old_contents: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  import unittest
  from spectralcluster import spectral_clusterer
  
  
  class TestSpectralClusterer(unittest.TestCase):
      """Tests for the SpectralClusterer class."""
  
      def setUp(self):
          # fix random seeds for reproducing results
          np.random.seed(1)
  
      def test_6by2_matrix(self):
          X = np.array([
              [1.0, 0.0],
              [1.1, 0.1],
              [0.0, 1.0],
              [0.1, 1.0],
              [0.9, -0.1],
              [0.0, 1.2],
          ])
          clusterer = spectral_clusterer.SpectralClusterer(
              p_percentile=0.95,
              gaussian_blur_sigma=0)
          labels = clusterer.predict(X)
          expected = np.array([0, 0, 1, 1, 0, 1])
          self.assertTrue(np.array_equal(expected, labels))
  
      def test_1000by6_matrix(self):
          X = np.array(
              [[1.0, 0.0, 0.0, 0.0, 0.0, 0.0]] * 400 +
              [[0.0, 1.0, 0.0, 0.0, 0.0, 0.0]] * 300 +
              [[0.0, 0.0, 2.0, 0.0, 0.0, 0.0]] * 200 +
              [[0.0, 0.0, 0.0, 1.0, 0.0, 0.0]] * 100
          )
          noisy = np.random.rand(1000, 6) * 2 - 1
          X = X + noisy * 0.1
          clusterer = spectral_clusterer.SpectralClusterer(
              p_percentile=0.2,
              gaussian_blur_sigma=0,
              stop_eigenvalue=0.01)
          labels = clusterer.predict(X)
          expected = np.array(
              [2] * 400 + [0] * 300 + [1] * 200 + [3] * 100
          )
          self.assertTrue(np.array_equal(expected, labels))
  
  
  if __name__ == "__main__":
      unittest.main()
  
new_contents: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  import unittest
  from spectralcluster import spectral_clusterer
  
  
  class TestSpectralClusterer(unittest.TestCase):
      """Tests for the SpectralClusterer class."""
  
      def setUp(self):
          # fix random seeds for reproducing results
          np.random.seed(1)
  
      def test_6by2_matrix(self):
          X = np.array([
              [1.0, 0.0],
              [1.1, 0.1],
              [0.0, 1.0],
              [0.1, 1.0],
              [0.9, -0.1],
              [0.0, 1.2],
          ])
          clusterer = spectral_clusterer.SpectralClusterer(
              p_percentile=0.95,
              gaussian_blur_sigma=0)
          labels = clusterer.predict(X)
          expected = np.array([0, 0, 1, 1, 0, 1])
          self.assertTrue(np.array_equal(expected, labels))
  
      def test_1000by6_matrix(self):
          X = np.array(
              [[1.0, 0.0, 0.0, 0.0, 0.0, 0.0]] * 400 +
              [[0.0, 1.0, 0.0, 0.0, 0.0, 0.0]] * 300 +
              [[0.0, 0.0, 2.0, 0.0, 0.0, 0.0]] * 200 +
              [[0.0, 0.0, 0.0, 1.0, 0.0, 0.0]] * 100
          )
          noisy = np.random.rand(1000, 6) * 2 - 1
          X = X + noisy * 0.1
          clusterer = spectral_clusterer.SpectralClusterer(
              p_percentile=0.2,
              gaussian_blur_sigma=0,
              stop_eigenvalue=0.01)
          labels = clusterer.predict(X)
          expected = np.array(
              [0] * 400 + [1] * 300 + [2] * 200 + [3] * 100
          )
          self.assertTrue(np.array_equal(expected, labels))
  
  
  if __name__ == "__main__":
      unittest.main()
  
subject: Fix the test