Add. Loss and forecast module

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

@@ -32,7 +32,7 @@ class LinearRegression:
 
     def train(self, alpha, num_iterations=500):
         """
-        训练模块，执行梯度下降
+        训练模块，执行梯度下降得到theta值和损失值loss
 
         alpha: 学习率
         num_iterations: 迭代次数
@@ -46,10 +46,12 @@ class LinearRegression:
 
         alpha: 学习率
         num_iterations: 迭代次数
+
+        :return: 返回损失值 loss
         """
-        cost_history = []
+        cost_history = []  # 收集每次的损失值
         for _ in range(num_iterations):  # 开始迭代
-            self.gradient_step(alpha)
+            self.gradient_step(alpha)  # 每次更新theta
             cost_history.append(self.cost_function(self.data, self.labels))
         return cost_history
 
@@ -68,6 +70,15 @@ class LinearRegression:
         theta = theta - alpha * (1/num_examples)*(np.dot(delta.T, self.data)).T
         self.theta = theta  # 计算完theta后更新当前theta
 
+    def cost_function(self, data, labels):
+        """
+        损失计算方法，计算平均的损失而不是每个数据的损失值
+        """
+        num_examples = data.shape[0]
+        delta = LinearRegression.hypothesis(data, self.theta) - labels  # 预测值-真实值 得到残差
+        cost = np.dot(delta, delta.T)  # 损失值
+        return cost[0][0]
+
     @staticmethod
     def hypothesis(data, theta):
         """
@@ -79,3 +90,25 @@ class LinearRegression:
         """
         predictions = np.dot(data, theta)
         return predictions
+
+    def get_cost(self, data, labels):
+        """
+        得到当前损失
+        """
+        data_processed = prepare_for_training.prepare_for_training(data,
+                                                                   self.polynomial_degree,
+                                                                   self.sinusoid_degree,
+                                                                   self.normalize_data)[0]
+        return self.cost_function(data_processed, labels)
+
+    def predict(self, data):
+        """
+        用训练的参数模型，预测得到回归值的结果
+        """
+        data_processed = prepare_for_training.prepare_for_training(data,
+                                                                   self.polynomial_degree,
+                                                                   self.sinusoid_degree,
+                                                                   self.normalize_data)[0]
+        predictions = LinearRegression.hypothesis(data_processed, self.theta)
+
+        return predictions