def usekNN(path): print "step1: load data..." train_x, train_y = loadDataSet() print "step 2: caculating..." items = kNNClassify(handle(path), train_x, train_y, 3) pitems = kNNClassify(handle(path), train_x, train_y, 50) print "the answer is:", print items[0][0] print "the possible answers are(include answer):", for i, item in enumerate(pitems): print item[0], print ""
def useSVM(path): print "step 1: load data..." train_x, train_y, test_x, test_y = loadDataSetForTest() print "step 2: train..." m = SVMTrain(train_y, train_x) print "step 3: caculating..." print "the answer is:", print SVMPredict([], handle(path), m, False)[0]
def useBayes(path): print "step1: load data..." train_x, train_y = loadDataSet() print "step 2: train..." pv, pa = trainNB0(train_x, train_y) print "step 3: caculating..." items = classifyNB(handle(path), pv, pa) print "the answer is:", print items[0][0] print "the possible answers are(include answer):", for i, item in enumerate(items): print item[0], print ""