class_docstr: Wrapper class for GPy Gaussian Process classification models.
class_docstr_tok: ["Wrapper","class","for","GPy","Gaussian","Process","classification","models","."]
docstr: Create a `Classifier` instance GPY Gaussian Process classification models.
  
  :param model: GPY Gaussian Process Classification model.
  :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
         for features.
  :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
  :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
  :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
         used for data preprocessing. The first value will be subtracted from the input. The input will then
         be divided by the second one.
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code: def __init__(
      self,
      model: Optional["GPClassification"] = None,
      clip_values: Optional["CLIP_VALUES_TYPE"] = None,
      preprocessing_defences: Union[
          "Preprocessor", List["Preprocessor"], None
      ] = None,
      postprocessing_defences: Union[
          "Postprocessor", List["Postprocessor"], None
      ] = None,
      preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
  ) -> None:
      """
      Create a `Classifier` instance GPY Gaussian Process classification models.
  
      :param model: GPY Gaussian Process Classification model.
      :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
             for features.
      :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
      :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
      :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
             used for data preprocessing. The first value will be subtracted from the input. The input will then
             be divided by the second one.
      """
      from GPy.models import GPClassification
  
      if not isinstance(model, GPClassification):  # pragma: no cover
          raise TypeError(
              "Model must be of type GPy.models.GPClassification"
          )
  
      super().__init__(
          model=model,
          clip_values=clip_values,
          preprocessing_defences=preprocessing_defences,
          postprocessing_defences=postprocessing_defences,
          preprocessing=preprocessing,
      )
      self.nb_classes = 2
  
file_path: adversarial-robustness-toolbox/art/estimators/classification/GPy.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import os
  from typing import List, Optional, Union, Tuple, TYPE_CHECKING
  import numpy
  from art.estimators.classification.classifier import ClassifierClassLossGradients
  from art import config
label_desc: Use image node_id 1 to create a new GPyGaussianProcessClassifier object from inherited base classes: ClassifierClassLossGradients with example: obj = GPyGaussianProcessClassifier(model, clip_values, preprocessing_defences, postprocessing_defences, preprocessing)