帮我把这俩段代码结合一下,以方便直接运行% 设置节点数和测试数据 numNodes = [20, 30, 40, 50, 60, 70, 80, 90]; numTests = 100; % 每个节点数运行的测试次数 % 初始化结果记录变量 runtimeCentrality = zeros(length(numNodes), numTests); runtimeSIR = zeros(length(numNodes), numTests); accuracyCentrality = zeros(length(numNodes), numTests); accuracySIR = zeros(length(numNodes), numTests); % 循环运行测试 for i = 1:length(numNodes) for j = 1:numTests % 生成随机测试数据 network = generateRandomNetwork(numNodes(i)); % 使用介数中心算法进行关键节点检测 tic; centralityResult = computeCentrality(network); runtimeCentrality(i, j) = toc; % 使用SIR模型进行关键节点检测 tic; SIRResult = computeSIR(network); runtimeSIR(i, j) = toc; % 计算准确率 accuracyCentrality(i, j) = computeAccuracy(centralityResult, trueCriticalNodes); accuracySIR(i, j) = computeAccuracy(SIRResult, trueCriticalNodes); end end % 计算平均准确率和运行时间 avgAccuracyCentrality = mean(accuracyCentrality, 2); avgAccuracySIR = mean(accuracySIR, 2); avgRuntimeCentrality = mean(runtimeCentrality, 2); avgRuntimeSIR = mean(runtimeSIR, 2); % 绘制准确率折线图 figure; plot(numNodes, avgAccuracyCentrality, 'o-', 'LineWidth', 2, 'MarkerSize', 8); hold on; plot(numNodes, avgAccuracySIR, 'o-', 'LineWidth', 2, 'MarkerSize', 8); xlabel('节点数'); ylabel('准确率'); legend('介数中心算法', 'SIR模型'); grid on;和function network = generateRandomNetwork(numNodes) % 生成 numNodes x numNodes 大小的随机邻接矩阵 adjacencyMatrix = rand(numNodes) < 0.5; adjacencyMatrix = triu(adjacencyMatrix, 1); % 保留上三角形部分 % 构建网络模型 network = graph(adjacencyMatrix); end
时间: 2024-04-17 16:24:45 浏览: 18
以下是将两段代码结合在一起的完整代码:
```matlab
% 设置节点数和测试数据
numNodes = [20, 30, 40, 50, 60, 70, 80, 90];
numTests = 100; % 每个节点数运行的测试次数
% 初始化结果记录变量
runtimeCentrality = zeros(length(numNodes), numTests);
runtimeSIR = zeros(length(numNodes), numTests);
accuracyCentrality = zeros(length(numNodes), numTests);
accuracySIR = zeros(length(numNodes), numTests);
% 循环运行测试
for i = 1:length(numNodes)
for j = 1:numTests
% 生成随机测试数据
network = generateRandomNetwork(numNodes(i));
% 使用介数中心算法进行关键节点检测
tic;
centralityResult = computeCentrality(network);
runtimeCentrality(i, j) = toc;
% 使用SIR模型进行关键节点检测
tic;
SIRResult = computeSIR(network);
runtimeSIR(i, j) = toc;
% 计算准确率
accuracyCentrality(i, j) = computeAccuracy(centralityResult, trueCriticalNodes);
accuracySIR(i, j) = computeAccuracy(SIRResult, trueCriticalNodes);
end
end
% 计算平均准确率和运行时间
avgAccuracyCentrality = mean(accuracyCentrality, 2);
avgAccuracySIR = mean(accuracySIR, 2);
avgRuntimeCentrality = mean(runtimeCentrality, 2);
avgRuntimeSIR = mean(runtimeSIR, 2);
% 绘制准确率折线图
figure;
plot(numNodes, avgAccuracyCentrality, 'o-', 'LineWidth', 2, 'MarkerSize', 8);
hold on;
plot(numNodes, avgAccuracySIR, 'o-', 'LineWidth', 2, 'MarkerSize', 8);
xlabel('节点数');
ylabel('准确率');
legend('介数中心算法', 'SIR模型');
grid on;
function network = generateRandomNetwork(numNodes)
% 生成 numNodes x numNodes 大小的随机邻接矩阵
adjacencyMatrix = rand(numNodes) < 0.5;
adjacencyMatrix = triu(adjacencyMatrix, 1); % 保留上三角形部分
% 构建网络模型
network = graph(adjacencyMatrix);
end
```
请注意,在上述代码中,你需要根据你的具体需求实现 `computeCentrality`、`computeSIR` 和 `computeAccuracy` 函数。这些函数分别用于计算介数中心算法和SIR模型的关键节点检测结果以及准确率。
此外,你还需要提供真实的关键节点信息(即 `trueCriticalNodes`),并在 `computeAccuracy` 函数中与算法输出进行比较以计算准确率。
运行上述代码后,将会生成一个折线图,展示介数中心算法和SIR模型在不同节点数下的平均准确率。
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