code: def do_naive_bayes_prediction(
      x, observed_class_distribution: dict, splitters: dict
  ):
      """Perform Naive Bayes prediction
  
      Parameters
      ----------
      x
          The feature values.
  
      observed_class_distribution
          Observed class distribution.
  
      splitters
          Attribute (features) observers.
  
      Returns
      -------
      The probabilities related to each class.
  
      Notes
      -----
      This method is not intended to be used as a stand-alone method.
      """
      total_weight = sum(observed_class_distribution.values())
      if not observed_class_distribution or total_weight == 0:
          # No observed class distributions, all classes equal
          return {}
  
      votes = {}
      for (
          class_index,
          class_weight,
      ) in observed_class_distribution.items():
          # Prior
          if class_weight > 0:
              votes[class_index] = math.log(class_weight / total_weight)
          else:
              votes[class_index] = 0.0
              continue
  
          if splitters:
              for att_idx in splitters:
                  if att_idx not in x:
                      continue
                  obs = splitters[att_idx]
                  # Prior plus the log likelihood
                  tmp = obs.cond_proba(x[att_idx], class_index)
                  votes[class_index] += (
                      math.log(tmp) if tmp > 0 else 0.0
                  )
  
      # Max log-likelihood
      max_ll = max(votes.values())
      # Apply the log-sum-exp trick (https://stats.stackexchange.com/a/253319)
      lse = max_ll + math.log(
          sum(
              math.exp(log_proba - max_ll)
              for log_proba in votes.values()
          )
      )
  
      for class_index in votes:
          votes[class_index] = math.exp(votes[class_index] - lse)
  
      return votes
  
imports: from __future__ import annotations
  import collections
  import copy
  import dataclasses
  import functools
  import math
  import sys
  import typing
  from river.base.typing import FeatureName
  from river.stats import Cov, Var
label_desc: Use image node_id 1 for calling a global function with example usage: do_naive_bayes_prediction(x, observed_class_distribution, splitters) and returns: votes, dict