original_docstring: Calculate Mcnemar's given two datasets and "correct" set
  
      Args:
          y_none: Predictions without emoji vectors
          y_with: Predictions made with our emoji vectors
          y_test: True predictions
  
      Returns:
  
      
docstring: Calculate Mcnemar's given two datasets and "correct" set
docstring_tokens: Calculate Mcnemar ' s given two datasets and " correct " set
code: def calculate_mcnemars(y_none, y_with, y_test):
      """Calculate Mcnemar's given two datasets and "correct" set
  
      Args:
          y_none: Predictions without emoji vectors
          y_with: Predictions made with our emoji vectors
          y_test: True predictions
  
      Returns:
  
      """
      r = np.zeros(4)
      # syntax: 0=none/ours, 1=none/theirs | 0=no/no, 1=no/yes, 2=yes/no, 3=yes/yes
  
      for j in range(len(y_none)):
          ind = 0
          if y_none[j] == y_test[j]:
              ind += 2
          if y_with[j] == y_test[j]:
              ind += 1
          r[ind] += 1
  
      return __mcnemar_chi(r[2], r[1])
code_tokens: def calculate_mcnemars ( y_none , y_with , y_test ) : r = np . zeros ( 4 ) for j in range ( len ( y_none ) ) : ind = 0 if y_none [ j ] == y_test [ j ] : ind += 2 if y_with [ j ] == y_test [ j ] : ind += 1 r [ ind ] += 1 return __mcnemar_chi ( r [ 2 ] , r [ 1 ] )
short_docstring: Calculate Mcnemar's given two datasets and "correct" set
short_docstring_tokens: Calculate Mcnemar ' s given two datasets and " correct " set
comment: """Calculate Mcnemar's given two datasets and "correct" set
  
      Args:
          y_none: Predictions without emoji vectors
          y_with: Predictions made with our emoji vectors
          y_test: True predictions
  
      Returns:
  
      """$SEP$# syntax: 0=none/ours, 1=none/theirs | 0=no/no, 1=no/yes, 2=yes/no, 3=yes/yes