Add. Training LinearRegression

机器学习算法理论及应用/第二章——手写线性回归算法/LinearRegression/UnivariateLinearRegression.py

@@ -26,5 +26,18 @@ plt.title('Happy')
 plt.legend()
 plt.show()
 
-num_iterations = 500
-learning_rate = 0.01
+# 训练线性回归模型
+num_iterations = 500  # 迭代次数
+learning_rate = 0.01  # 学习率
+
+linear_regression = LinearRegression(x_train, y_train)  # 初始化模型
+(theta, cost_history) = linear_regression.train(learning_rate, num_iterations)
+
+print('开始时的损失：', cost_history[0])
+print('训练后的损失：', cost_history[-1])
+
+plt.plot(range(num_iterations), cost_history)
+plt.xlabel('Iteration')
+plt.ylabel('Cost')
+plt.title('GD')
+plt.show()