class VectorSpaceModel():
def __init__(self, N):
self.frequency = Frequency()
self.noOfDocs = N
def userInterface(self):
op = '1'
while (op != '0'):
print("vector Space Model")
print("-----------------------------")
print("1. Execute Query")
print("0. Exit")
op = input("Enter input: ")
self.inputQuery(op)
def inputQuery(self, op):
if op == '1':
query = input("Enter query: ")
queryArr = query.split(" ")
self.data = self.createTable()
qVector = self.getVector(queryArr)
docVectors = self.getDocumentVectors()
# print('q= ', qVector)
# print('docs = ', docVectors)
rankings = self.generateRankings(docVectors, qVector)
print(self.formatRankings(rankings))
else:
return
def formatRankings(self, rankings):
rankings = rankings.loc[rankings['sim'] > 0.005]
return rankings.sort_values(by=['sim'], ascending=False)
def generateRankings(self, docs, q):
rankings = pd.DataFrame({
'docs': [str(x) + '.txt' for x in range(1, self.noOfDocs + 1)],
'sim': [self.sim(docs[i], q) for i in range(1, self.noOfDocs + 1)]
})
return rankings
def sim(self, d, q):
x = np.array(d)
y = np.array(q)
modX = sum(x * x)**0.5
modY = sum(y * y)**0.5
return sum(x * y) / (modX * modY)
def createTable(self):
self.frequency.loadDocuments()
self.frequency.buildDictionary()
# data = pd.DataFrame({
# 'words': self.frequency.getWords(),
# 'idf': self.frequency.getIdf()
# })
keys = self.frequency.getWords()
values = self.frequency.getIdf(self.noOfDocs)
data = dict(zip(keys, values))
# print('data: ', data)
return data
def getVector(self, array, docId=0):
vector = []
for word, idf in self.data.items():
if word in array:
if (docId == 0):
# docId=0 means getting vector for query
tf = self.getQueryFrequency(array)[word]
else:
tf = self.frequency.getTermFrequency(word)[docId]
vector.append(self.tf_idf(tf, idf))
else:
vector.append(0)
return vector
def getDocumentVectors(self):
docVectors = {}
docId = 1
for i in range(self.noOfDocs):
doc = self.frequency.collection[i]
docVectors[docId] = self.getVector(doc, docId)
docId += 1
return docVectors
def getQueryFrequency(self, queryArr):
tf = {}
for q in queryArr:
if q not in tf:
tf[q] = 1
else:
tf[q] = tf[q] + 1
return tf
def tf_idf(self, tf, idf):
return tf * idf
