generated from LufsX/Challenge-SVM
68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
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id = "202210423201"
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name = "陈瑞琪祥"
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import numpy as np
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from sklearn.svm import SVC
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import classification_report, accuracy_score
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from sklearn.preprocessing import StandardScaler
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# 数据加载函数
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def load_data(filepath):
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data = []
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labels = []
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with open(filepath, 'r') as file:
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for line in file:
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fields = line.strip().split()
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fields = [0 if f == '?' else float(f) for f in fields]
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label = int(fields[-6])
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features = np.array(fields[:-6] + fields[-5:], dtype=float)
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if label in [1, 2, 3]:
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label = {1: 0, 2: 1, 3: 2}[label]
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data.append(features)
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labels.append(label)
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else:
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print(f"无效标签，跳过此样本：{label}")
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return np.array(data), np.array(labels)
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def train_svm(X, y):
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"""
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支持向量机训练函数。
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"""
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scaler = StandardScaler()
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X_scaled = scaler.fit_transform(X)
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model = SVC(kernel='rbf', C=1.0, gamma='scale', random_state=42)
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model.fit(X_scaled, y)
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return model, scaler
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def predict_svm(model, scaler, X):
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X_scaled = scaler.transform(X)
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return model.predict(X_scaled)
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if __name__ == "__main__":
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# 加载数据
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X, y = load_data("./data/horse-colic.data")
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# 分割数据集
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# 训练SVM模型
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svm_model, scaler = train_svm(X_train, y_train)
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# 预测测试集
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y_pred = predict_svm(svm_model, scaler, X_test)
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# 评估模型
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accuracy = accuracy_score(y_test, y_pred)
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print(f"测试集准确率: {accuracy * 100:.2f}%")
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# 测试单个数据
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test_sample = X_test[11] # 选择第12个样本（从0开始计数）
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y_pred_single = predict_svm(svm_model, scaler, test_sample.reshape(1, -1))
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print(f"\n单个样本预测结果: {y_pred_single[0]}, 实际结果: {y_test[11]}") |