class_docstr: Provides a dictionary that performs fuzzy lookup
docstr: Construct a new FuzzyDict instance
  
  items is an dictionary to copy items from (optional)
  cutoff is the match ratio below which matches should not be considered
  cutoff needs to be a float between 0 and 1 (where zero is no match
  and 1 is a perfect match)
docstr_tok: ["Construct","a","new","FuzzyDict","instance","items","is","an","dictionary","to","copy","items","from","(","optional",")","cutoff","is","the","match","ratio","below","which","matches","should","not","be","considered","cutoff","needs","to","be","a","float","between","0","and","1","(","where","zero","is","no","match","and","1","is","a","perfect","match",")"]
code: def __init__(self, cutoff: float = 0.6):
      """Construct a new FuzzyDict instance
  
      items is an dictionary to copy items from (optional)
      cutoff is the match ratio below which matches should not be considered
      cutoff needs to be a float between 0 and 1 (where zero is no match
      and 1 is a perfect match)"""
      super(FuzzyDict, self).__init__()
      self.cutoff = cutoff
  
      # short wrapper around some super (dict) methods
      self._dict_contains = lambda key: super(
          FuzzyDict, self
      ).__contains__(key)
      self._dict_getitem = lambda key: super(
          FuzzyDict, self
      ).__getitem__(key)
  
file_path: pyecharts/pyecharts/datasets/__init__.py
imports: import difflib
  import os
  import typing
  import urllib.request
  import simplejson
label_desc: Use image node_id 1 to create a new FuzzyDict object from inherited base classes: dict with example: obj = FuzzyDict(cutoff)