更新 function.py

function.py

@@ -1,76 +1,68 @@
-import numpy as np
 
 id = "202210423201"
 name = "陈瑞琪祥"
 
+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
+
+# 数据加载函数
 def load_data(filepath):
-    try:
-        # 使用numpy的genfromtxt函数加载数据，并处理缺失值
-        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
+    data = []
+    labels = []
     
-    # 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
+    with open(filepath, 'r') as file:
+        for line in file:
+            fields = line.strip().split()
+            fields = [0 if f == '?' else float(f) for f in fields]
             
-    # 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
+            label = int(fields[-6])
+            features = np.array(fields[:-6] + fields[-5:], dtype=float)
             
-    # 分离特征和标签
-    X = data[:, :-1]
-    y = data[:, -1].astype(int)
-    return X, y
-
-def train_svm(X, y, learning_rate=0.01, iterations=1000, regularization=1.0):
-    n_samples, n_features = X.shape
-    weights = np.zeros(n_features)
-    bias = 0
-
-    for _ in range(iterations):
-        for idx, x_i in enumerate(X):
-            condition = y[idx] * (np.dot(x_i, weights) - bias) >= 1
-            if condition:
-                weights -= learning_rate * (2 * regularization * weights)
+            if label in [1, 2, 3]:
+                label = {1: 0, 2: 1, 3: 2}[label]
+                data.append(features)
+                labels.append(label)
             else:
-                weights -= learning_rate * (2 * regularization * weights - np.dot(x_i, y[idx]))
-                bias -= learning_rate * y[idx]
+                print(f"无效标签，跳过此样本：{label}")
     
-    return weights, bias
+    return np.array(data), np.array(labels)
 
-def predict_svm(inX, weights, bias):
-    return np.sign(np.dot(inX, weights) - bias)
+def train_svm(X, y):
+    """
+    支持向量机训练函数。
+    """
+    scaler = StandardScaler()
+    X_scaled = scaler.fit_transform(X)
     
-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
+    model = SVC(kernel='rbf', C=1.0, gamma='scale', random_state=42)
+    model.fit(X_scaled, y)
+    return model, scaler
 
-    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
+def predict_svm(model, scaler, X):
+    X_scaled = scaler.transform(X)
+    return model.predict(X_scaled)
 
 if __name__ == "__main__":
-    # 加载训练集
-    X_train, y_train = load_data("./data/horse-colic.data")
-    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:
-            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)
+    # 加载数据
+    X, y = load_data("./data/horse-colic.data")
+    
+    # 分割数据集
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    # 训练SVM模型
+    svm_model, scaler = train_svm(X_train, y_train)
+    
+    # 预测测试集
+    y_pred = predict_svm(svm_model, scaler, X_test)
     
     # 评估模型
-            accuracy = evaluate_svm(X_test, y_test, weights, bias)
-            print(f"模型准确率: {accuracy}")
+    accuracy = accuracy_score(y_test, y_pred)
+    print(f"测试集准确率: {accuracy * 100:.2f}%")
+    
+    # 测试单个数据
+    test_sample = X_test[11]  # 选择第12个样本（从0开始计数）
+    y_pred_single = predict_svm(svm_model, scaler, test_sample.reshape(1, -1))
+    print(f"\n单个样本预测结果: {y_pred_single[0]}, 实际结果: {y_test[11]}")