from indexing import create_index create_index()
sys.exit(2)
query = raw_input("Please enter the search string: ") ## Expect exactly 2 arguments: the search query and number of clusters
k = raw_input("Please enter the number of clusters: ")
k=int(k)
## query = "jaguar"
start = time.clock()
## k=7
interim_path = extract_results.get_search_results(query) ## extract search results
parsing_search_results.parse_file(interim_path) ## parse the serach results
title_dict = pickle.load(open("title_dict","rb")) ## unpickle the document dictionaries
desc_dict = pickle.load(open("desc_dict","rb"))
url_dict = pickle.load(open("url_dict","rb"))
indexing.create_index(title_dict,desc_dict,url_dict) ## create an index
index = pickle.load(open("index","rb")) ## unpickle the index dictionaries
np_ind = pickle.load(open("np_ind","rb"))
stem_dict = pickle.load(open("stem_dict","rb"))
indexing.calc_tf_idf(title_dict,index,np_ind) ## calculate the tf-idf values for the document vector
doc_word_dict = pickle.load(open("doc_word_dict","rb")) ## unpickle the tf-idf dictionaries representing document vectors
doc_np_dict = pickle.load(open("doc_np_dict","rb"))
norm_doc_word_dict = clustering.normalize_doc_dict(doc_word_dict) ## normalize the document vector
dij = clustering.calc_eucl_dist(norm_doc_word_dict) ## calculate the eucledian distance
clustering.get_mediods(k,dij) ## use k-mediods to get the clusters
cluster = pickle.load(open("cluster","rb")) ## unpickle the clusters
label_dict = labeling.label(cluster,doc_np_dict, stem_dict,np_ind,query) ## label the clusters
import math
import random
import re
from xml.dom.minidom import parse, parseString
import xml.dom.minidom as minidom
from collections import OrderedDict,defaultdict
import pickle
from sets import Set
from operator import itemgetter
import os
import nltk
from nltk.tokenize.regexp import RegexpTokenizer
import time
import Queue
import parsing
import indexing
if __name__ == "__main__":
if len(sys.argv)!=2: # Expect exactly 1 argument
sys.exit(2)
path1 = sys.argv[1]
parsing.parse_file(path1)
indexing.create_index()
###! /usr/bin/python
import sys
import math
import random
import re
from collections import OrderedDict
import os
import pickle
import time
import nltk
from nltk.stem.porter import PorterStemmer
import itertools
import indexing
import train
if __name__ == "__main__":
if len(sys.argv) != 2: # Expect exactly 1 argument: the training data file
sys.exit(2)
input1 = file(sys.argv[1], "r")
indexing.create_index(input1) ## this function will create dp the basic pre-processing and create the index
train.train_classifier() ## this function will calculate the model parameters for the classifier
