code: 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.",
          )
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  from numpy.testing import assert_
  import numpy.testing
  from scipy.sparse import csr_matrix
  from scipy.sparse.csgraph import minimum_spanning_tree
label_desc: Use image node_id 1 for calling a global function with example usage: test_minimum_spanning_tree() without return types