From 8d4d353ec2f58e7734add49d263365f4a38185bc Mon Sep 17 00:00:00 2001
From: 77 <3298722099@qq.com>
Date: Fri, 27 Dec 2024 14:34:04 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 28 ++++++++++++++++++++--------
 1 file changed, 20 insertions(+), 8 deletions(-)

diff --git a/function.py b/function.py
index aaff34e..edcfb86 100644
--- a/function.py
+++ b/function.py
@@ -9,17 +9,17 @@ def load_data(filepath):
         data = np.genfromtxt(filepath, delimiter=',', dtype=float, filling_values=np.nan)
     except Exception as e:
         print(f"数据加载错误，可能是由于数据格式不正确：{e}")
-        return None, None
+        return None
 
     # 检查数据是否为二维数组
     if data is None or data.ndim != 2:
         print("数据不是二维数组，无法继续处理。")
-        return None, None
+        return None
 
     # 确保所有行都有相同数量的列
     if data.shape[1] < 2:
         print("数据列数不正确，无法继续处理。")
-        return None, None
+        return None
 
     # 分离特征和标签
     X = data[:, :-1]
@@ -45,20 +45,32 @@ def train_svm(X, y, learning_rate=0.01, iterations=1000, regularization=1.0):
 def predict_svm(inX, weights, bias):
     return np.sign(np.dot(inX, weights) - bias)
 
+def evaluate_svm(X_test, y_test, weights, bias):
+    if X_test is None or y_test is None:
+        print("测试数据加载失败，无法评估。")
+        return 0
+
+    correct = 0
+    for i in range(len(X_test)):
+        if predict_svm(X_test[i], weights, bias) == y_test[i]:
+            correct += 1
+    accuracy = correct / len(X_test)
+    return accuracy
+
 if __name__ == "__main__":
     # 加载训练集
     X_train, y_train = load_data("./data/horse-colic.data")
-    if X_train is None or y_train is None:
+    if X_train is None:
         print("训练数据加载失败。")
     else:
         # 加载测试集
         X_test, y_test = load_data("./data/horse-colic.test")
-        if X_test is None or y_test is None:
+        if X_test is None:
             print("测试数据加载失败。")
         else:
             # 训练支持向量机模型
             weights, bias = train_svm(X_train, y_train, learning_rate=0.01, iterations=1000)
 
-            # 测试单个数据
-            y_pred = predict_svm(X_test[11], weights, bias)
-            print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")
\ No newline at end of file
+            # 评估模型
+            accuracy = evaluate_svm(X_test, y_test, weights, bias)
+            print(f"模型准确率: {accuracy}")
\ No newline at end of file