From 74963892ae8e2344b58c88264d07bb8b938bbd29 Mon Sep 17 00:00:00 2001
From: wujunjie <297081150@qq.com>
Date: Tue, 29 Oct 2024 09:22:31 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 87 +++++++++++++++++++++++++++++------------------------
 1 file changed, 47 insertions(+), 40 deletions(-)

diff --git a/function.py b/function.py
index 1b8489d..4c3e626 100644
--- a/function.py
+++ b/function.py
@@ -1,6 +1,7 @@
 # function.py
 import numpy as np
-
+from sklearn.preprocessing import StandardScaler  
+from sklearn.metrics import accuracy_score
 """
 实验说明:
 - 自行编写实现数据加载与预测病马死亡的对率回归算法。
@@ -26,64 +27,70 @@ name = "吴俊煯"
 
 
 # 这里写自己的代码实现
-def train_logreg(X, y, learning_rate=0.1, iterations=100):
+def train_logreg(X, y, learning_rate=0.01, iterations=1000, lambda_reg=0.1):
     m, n = X.shape  
     weights = np.zeros(n)  
     bias = 0  
       
     for _ in range(iterations):  
-        for idx, x_i in enumerate(X):  
-            linear_output = np.dot(x_i, weights) + bias  
-            y_predicted = 1 / (1 + np.exp(-linear_output))  
-              
-            # 计算误差  
-            error = y[idx] - y_predicted  
-              
-            # 更新权重和偏置  
-            weights += learning_rate * error * x_i  
-            bias += learning_rate * error  
+        linear_output = np.dot(X, weights) + bias  
+        y_predicted = 1 / (1 + np.exp(-linear_output))  
+          
+        # 计算梯度（包括正则化项）  
+        dw = (1 / m) * np.dot(X.T, (y_predicted - y)) + (lambda_reg / m) * weights  
+        db = (1 / m) * np.sum(y_predicted - y)  
+          
+        # 更新权重和偏置  
+        weights -= learning_rate * dw  
+        bias -= learning_rate * db  
       
-    return weights    
+    return weights, bias    
 
-def predict_logreg(inX, weights):
-    linear_output = np.dot(inX, weights)  
-    y_predicted = 1 / (1 + np.exp(-linear_output))  
-      
-    # 将输出转换为分类标签  
-    return 1 if y_predicted >= 0.5 else 0 
+def predict_logreg(inX, weights, bias):
+    linear_output = np.dot(inX, weights) + bias  
+    y_predicted = 1 if linear_output >= 0 else 0  # 简化阈值判断，因为已经通过sigmoid函数输出  
+    # 注意：这里为了与原始代码保持一致，使用了0.5作为阈值，但实际上由于sigmoid输出范围在(0,1)，可以直接比较linear_output和0  
+    # return 1 if y_predicted >= 0.5 else 0  # 等价于上面的判断，但多余计算了sigmoid  
+    return y_predicted 
 
 # 这里也写自己的代码实现
 def load_data(filepath):
     data = []  
     with open(filepath, 'r') as file:  
         for line in file:  
-            # 分割行，并去除任何可能的空白字符  
-            parts = [part.strip() for part in line.split()]  
-            # 将缺失值'?'替换为0（或者你可以选择其他适当的处理方式）  
-            features = [float(part) if part != '?' else 1 for part in parts[:-1]]  
-            # 将标签从2转换为1（如果原始标签是1和2的话）  
-            label = int(parts[-1]) - 1  # 假设原始标签是1和2，转换为0和1  
+            parts = line.strip().split()  
+            # 将缺失值'?'替换为np.nan，然后使用0填充（或选择其他适当的填充策略）  
+            features = [float(part) if part != '?' else np.nan for part in parts[:-1]]  
+            # 填充NaN值（这里选择0，但也可以考虑使用均值、中位数等）  
+            features = np.nan_to_num(features, nan=0.0)  
+            # 将标签从2转换为1（如果原始标签是1和2的话），这里假设标签已经是0和1  
+            label = int(parts[-1])  # 假设标签已经是0和1，无需转换  
             data.append(features + [label])  
-      
-    # 将数据转换为numpy数组  
+  
     data_array = np.array(data)  
+    X = data_array[:, :-1]  
+    y = data_array[:, -1]  
       
-    # 分离特征和标签  
-    X = data_array[:, :-1]  # 所有行，除了最后一列  
-    y = data_array[:, -1]   # 所有行的最后一列  
+    # 特征缩放（标准化）  
+    scaler = StandardScaler()  
+    X_scaled = scaler.fit_transform(X)  
       
-    return X, y
+    return X_scaled, y
 
 if __name__ == "__main__":
-    # 加载训练集  
+    # 加载训练集和测试集  
     X_train, y_train = load_data("./data/horse-colic.data")  
-  
-    # 加载测试集  
     X_test, y_test = load_data("./data/horse-colic.test")  
-  
+      
     # 训练对率回归模型  
-    weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000)  
-  
-    # 测试单个数据  
-    y_pred = predict_logreg(X_test[11], weights)  
-    print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")
\ No newline at end of file
+    weights, bias = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000, lambda_reg=0.1)  
+      
+    # 测试模型并评估性能  
+    y_pred = [predict_logreg(x, weights, bias) for x in X_test]  
+    accuracy = accuracy_score(y_test, y_pred)  
+    print(f"模型准确率: {accuracy}")  
+      
+    # 打印单个测试样本的预测结果和实际结果（示例）  
+    # 注意：索引11可能需要根据实际测试集调整，这里仅作为示例  
+    sample_index = 11  # 假设要测试的样本索引是11（注意：索引可能需要根据实际数据调整）  
+    print(f"预测结果: {y_pred[sample_index]}, 实际结果: {y_test[sample_index]}")
\ No newline at end of file