1 def test_fj():
 2     X = np.array([[500, 3, 0.3], [1000, 1, 0.6], [750, 2, 0.3], [600, 5, 0.2], [1200, 1, 0.6]], dtype=float)
 3     Y = np.array([10000, 9000, 8000, 12000, 8500], dtype=float)
 4 
 5     x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.25)
 6     print(x_train, x_test)
 7     print('===================================================
 
')
 8     print(y_train, y_test)
 9 
10     std_x = StandardScaler()
11     x_train = std_x.fit_transform(x_train)
12     x_test = std_x.transform(x_test)
13 
14     std_y = StandardScaler()
15     y_train = std_y.fit_transform(y_train.reshape(-1, 1))
16     y_test = std_y.transform(y_test.reshape(-1, 1))
 
17 
18     lr = LinearRegression()
19     lr.fit(x_train, y_train)
20     print(lr.coef_)
21 
22     # orign = std_y.inverse_transform(y_test[1])
23     # print('orign is value:::::',orign)
24     # y_lr_predict = std_y.inverse_transform(lr.predict(np.array([x_test[1]])))
25     y_lr_predict = std_y.inverse_transform(lr.predict(x_test))
 
26 
27     print('房價：', y_lr_predict)
28     print('評分：', r2_score(std_y.inverse_transform(y_test), y_lr_predict))
29 
30 
31 def price_predict():
32     # 資料有三個特徵：距離地鐵距離、附近小學、小區綠化率
33     X = np.array([[500, 3, 0.3], [1000, 1, 0.6], [750, 2, 0.3], [600, 5, 0.2], [1200, 1, 0.6]], dtype=float)
34     # 具有三個特徵的房屋對應的房價
35     Y = np.array([10000, 9000, 8000, 12000, 8500], dtype=float)
36 
37     std_x = StandardScaler()
38     x_train = std_x.fit_transform(X)
39 
40     std_y = StandardScaler()
41     y_train = std_y.fit_transform(Y.reshape(-1, 1))
42     # 構建線性預測模型
43     lr = LinearRegression()
44     # 模型在歷史資料上進行訓練，Y.reshape(-1,1)將Y變為二維陣列，fit函式引數要求是二維陣列
45     lr.fit(x_train, y_train.reshape(-1, 1))
46     # 使用訓練模型預測新房屋價格
47     distance = input('請輸入新房屋距離地鐵的距離：')
48     school = input('請輸入附近小學數量：')
49     green = input('請輸入小區綠化率：')
50     x_predict = std_x.transform(np.array([[distance, school, green]], dtype=float))
51     print(std_y.inverse_transform(lr.predict(x_predict)))
52     # print(lr.predict(np.array([[distance, school, green]], dtype=float)))
53     # print(lr.predict(np.array([[1300, 3, 0.4]])))
54 
55 
56 if __name__ == '__main__':
57     pairplot_analyse()
58     # heatmap_analyse()
59     # bostn_linear()
60     # log_fit()
61     # test_fj()
62     # price_predict()
63     pass