更新 function.py

function.py

@@ -1,13 +1,11 @@
-import numpy as np
+
-from sklearn.svm import SVC
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import classification_report, accuracy_score
-from sklearn.preprocessing import StandardScaler
 
 id = "202210423201"
 name = "陈瑞琪祥"
 
 import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
 
 # 数据加载函数
 def load_data(filepath):
@@ -52,6 +50,11 @@ def train_svm(X, y):
 def predict_svm(X, weights, bias):
     return np.sign(np.dot(X, weights) - bias)
 
+# 评估函数
+def evaluate_svm(test_data, weights, bias):
+    predictions = [predict_svm(test_data[i].reshape(1, -1), weights, bias) for i in range(len(test_data))]
+    return predictions
+
 if __name__ == "__main__":
     # 加载数据
     X, y = load_data("./data/horse-colic.data")
@@ -63,7 +66,7 @@ if __name__ == "__main__":
     weights, bias = train_svm(X_train, y_train)
     
     # 预测测试集
-    y_pred = predict_svm(X_test, weights, bias)
+    y_pred = evaluate_svm(X_test, weights, bias)
     
     # 评估模型
     accuracy = accuracy_score(y_test, y_pred)
@@ -73,4 +76,3 @@ if __name__ == "__main__":
     test_sample = X_test[11]  # 选择第12个样本（从0开始计数）
     y_pred_single = predict_svm(test_sample.reshape(1, -1), weights, bias)
     print(f"\n单个样本预测结果: {y_pred_single[0]}, 实际结果: {y_test[11]}")
-