更新 function.py

function.py

@@ -1,5 +1,4 @@
 import numpy as np
-from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.preprocessing import PolynomialFeatures, StandardScaler
 
@@ -36,7 +35,9 @@ def train_logreg(X, y):
     return model
 
 def predict_logreg(model, X):
-    return model.predict(X)
+    # 确保传入的是训练好的模型
+    X_processed = preprocess_data(X.reshape(1, -1))  # 将单个样本重塑并预处理
+    return model.predict(X_processed)[0]  # 预测并返回结果
 
 if __name__ == "__main__":
     # 加载数据
@@ -53,7 +54,9 @@ if __name__ == "__main__":
     model = train_logreg(X_train_processed, y_train)
 
     # 预测
-    y_pred = predict_logreg(model, X_test_processed)
+    y_pred = []
+    for sample in X_test:
+        y_pred.append(predict_logreg(model, sample))
 
     # 计算正确率
     accuracy = np.mean(y_pred == y_test)