class_docstr: Computes the competence level of the classifiers based on the difference
  between the support obtained by each class. The competence level at a data
  point :math:`\mathbf{x}_{k}` is equal to the minimum difference between
  the support obtained to the correct class and the support obtained for
  different classes.
  
  The influence of each sample xk is defined according to a Gaussian function
  model[2]. Samples that are closer to the query have a higher influence
  in the competence estimation.
  
  Parameters
  ----------
   pool_classifiers : list of classifiers (Default = None)
      The generated_pool of classifiers trained for the corresponding
      classification problem. Each base classifiers should support the method
      "predict". If None, then the pool of classifiers is a bagging
      classifier.
  
  k : int (Default = 7)
      Number of neighbors used to estimate the competence of the base
      classifiers.
  
  DFP : Boolean (Default = False)
      Determines if the dynamic frienemy pruning is applied.
  
  with_IH : Boolean (Default = False)
      Whether the hardness level of the region of competence is used to
      decide between using the DS algorithm or the KNN for classification of
      a given query sample.
  
  safe_k : int (default = None)
      The size of the indecision region.
  
  IH_rate : float (default = 0.3)
      Hardness threshold. If the hardness level of the competence region is
      lower than the IH_rate the KNN classifier is used. Otherwise, the DS
      algorithm is used for classification.
  
  
  mode : String (Default = "selection")
         Whether the technique will perform dynamic selection,
         dynamic weighting or an hybrid approach for classification.
  
  random_state : int, RandomState instance or None, optional (default=None)
      If int, random_state is the seed used by the random number generator;
      If RandomState instance, random_state is the random number generator;
      If None, the random number generator is the RandomState instance used
      by `np.random`.
  
  knn_classifier : {'knn', 'faiss', None} (Default = 'knn')
       The algorithm used to estimate the region of competence:
  
       - 'knn' will use :class:`KNeighborsClassifier` from sklearn
  
       - 'faiss' will use Facebook's Faiss similarity search through the
         class :class:`FaissKNNClassifier`
  
       - None, will use sklearn :class:`KNeighborsClassifier`.
  
  knn_metric : {'minkowski', 'cosine', 'mahalanobis'} (Default = 'minkowski')
      The metric used by the k-NN classifier to estimate distances.
  
      - 'minkowski' will use minkowski distance.
  
      - 'cosine' will use the cosine distance.
  
      - 'mahalanobis' will use the mahalonibis distance.
  
  DSEL_perc : float (Default = 0.5)
      Percentage of the input data used to fit DSEL.
      Note: This parameter is only used if the pool of classifier is None or
      unfitted.
  
  voting : {'hard', 'soft'}, default='hard'
          If 'hard', uses predicted class labels for majority rule voting.
          Else if 'soft', predicts the class label based on the argmax of
          the sums of the predicted probabilities, which is recommended for
          an ensemble of well-calibrated classifiers.
  
  n_jobs : int, default=-1
      The number of parallel jobs to run. None means 1 unless in
      a joblib.parallel_backend context. -1 means using all processors.
      Doesn’t affect fit method.
  
  References
  ----------
  [1] B. Antosik, M. Kurzynski, New measures of classifier competence
  – heuristics and application to the design of
  multiple classifier systems., in: Computer recognition systems
  4., 2011, pp. 197–206.
  
  [2] Woloszynski, Tomasz, and Marek Kurzynski. "A probabilistic model of
  classifier competence for dynamic ensemble selection."
  Pattern Recognition 44.10 (2011): 2656-2668.
docstr: Calculates the source of competence using the
  Minimum Difference method.
  
  The source of competence C_src at the validation point
  :math:`\mathbf{x}_{k}` calculated by the Minimum Difference between
  the supports obtained to the correct class and the support obtained by
  the other classes
  
  Returns
  ----------
  C_src : array of shape (n_samples, n_classifiers)
      The competence source for each base classifier at each data point.
docstr_tok: ["Calculates","the","source","of","competence","using","the","Minimum","Difference","method",".","The","source","of","competence","C_src","at","the","validation","point",":","math",":","`","\\mathbf","{","x","}","_","{","k","}","`","calculated","by","the","Minimum","Difference","between","the","supports","obtained","to","the","correct","class","and","the","support","obtained","by","the","other","classes","Returns","--","--","--","--","--","C_src",":","array","of","shape","(","n_samples",",","n_classifiers",")","The","competence","source","for","each","base","classifier","at","each","data","point","."]
code: def source_competence(self):
      """Calculates the source of competence using the
      Minimum Difference method.
  
      The source of competence C_src at the validation point
      :math:`\\mathbf{x}_{k}` calculated by the Minimum Difference between
      the supports obtained to the correct class and the support obtained by
      the other classes
  
      Returns
      ----------
      C_src : array of shape (n_samples, n_classifiers)
          The competence source for each base classifier at each data point.
      """
      C_src = np.zeros((self.n_samples_, self.n_classifiers_))
      for clf_index in range(self.n_classifiers_):
          supports = self.dsel_scores_[:, clf_index, :]
          C_src[:, clf_index] = min_difference(
              supports, self.DSEL_target_
          )
  
      return C_src
  
imports: import numpy
  from deslib.des.probabilistic import BaseProbabilistic
  from deslib.util import min_difference
label_desc: Use image node_id 2 for calling the MinimumDifference obj's underlying member method code with example usage: obj.source_competence() and returns: C_src