code: def matthews_correlation_coefficient(self, idx):
      tp = self.true_positives(idx)
      tn = self.true_negatives(idx)
      fp = self.false_positives(idx)
      fn = self.false_negatives(idx)
      nom = tp * tn - fp * fn
      den = np.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
      if den == 0 or den == np.nan:
          return 0
      else:
          return nom / den
  
imports: import logging
  from collections import OrderedDict
  import numpy
  from sklearn import metrics
  from sklearn.metrics import confusion_matrix
label_desc: Use image node_id 25 for calling the ConfusionMatrix obj's underlying member method code with example usage: obj.matthews_correlation_coefficient(idx) and returns: int, unknown