machine "svm"

from sklearn import svm
import random as rd

train_data  = list()
result_data = list()

for _ in range(100):
    train_data.append([rd.uniform(1., 1.9) for _ in range(2)])
    result_data.append(1)

for _ in range(100):
    train_data.append([rd.uniform(3., 3.9) for _ in range(2)])
    result_data.append(3)

print (train_data)
# 객체 생성 _________________
clf = svm.SVC()
clf.fit(train_data, result_data)
print (clf.predict([[2.1, 3.1]]))