35 lines
772 B
Python
35 lines
772 B
Python
import sklearn
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from sklearn import linear_model, tree
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# 普通最小二乘法
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reg = linear_model.LinearRegression()
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reg.fit([[0, 0], [1, 1], [2, 2]], [0, 1, 2])
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result = reg.predict([[3, 1]])
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print(reg.coef_)
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print(result)
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# 贝叶斯岭回归
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X = [[0., 0.], [1., 1.], [2., 2.], [3., 3.]]
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Y = [0., 1., 2., 3.]
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reg = linear_model.BayesianRidge()
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reg.fit(X, Y)
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result = reg.predict([[1, 0]])
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print(reg.coef_)
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print(result)
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# 决策树分类
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X = [[0, 0], [1, 1]]
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Y = [0, 1]
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clf = tree.DecisionTreeClassifier()
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clf = clf.fit(X, Y)
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result = clf.predict([[10., 11.]])
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print(result)
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print(clf.predict_proba([[10., 11.]]))
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# 决策树回归
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X = [[0, 0], [2, 2]]
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Y = [1.5, 6.5]
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clf = tree.DecisionTreeRegressor()
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clf = clf.fit(X, Y)
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result = clf.predict([[1, 2]])
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print(result)
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