"""

Given a list of fully-qualified filenames, build an index from word

to set of document IDs. A document ID is just the index into the

files parameter (indexed from 0) to get the file name. Make sure that

you are mapping a word to a set of doc IDs, not a list.

For each word w in file i, add i to the set of document IDs containing w

Return a dict object mapping a word to a set of doc IDs.

"""

dct_index = defaultdict() # Create an empty dict

for idx,fname in enumerate(files): # Iterate through every given file names

s_content = get_text(fname) # Turn each file name into a string content

lst_word = words(s_content) # Turn the string content into a list of normalized words

for word in lst_word: # For each normalized words, update the dict by word-file as key-value pairs

if word not in dct_index:

dct_index[word] = {idx} # If the key doesn't exist, create one

else:

dct_index[word].add(idx) # If the key exist, add the file name into the set of the file names under that word

"""

Given an index and a list of fully-qualified filenames, return a list of

filenames whose file contents has all words in terms parameter as normalized

by your words() function. Parameter terms is a list of strings.

You can only use the index to find matching files; you cannot open the files

and look inside.

"""

# index is a dictionary, containg words mapping to its file IDs

set_file = set(files) # files is a list of file names, not file IDs

for term in terms:

if term not in index: # Edge case

return []

# Turn the IDs into file names

set_IDs = index[term]

lst_fnames_of_the_term = []

for ID in set_IDs:

# Get back the file names by ID, the index of the files (they are ordered in the list)

lst_fnames_of_the_term.append(files[ID])

set_file = set_file.intersection(set(lst_fnames_of_the_term)) # The set will getting smaller under each loop