parameters: (self, sequences, mode='binary')
return_statement: return x
docstring: Converts a list of sequences into a Numpy matrix.
  
          # Arguments
              sequences: list of sequences
                  (a sequence is a list of integer word indices).
              mode: one of "binary", "count", "tfidf", "freq"
  
          # Returns
              A Numpy matrix.
  
          # Raises
              ValueError: In case of invalid `mode` argument,
                  or if the Tokenizer requires to be fit to sample data.
docstring_summary: Converts a list of sequences into a Numpy matrix.
docstring_tokens: Converts a list of sequences into a Numpy matrix .
function: def sequences_to_matrix(self, sequences, mode='binary'):
          """Converts a list of sequences into a Numpy matrix.
  
          # Arguments
              sequences: list of sequences
                  (a sequence is a list of integer word indices).
              mode: one of "binary", "count", "tfidf", "freq"
  
          # Returns
              A Numpy matrix.
  
          # Raises
              ValueError: In case of invalid `mode` argument,
                  or if the Tokenizer requires to be fit to sample data.
          """
          if not self.num_words:
              if self.word_index:
                  num_words = len(self.word_index) + 1
              else:
                  raise ValueError('Specify a dimension (num_words argument), '
                                   'or fit on some text data first.')
          else:
              num_words = self.num_words
  
          if mode == 'tfidf' and not self.document_count:
              raise ValueError('Fit the Tokenizer on some data '
                               'before using tfidf mode.')
  
          x = np.zeros((len(sequences), num_words))
          for i, seq in enumerate(sequences):
              if not seq:
                  continue
              counts = {}
              for j in seq:
                  if j >= num_words:
                      continue
                  if j not in counts:
                      counts[j] = 1.
                  else:
                      counts[j] += 1
              for j, c in list(counts.items()):
                  if mode == 'count':
                      x[i][j] = c
                  elif mode == 'freq':
                      x[i][j] = c / len(seq)
                  elif mode == 'binary':
                      x[i][j] = 1
                  elif mode == 'tfidf':
                      # Use weighting scheme 2 in
                      # https://en.wikipedia.org/wiki/Tf%E2%80%93idf
                      tf = 1 + np.log(c)
                      idf = np.log(1 + self.document_count /
                                   (1 + self.index_docs.get(j, 0)))
                      x[i][j] = tf * idf
                  else:
                      raise ValueError('Unknown vectorization mode:', mode)
          return x
function_tokens: def sequences_to_matrix ( self , sequences , mode = 'binary' ) : if not self . num_words : if self . word_index : num_words = len ( self . word_index ) + 1 else : raise ValueError ( 'Specify a dimension (num_words argument), ' 'or fit on some text data first.' ) else : num_words = self . num_words if mode == 'tfidf' and not self . document_count : raise ValueError ( 'Fit the Tokenizer on some data ' 'before using tfidf mode.' ) x = np . zeros ( ( len ( sequences ) , num_words ) ) for i , seq in enumerate ( sequences ) : if not seq : continue counts = { } for j in seq : if j >= num_words : continue if j not in counts : counts [ j ] = 1. else : counts [ j ] += 1 for j , c in list ( counts . items ( ) ) : if mode == 'count' : x [ i ] [ j ] = c elif mode == 'freq' : x [ i ] [ j ] = c / len ( seq ) elif mode == 'binary' : x [ i ] [ j ] = 1 elif mode == 'tfidf' : # Use weighting scheme 2 in # https://en.wikipedia.org/wiki/Tf%E2%80%93idf tf = 1 + np . log ( c ) idf = np . log ( 1 + self . document_count / ( 1 + self . index_docs . get ( j , 0 ) ) ) x [ i ] [ j ] = tf * idf else : raise ValueError ( 'Unknown vectorization mode:' , mode ) return x