old_contents: """
  Testing for Clustering methods
  
  """
  
  import numpy as np
  from numpy.testing import assert_equal
  from scipy import sparse
  
  from .. import SpectralClustering
  
  
  def test_spectral_clustering():
      S = np.array([[1, 5, 2, 1, 0, 0, 0],
                    [5, 1, 3, 1, 0, 0, 0],
                    [2, 3, 1, 1, 0, 0, 0],
                    [1, 1, 1, 1, 2, 1, 1],
                    [0, 0, 0, 2, 2, 3, 2],
                    [0, 0, 0, 1, 3, 1, 4],
                    [0, 0, 0, 1, 2, 4, 1],
                   ])
  
      for mat in (S, sparse.csr_matrix(S)):
          labels = SpectralClustering().fit(mat, k=2).labels_
          if labels[0] == 0:
              labels = 1 - labels
  
          assert_equal(labels, [1, 1, 1, 0, 0, 0, 0])
  
  
  def test_spectral_clustering_sparse():
      # We need a large matrice, or the lobpcg solver will fallback to its
      # non-sparse and buggy mode
      S = np.array([[1, 5, 2, 2, 1, 0, 0, 0, 0, 0],
                    [5, 1, 3, 2, 1, 0, 0, 0, 0, 0],
                    [2, 3, 1, 1, 1, 0, 0, 0, 0, 0],
                    [2, 2, 1, 1, 1, 0, 0, 0, 0, 0],
                    [1, 1, 1, 1, 1, 1, 2, 1, 1, 1],
                    [0, 0, 0, 0, 1, 2, 2, 3, 3, 2],
                    [0, 0, 0, 0, 2, 2, 3, 3, 3, 4],
                    [0, 0, 0, 0, 1, 3, 3, 1, 2, 4],
                    [0, 0, 0, 0, 1, 3, 3, 2, 1, 4],
                    [0, 0, 0, 0, 1, 2, 4, 4, 4, 1],
                   ])
  
      S = sparse.coo_matrix(S)
  
      labels = SpectralClustering().fit(S, k=2).labels_
      if labels[0] == 0:
          labels = 1 - labels
  
      assert np.mean(labels == [1, 1, 1, 1, 1, 0, 0, 0, 0, 0]) > .9
  
  
new_contents: """
  Testing for Clustering methods
  
  """
  
  import numpy as np
  from numpy.testing import assert_equal
  from scipy import sparse
  import nose
  
  from .. import SpectralClustering
  
  
  def test_spectral_clustering():
      S = np.array([[1, 5, 2, 1, 0, 0, 0],
                    [5, 1, 3, 1, 0, 0, 0],
                    [2, 3, 1, 1, 0, 0, 0],
                    [1, 1, 1, 1, 2, 1, 1],
                    [0, 0, 0, 2, 2, 3, 2],
                    [0, 0, 0, 1, 3, 1, 4],
                    [0, 0, 0, 1, 2, 4, 1],
                   ])
  
      for mat in (S, sparse.csr_matrix(S)):
          labels = SpectralClustering().fit(mat, k=2).labels_
          if labels[0] == 0:
              labels = 1 - labels
  
          assert_equal(labels, [1, 1, 1, 0, 0, 0, 0])
  
  
  def test_spectral_clustering_sparse():
      # We need a large matrice, or the lobpcg solver will fallback to its
      # non-sparse and buggy mode
      raise nose.SkipTest("XFailed Test")
      S = np.array([[1, 5, 2, 2, 1, 0, 0, 0, 0, 0],
                    [5, 1, 3, 2, 1, 0, 0, 0, 0, 0],
                    [2, 3, 1, 1, 1, 0, 0, 0, 0, 0],
                    [2, 2, 1, 1, 1, 0, 0, 0, 0, 0],
                    [1, 1, 1, 1, 1, 1, 2, 1, 1, 1],
                    [0, 0, 0, 0, 1, 2, 2, 3, 3, 2],
                    [0, 0, 0, 0, 2, 2, 3, 3, 3, 4],
                    [0, 0, 0, 0, 1, 3, 3, 1, 2, 4],
                    [0, 0, 0, 0, 1, 3, 3, 2, 1, 4],
                    [0, 0, 0, 0, 1, 2, 4, 4, 4, 1],
                   ])
  
      S = sparse.coo_matrix(S)
  
      labels = SpectralClustering().fit(S, k=2).labels_
      if labels[0] == 0:
          labels = 1 - labels
  
      assert np.mean(labels == [1, 1, 1, 1, 1, 0, 0, 0, 0, 0]) > .9
  
  
subject: mark heisentest as skipped: it randomly passes 3 out of 5 times on my box with pyamg installed