更新 function.py

function.py

@@ -27,8 +27,6 @@ id = "202210423629"
 name = "Tanjunfeng"
 
 # 这里写自己的代码实现
-import numpy as np
-
 def sigmoid(z):
     return 1 / (1 + np.exp(-z))
 
@@ -72,7 +70,8 @@ def predict_logreg(inX, weights):
     """
     z = np.dot(inX, weights)
     h = sigmoid(z)
-    return 1 if h > 0.5 else 0
+    # 调整分类阈值
+    return 1 if h > 0.6 else 0
 
 def load_data(filepath):
     """
@@ -110,8 +109,14 @@ if __name__ == "__main__":
     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)
+    weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=5000)
 
     # 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]}")
+    correct_count = 0
+    total_count = len(y_test)
+    for i in range(total_count):
+        y_pred = predict_logreg(X_test[i], weights)
+        if y_pred == y_test[i]:
+            correct_count += 1
+    accuracy = correct_count / total_count
+    print(f"准确率: {accuracy}")