original_docstring:  _test_adwin
      
      In this demo, an ADWIN object evaluates a sequence of numbers corresponding to 2 distributions. 
      The ADWIN object indicates the indices where change is detected.
  
      The first half of the data is a sequence of randomly generated 0's and 1's. 
      The second half of the data is a normal distribution of integers from 0 to 7.
      
      
docstring: _test_adwin
  In this demo, an ADWIN object evaluates a sequence of numbers corresponding to 2 distributions.
  The ADWIN object indicates the indices where change is detected.
  
  The first half of the data is a sequence of randomly generated 0's and 1's.
  The second half of the data is a normal distribution of integers from 0 to 7.
docstring_tokens: _test_adwin In this demo an ADWIN object evaluates a sequence of numbers corresponding to 2 distributions . The ADWIN object indicates the indices where change is detected . The first half of the data is a sequence of randomly generated 0 ' s and 1 ' s . The second half of the data is a normal distribution of integers from 0 to 7 .
code: def demo():
      """ _test_adwin
      
      In this demo, an ADWIN object evaluates a sequence of numbers corresponding to 2 distributions. 
      The ADWIN object indicates the indices where change is detected.
  
      The first half of the data is a sequence of randomly generated 0's and 1's. 
      The second half of the data is a normal distribution of integers from 0 to 7.
      
      """
      adwin = ADWIN()
      size = 2000
      change_start = 999
      np.random.seed(1)
      data_stream = np.random.randint(2, size=size)
      data_stream[change_start:] = np.random.randint(8, size=size-change_start)
  
      for i in range(size):
          adwin.add_element(data_stream[i])
          if adwin.detected_change():
              print('Change has been detected in data: ' + str(data_stream[i]) + ' - of index: ' + str(i))
code_tokens: def demo ( ) : adwin = ADWIN ( ) size = 2000 change_start = 999 np . random . seed ( 1 ) data_stream = np . random . randint ( 2 , size = size ) data_stream [ change_start : ] = np . random . randint ( 8 , size = size - change_start ) for i in range ( size ) : adwin . add_element ( data_stream [ i ] ) if adwin . detected_change ( ) : print ( 'Change has been detected in data: ' + str ( data_stream [ i ] ) + ' - of index: ' + str ( i ) )
short_docstring: _test_adwin
  In this demo, an ADWIN object evaluates a sequence of numbers corresponding to 2 distributions.
short_docstring_tokens: _test_adwin In this demo an ADWIN object evaluates a sequence of numbers corresponding to 2 distributions .
comment: """ _test_adwin
      
      In this demo, an ADWIN object evaluates a sequence of numbers corresponding to 2 distributions. 
      The ADWIN object indicates the indices where change is detected.
  
      The first half of the data is a sequence of randomly generated 0's and 1's. 
      The second half of the data is a normal distribution of integers from 0 to 7.
      
      """