def word_features(table):
 features = numpy.zeros((len(table), 620), dtype='float32')
 keys = table.keys()
 for i in range(len(table)):
  f = table[keys[i]]
  features[i] = (f / norm(f))
 return features

def word_features ( table ) : features = numpy . zeros ( ( len ( table ) , 620 ) , dtype = 'float32' ) keys = table . keys ( ) for i in range ( len ( table ) ) : f = table [ keys [ i ] ] features [ i ] = ( f / norm ( f ) ) return features
extract word features into a normalized matrix .