def Build_TF_IDF(self):
for i in range(self.Total_Mails):
Msg = ProcessData.Process_Msg(self.mails[i])
count = list()
for word in Msg:
if self.labels[i]:
self.TF_Spam[word] = self.TF_Spam.get(word, 0) + 1
else:
self.TF_Ham[word] = self.TF_Ham.get(word, 0) + 1
if word not in count:
count += [word]
for word in count:
if self.labels[i]:
self.IDF_Spam[word] = self.IDF_Spam.get(word, 0) + 1
else:
self.IDF_Ham[word] = self.IDF_Ham.get(word, 0) + 1
def Predict(self, test_data):
result = dict()
for (i, message) in enumerate(test_data):
msg = ProcessData.Process_Msg(message)
result[i] = int(self.Classify(msg))
return result
