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regression_example.py

+import numpy as np
+from sklearn.datasets import fetch_california_housing
+from sklearn.model_selection import train_test_split
+# from sklearn.metrics import r2_score
+from metrics import r2_score
+from linear_regression import LinearRegression_Equation, LinearRegression_GD
+
+if __name__ == '__main__':
+    X, y = fetch_california_housing(return_X_y=True)
+    # 归一化
+    X = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
+    # 加上偏置b
+    X = np.c_[np.ones((len(X), 1)), X]
+    # 将数据集分为训练集和测试集
+    X_train, X_test, y_train, y_test = train_test_split(X, y)
+
+    # 正规方程法
+    model1 = LinearRegression_Equation()
+    model1.fit(X_train, y_train)
+    y_pred = model1.predict(X_test)
+    score = r2_score(y_test, y_pred)
+    print('Equation of linearregression score is {:.3f}'.format(score))
+
+    # 梯度下降法
+    model2 = LinearRegression_GD(20000, 0.005)
+    model2.fit(X_train, y_train)
+    y_pred = model2.predict(X_test).flatten()
+    score = r2_score(y_test, y_pred)
+    print('gradient descent of linearregression score is {:.3f}'.format(score))
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