def similar(docSet, InvIndex, word):
textS = []
wordCol = []
if word not in InvIndex:
print ""
print "Word is not in the index."
print ""
return
for doc in docSet:
if doc in InvIndex[word]:
textS = stemmer(
filterData(
tokenize(
lowercase(parseXML(InvIndex["path_of_documents"] + "/" + "cranfield" + zfill(str(doc), 4)))
)
)
)
textS = [t for t in textS if t != ""]
while word in textS and len(wordCol) < 1000:
index = textS.index(word)
textS[index] = ""
wordCol.extend(textS[(index + 1) % len(textS) : (index + 4) % len(textS)])
wordCol.extend(textS[(index - 4) % len(textS) : (index - 1) % len(textS)])
result = sorted(set(wordCol), key=wordCol.count, reverse=True)
if len(result) < 10:
print ""
print "List of Similar terms (in stemmed form) is"
print result
print ""
else:
print ""
print "List of Similar terms (in stemmed form) is"
print result[:10]
print ""
def MakeIndex(path):
InvertedIndex = {}
InvertedIndex['path_of_documents'] = path
if exists(path):
textFiles = [f for f in listdir(path)]
else:
print path,'is not a valid path, exiting...'
exit()
for file in textFiles:
text = parseXML( path + "/" + file )
text = lowercase(text)
text = tokenize(text)
text = filterData(text)
text = stemmer(text)
InvertedIndex = invertedListAppend( text, file, InvertedIndex )
return InvertedIndex
def phraseSearch(phrase, wordSet, docSet, InvIndex):
words = phrase.strip().strip('"').split()
words = stemmer(words)
phraseLen = len(words)
if not set(words).issubset(wordSet):
return {}
if len(words) == 0:
return {}
word1 = words[0]
words.remove(word1)
mergeList1 = InvIndex[word1]
while words:
word2 = words[0]
words.remove(word2)
mergeList2 = InvIndex[word2]
mergeList1 = phraseMerge(mergeList1, mergeList2)
for keys, values in mergeList1.items():
mergeList1[keys] = phraseLen * len(values)
return mergeList1
from Document import lowercase, tokenize, filterData, printDocument, getSnippets, printDoc, printTitle, stemmer
if __name__ == '__main__':
#wordset is the set of all unique words in index, docset is the set of all documents, invl is the inverted list
wordSet,docSet,invl = preprocess()
while True:
print 'Enter search string: (Empty string to exit)'
searchString = raw_input()
searchString = searchString.strip()
if searchString == '':
break
#seperate the individual words or phrases in the searchString
parsedSearch = parseSearchString(searchString)
parsedSearch = stemmer(parsedSearch)
searchString = ' '.join(parsedSearch)
#Checking for the type of query desired by the user
if parsedSearch[0] == 'df':
print ''
print 'Document Frequency of \'',searchString[3:],'\' is', df(searchString[3:], wordSet, docSet, invl)
print ''
continue
elif parsedSearch[0] == 'tf':
newSearch = ' '.join(parsedSearch[2:])
if len(parsedSearch) == 1:
print ''
print 'Please name of document after tf.....'
print ''
continue
print ''
