code: def ruleMatch(rule, featureVector):
      """Determine if the passed featureVector matches the passed rule"""
      if False:
          print("ruleMatch called, ", end="")
          printRule(rule)
          print(" feature vector: " + featuresToString(featureVector))
  
      for i in range(0, 6):  # loop over first 6 features, 0..5
          if not match(
              rule[i], featureVector[i]
          ):  # if we don't find a feature match, the rule doesn't match
              # print("Didn't match feature #", i, ruleValToString(featureVector[i]))
              return False
  
      # These features are interval-based, so need a different matching routine
      if not intervalMatch(
          rule[6], rule[7], featureVector[6]
      ):  # rule[6] and rule[7] have the lower and upper bounds of interval
          # print("Didn't match feature # 6: ", featureVector[6])
          return False
      if not intervalMatch(
          rule[8], rule[9], featureVector[7]
      ):  # rule[8] and rule[9] have the lower and upper bounds of interval
          # print("Didn't match feature # 7: ", featureVector[7])
          return False
  
      # print("Matched all features")
      return True
  
file_path: AIX360/aix360/data/ted_data/GenerateData.py
imports: import random
  from random import choices
  import pandas
label_desc: Use image node_id 11 for calling a global function with example usage: ruleMatch(rule, featureVector) and returns: True, False, False, False