text: import sys
  sys.path.append('../')
  from Normalizer.Normalizer import Normalizer
  import unittest
  import numpy as np
  from sklearn.preprocessing import MinMaxScaler, StandardScaler
  
  class TestNormalizer(unittest.TestCase):
      normalizer = Normalizer()
      test_data = [ 61.19499969,  57.31000137,  56.09249878,  61.72000122,
                  61.38000107,  64.61000061,  61.93500137,  63.70249939,
                  63.57249832,  60.22750092,  61.23249817,  60.35250092,
                  65.61750031,  64.85749817,  66.51750183,  66.99749756,
                  68.3125    ,  71.76249695,  71.10749817,  71.67250061,
                  70.69999695,  69.23249817,  67.09249878,  69.02500153,
                  68.75749969,  70.74250031,  70.79250336,  69.64499664,
                  71.93250275,  73.44999695,  72.26750183,  73.29000092,
                  74.38999939,  75.15750122,  75.93499756,  77.53250122,
                  78.75250244,  77.85250092,  76.91249847,  77.38500214,
                  76.92749786,  78.73999786,  78.28500366,  79.80750275,
                  79.21250153,  79.72250366,  79.18250275,  79.52749634,
                  79.5625    ,  79.48500061,  80.46250153,  80.83499908,
                  81.27999878,  80.58000183,  82.875     ,  83.36499786,
                  85.99749756,  88.20999908,  83.97499847,  84.69999695,
                  85.74749756,  88.01999664,  87.89749908,  87.93250275,
                  87.43000031,  89.71749878,  91.63249969,  90.01499939,
                  91.20999908,  88.40750122,  90.44499969,  91.19999695,
                  91.02749634,  91.02749634,  93.46250153,  93.17250061,
                  95.34249878,  95.75250244,  95.91999817,  95.47750092,
                  97.05750275,  97.72499847,  96.52249908,  96.32749939,
                  98.35749817,  97.        ,  97.27249908,  92.84500122,
                  92.61499786,  94.80999756,  93.25250244,  95.04000092,
                  96.19000244, 106.26000214, 108.9375    , 109.66500092,
                  110.0625    , 113.90249634, 111.11250305, 112.72750092]
      # test_data = np.array([round(t, 2) for t in test_data])
      test_data = np.array(test_data)
      test_data = np.reshape(test_data, (-1,1))
  
      def test_transform_featurescaler(self):
          result = self.normalizer.FeatureScaler.transform(self.test_data)
          scaler = MinMaxScaler()
          scaler.fit(self.test_data)
          correct = scaler.transform(self.test_data)
          self.assertTrue(np.allclose(result, correct))
      
      def test_reverse_transform_featurescaler(self):
          result = self.normalizer.FeatureScaler.transform(self.test_data)
          result = self.normalizer.FeatureScaler.reverse_transform(result)
          self.assertTrue(np.allclose(result, self.test_data))
      
      def test_raise_transform(self):
          test_data = self.test_data.tolist()
          with self.assertRaises(ValueError):
              self.normalizer.FeatureScaler.transform(test_data)
              self.normalizer.MeanScaler.transform(test_data)
              self.normalizer.ZScoreScaler.transform(test_data)
              self.normalizer.UnitLengthScaler.transform(test_data)
      