original_docstring: 
    Predicts on 5.zip with the provided model.
    
    Args:
      model - the model to predict with
      infile_base - bare input data name without path or extension
      bits - the feature space shoul be of dimension 2**bits
  
    Returns:
      predictions on 5.zip
    
docstring: Predicts on 5.zip with the provided model.
  Args:
  model - the model to predict with
  infile_base - bare input data name without path or extension
  bits - the feature space shoul be of dimension 2**bits
  
  
docstring_tokens: Predicts on 5 . zip with the provided model . Args : model - the model to predict with infile_base - bare input data name without path or extension bits - the feature space shoul be of dimension 2 ** bits
code: def test(model, infile_base, bits):
    '''
    Predicts on 5.zip with the provided model.
    
    Args:
      model - the model to predict with
      infile_base - bare input data name without path or extension
      bits - the feature space shoul be of dimension 2**bits
  
    Returns:
      predictions on 5.zip
    '''
    test_set_name = infile_base + '.5'
    print 'loading test set...'
    x, _ = util.load_sparse(test_set_name, n_features=2**bits, verbose=False)
    dv = model.decision_function(x)
    return dv
code_tokens: def test ( model , infile_base , bits ) : test_set_name = infile_base + '.5' print 'loading test set...' x , _ = util . load_sparse ( test_set_name , n_features = 2 ** bits , verbose = False ) dv = model . decision_function ( x ) return dv
short_docstring: Predicts on 5.zip with the provided model.
short_docstring_tokens: Predicts on 5 . zip with the provided model .
comment: '''
    Predicts on 5.zip with the provided model.
    
    Args:
      model - the model to predict with
      infile_base - bare input data name without path or extension
      bits - the feature space shoul be of dimension 2**bits
  
    Returns:
      predictions on 5.zip
    '''