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import sys,os,re
import math 
import sqlite3 
import time

# the database is a simple dictionnary 
database = {}

# regular expression for: extract words, extract ID from path, check for hexa value 
chars = re.compile(r'\W+')
pattid= re.compile(r'(\d{3})/(\d{3})/(\d{3})')

# the higher ID 
tokens = 0
documents = 0
terms = 0

# We will create a term object for each unique instance of a term
class Term():
    termid = 0
    termfreq = 0
    docs = 0 
    docids = {}

# split on any chars 
def splitchars(line) :
    return chars.split(line)

# process the tokens of the source code 
def parsetoken(line):
    global documents 
    global tokens 
    global terms

    # this replaces any tab characters with a space character in the line
    # read from the file
    line = line.replace('\t',' ') 
    line = line.strip()
    # This routine splits the contents of the line into tokens 
    l = splitchars(line)
    # for each token in the line process 
    for elmt in l:
        # This statement removes the newline character if found 
        elmt = elmt.replace('\n','')
        # This statement converts all letters to lower case 
        lowerElmt = elmt.lower().strip()
        # Increment the counter of the number of tokens processed. This value will
        # provide the total size of the corpus in terms of the number of terms in the
        # entire collection
        tokens += 1
        # if the term doesn't currently exist in the term dictionary
        # then add the term
        if not (lowerElmt in database.keys()):
            database[lowerElmt] = Term()
            database[lowerElmt].termid = terms 
            database[lowerElmt].docids = dict()
            database[lowerElmt].docs = 0
            # if the document is not currently in the postings
            # list for the term then add it
        if not (documents in database[lowerElmt].docids.keys()):
            database[lowerElmt].docs += 1
            database[lowerElmt].docids[documents] = 0
    # Increment the counter that tracks the term frequency 
        database[lowerElmt].docids[documents] += 1
    return l

# Open and read the file line by line, parsing for tokens and processing. All of the tokenizing
# is done in the parsetoken() function. You should design your indexer program keeping the tokenizing
# as a separate function that will process a string as you will be able to reuse code for
# future assignments
def process(filename): 
        file = open(filename, 'r') 
    except IOError:
        print("Error in file %s" % filename) 
        return False
        for l in file.readlines(): 

# This function will scan through the specified directory structure selecting
# every file for processing by the tokenizer function
# Notices how this is a recursive function in that it calls itself to process
# sub-directories.
def walkdir(cur, dirname): 
    global documents 
    all = {}
    all = [f for f in os.listdir(dirname) if os.path.isdir(os.path.join(dirname, f)) or os.path.isfile(os.path.join(dirname, f))] 
    for f in all:
        if os.path.isdir(dirname + '/' + f): 
            walkdir(cur, dirname + '/' + f)
            documents += 1
            cur.execute("insert into DocumentDictionary values (?, ?)", (dirname+'/'+f, documents)) 
            process(dirname + '/' + f)
    return True

def get_cursor():
    conn = sqlite3.connect("indexer.db")
    return conn.cursor()
def select_all_records_by_author(cursor):
    sql = "SELECT * FROM DocumentDictionary"
    print(cursor.fetchall())  # or use fetchone()
    print("\nHere is a listing of the rows in the table DocumentDictionary\n")
    for row in cursor.execute("SELECT rowid, * FROM DocumentDictionary"):

>>> main

This section is the 'main' or starting point of the indexer program. The python interpreter will find this 'main' routine and execute it first.

if __name__ == '__main__':
    # Capture the start time of the routine so that we can determine the total running
    # time required to process the corpus
    t2 = time.localtime()
    print ("Start Time: %.2d:%.2d" % (t2.tm_hour, t2.tm_min))
    # The corpus of documents must be extracted from the zip file and placed into the C:\corpus
    # directory or another directory that you choose. If you use another directory make sure that
    # you point folder to the appropriate directory.
    # SPECIAL NOTE: Always place your code and corpus folder (cacm folder) in same folder
    folder = "C:\Users\datai\cacm" #PATH TO BE EDITED
    # Create a sqlite database to hold the inverted index. The isolation_level statment turns
    # on autocommit which means that changes made in the database are committed automatically
    con = sqlite3.connect("indexer_part2.db") #This create database in the folder where your code file is saved.
    con.isolation_level = None
    cur = con.cursor()
    # In the following section three tables and their associated indexes will be created.
    # Before we create the table or index we will attempt to drop any existing tables in
    # case they exist
    # Document Dictionary Table
    cur.execute("drop table if exists DocumentDictionary") 
    cur.execute("drop index if exists idxDocumentDictionary")
    cur.execute("create table if not exists DocumentDictionary (DocumentName text, DocId int)") 
    cur.execute("create index if not exists idxDocumentDictionary on DocumentDictionary (DocId)")
    # Term Dictionary Table
    cur.execute("drop table if exists TermDictionary") 
    cur.execute("drop index if exists idxTermDictionary")
    cur.execute("create table if not exists TermDictionary (Term text, TermId int)") 
    cur.execute("create index if not exists idxTermDictionary on TermDictionary (TermId)")
    # Postings Table
    cur.execute("drop table if exists Posting") 
    cur.execute("drop index if exists idxPosting1") 
    cur.execute("drop index if exists idxPosting2")
    cur.execute("create table if not exists Posting (TermId int, DocId int, tfidf real, docfreq int, termfreq int)") 
    cur.execute("create index if not exists idxPosting1 on Posting (TermId)")
    cur.execute("create index if not exists idxPosting2 on Posting (Docid)")
    # The walkdir method essentially executes the indexer. The walkdir method will
    # read the corpus directory, Scan all files, parse tokens, and create the inverted index.
    walkdir(cur, folder)
    t2 = time.localtime()
    print("Indexing Complete, write to disk: %.2d:%.2d" % (t2.tm_hour, t2.tm_min))

    # Create the inverted index tables.
    # Insert a row into the TermDictionary for each unique term along with a termid which is
    # a integer assigned to each term by incrementing an integer
    # Insert a row into the posting table for each unique combination of Docid and termid
    # The following execute statement will show all the values inserted in TermDictionary table.
    print("The content of TermDictionary table are as follows:")
    cur.execute("select * from TermDictionary")

    # Commit changes to the database and close the connection

    # Print processing statistics
    print ("Documents %i" % documents)
    print ("Terms %i"% terms)
    print ("Tokens %i" % tokens)
    t2 = time.localtime()
    print ("End Time: %.2d:%.2d" % (t2.tm_hour, t2.tm_min))
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