def classify(filename):
textData=featureSelected.featureSelect(filename)
label=getLabel(textData)
#print 'classify',label
if label==1:
print "类别：财经"
return '财经'
elif label==2:
print "类别:IT"
return 'IT'
elif label==3:
print "类别：旅游"
return '旅游'
elif label==4:
print "类别：体育"
return '体育'
def TextToVector(filename):
text = featureSelected.featureSelect(filename)
# print json.dumps(text,encoding='UTF-8',ensure_ascii=False)
f = open("../data/trainData/Dict/Chi_Feature.txt", "rb")
Dict = pickle.load(f)
f.close()
# print json.dumps(Dict,encoding='UTF-8',ensure_ascii=False)
DICT = []
for i in range(len(Dict)):
DICT.append(Dict[i][0])
tmp = []
print "lenth of DICT ", len(DICT)
for i in range(len(DICT)):
if DICT[i] in text:
tmp.append((i + 1, text[DICT[i]]))
if len(tmp) == 0:
print "len of tmp in VSM = 0"
return tmp
def classify2(filename):
textData=featureSelected.featureSelect(filename)
label=getLabel(textData)
return label
#print 'logJoint',logJoint
return label
def classify(filename):
textData=featureSelected.featureSelect(filename)
label=getLabel(textData)
#print 'classify',label
if label==1:
print "类别：财经"
return '财经'
elif label==2:
print "类别:IT"
return 'IT'
elif label==3:
print "类别：旅游"
return '旅游'
elif label==4:
print "类别：体育"
return '体育'
def classify2(filename):
textData=featureSelected.featureSelect(filename)
label=getLabel(textData)
return label
if __name__=='__main__':
#Bayes_FeatureP()
filename=('/Thu_Life/CS/SVM/data/trainData/Data/class2/1246.txt')
classify(filename)
text=featureSelected.featureSelect(filename)
#print json.dumps(text,encoding='UTF-8',ensure_ascii=False)
def classify(filename):
textData=featureSelected.featureSelect(filename)
label=getLabel(textData)
print 'classify',label
