import sklearn
from sklearn import linear_model, tree

# 普通最小二乘法
reg = linear_model.LinearRegression()
reg.fit([[0, 0], [1, 1], [2, 2]], [0, 1, 2])
result = reg.predict([[3, 1]])
print(reg.coef_)
print(result)

# 贝叶斯岭回归
X = [[0., 0.], [1., 1.], [2., 2.], [3., 3.]]
Y = [0., 1., 2., 3.]
reg = linear_model.BayesianRidge()
reg.fit(X, Y)
result = reg.predict([[1, 0]])
print(reg.coef_)
print(result)

# 决策树分类
X = [[0, 0], [1, 1]]
Y = [0, 1]
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, Y)
result = clf.predict([[10., 11.]])
print(result)
print(clf.predict_proba([[10., 11.]]))
# 决策树回归
X = [[0, 0], [2, 2]]
Y = [1.5, 6.5]
clf = tree.DecisionTreeRegressor()
clf = clf.fit(X, Y)
result = clf.predict([[1, 2]])
print(result)