old_contents: import unittest
  import numpy as np
  
  from Orange import data
  import Orange.classification.naive_bayes as nb
  from Orange.evaluation import scoring, testing
  
  
  class NaiveBayesTest(unittest.TestCase):
      def test_NaiveBayes(self):
          nrows = 1000
          ncols = 10
          x = np.random.random_integers(1, 3, (nrows, ncols))
          col = np.random.randint(ncols)
          y = x[:nrows, col].reshape(nrows, 1) + 100
  
          x1, x2 = np.split(x, 2)
          y1, y2 = np.split(y, 2)
          t = data.Table(x1, y1)
          learn = nb.BayesLearner()
          clf = learn(t)
          z = clf(x2)
          self.assertTrue((z.reshape((-1, 1)) == y2).all())
  
      def test_BayesStorage(self):
          nrows = 200
          ncols = 10
          x = np.random.random_integers(0, 5, (nrows, ncols))
          x[:, 0] = np.ones(nrows) * 3
          y = x[:, ncols / 2].reshape(nrows, 1)
          table = data.Table(x, y)
          bayes = nb.BayesStorageLearner()
          results = testing.CrossValidation(table, [bayes], k=10)
          ca = scoring.CA(results)
          self.assertGreater(ca, 0.5)
  
new_contents: import unittest
  import numpy as np
  
  import Orange
  import Orange.classification.naive_bayes as nb
  from Orange.evaluation import scoring, testing
  
  
  class NaiveBayesTest(unittest.TestCase):
      def test_NaiveBayes(self):
          nrows = 1000
          ncols = 10
          x = np.random.random_integers(1, 3, (nrows, ncols))
          col = np.random.randint(ncols)
          y = x[:nrows, col].reshape(nrows, 1) + 100
  
          x1, x2 = np.split(x, 2)
          y1, y2 = np.split(y, 2)
          t = Orange.data.Table(x1, y1)
          learn = nb.BayesLearner()
          clf = learn(t)
          z = clf(x2)
          self.assertTrue((z.reshape((-1, 1)) == y2).all())
  
      def test_BayesStorage(self):
          nrows = 200
          ncols = 10
          x = np.random.random_integers(0, 4, (nrows, ncols))
          x[:, 0] = 3
          y = x[:, ncols // 2].reshape(nrows, 1)
          continuous_table = Orange.data.Table(x, y)
          table = Orange.data.discretization.DiscretizeTable(continuous_table)
          bayes = nb.BayesStorageLearner()
          results = testing.CrossValidation(table, [bayes], k=10)
          ca = scoring.CA(results)
          self.assertGreater(ca, 0.6)
  
subject: Improve naive bayes unit test.