class_docstr: Close implementation of poisoning attack on Support Vector Machines (SVM) by Biggio et al.
  
  | Paper link: https://arxiv.org/pdf/1206.6389.pdf
class_docstr_tok: ["Close","implementation","of","poisoning","attack","on","Support","Vector","Machines","(","SVM",")","by","Biggio","et","al",".","|","Paper","link",":","https",":","\/\/arxiv.org\/pdf\/1206.6389.pdf"]
docstr: Predicts the inputs by binary classifier and outputs -1 and 1 instead of 0 and 1.
  
  :param vec: An input array.
  :return: An array of -1/1 predictions.
docstr_tok: ["Predicts","the","inputs","by","binary","classifier","and","outputs","-1","and","1","instead","of","0","and","1.",":","param","vec",":","An","input","array",".",":","return",":","An","array","of","-1\/1","predictions","."]
code: def predict_sign(self, vec: np.ndarray) -> np.ndarray:
      """
      Predicts the inputs by binary classifier and outputs -1 and 1 instead of 0 and 1.
  
      :param vec: An input array.
      :return: An array of -1/1 predictions.
      """
      # pylint: disable=W0212
      preds = self.estimator.model.predict(vec)
      return 2 * preds - 1
  
file_path: adversarial-robustness-toolbox/art/attacks/poisoning/poisoning_attack_svm.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Optional, Tuple
  import numpy
  from tqdm.auto import tqdm
  from art.attacks.attack import PoisoningAttackWhiteBox
  from art.estimators.classification.scikitlearn import ScikitlearnSVC
  from art.utils import compute_success
label_desc: Use image node_id 4 for calling the PoisoningAttackSVM obj's underlying member method code with example usage: obj.predict_sign(vec) and returns: unknown