class_docstr: A method for determining frequent itemsets in a transactional database and
  also for generating rules for those itemsets. 
  
  Parameters:
  -----------
  min_sup: float
      The minimum fraction of transactions an itemets needs to
      occur in to be deemed frequent
  min_conf: float:
      The minimum fraction of times the antecedent needs to imply
      the concequent to justify rule
class_docstr_tok: ["A","method","for","determining","frequent","itemsets","in","a","transactional","database","and","also","for","generating","rules","for","those","itemsets",".","Parameters",":","--","--","--","--","--","-","min_sup",":","float","The","minimum","fraction","of","transactions","an","itemets","needs","to","occur","in","to","be","deemed","frequent","min_conf",":","float",":","The","minimum","fraction","of","times","the","antecedent","needs","to","imply","the","concequent","to","justify","rule"]
code: def __init__(self, min_sup=0.3, min_conf=0.81):
      self.min_sup = min_sup
      self.min_conf = min_conf
      self.freq_itemsets = None  # List of freqeuent itemsets
      self.transactions = None
  
file_path: ML-From-Scratch/mlfromscratch/unsupervised_learning/apriori.py
imports: from __future__ import division, print_function
  import numpy
  import itertools
label_desc: Use image node_id 1 to create a new Apriori object  with example: obj = Apriori(min_sup, min_conf)