mintree = minimum_spanning_tree(csgraph, overwrite=True) npt.assert_array_equal(mintree.todense(), expected, 'Graph was not properly modified to contain MST.') np.random.seed(1234) for N in (5, 10, 15, 20): # Create a random graph. graph = 3 + np.random.random((N, N)) csgraph = csr_matrix(graph) # The spanning tree has at most N - 1 edges. mintree = minimum_spanning_tree(csgraph) assert_(mintree.nnz < N) # Set the sub diagonal to 1 to create a known spanning tree. idx = np.arange(N-1) graph[idx,idx+1] = 1 csgraph = csr_matrix(graph) mintree = minimum_spanning_tree(csgraph) # We expect to see this pattern in the spanning tree and otherwise # have this zero. expected = np.zeros((N, N)) expected[idx, idx+1] = 1 npt.assert_array_equal(mintree.todense(), expected, 'Incorrect spanning tree found.')