为什么输入参数的数目不足。 出错 rng (line 2) visited = zeros(1, length(graph));
时间: 2024-04-09 07:27:23 浏览: 11
这个错误是因为在调用`rng`函数时,输入参数的数目不足导致的。
`rng`函数用于控制随机数生成器的种子,以便在不同的运行中生成相同的随机数序列。然而,根据您提供的代码片段,`rng`函数的使用似乎与问题无关。错误实际上是发生在`visited`数组的初始化上。
根据错误消息,问题出现在第2行,即`visited = zeros(1, length(graph));`。这行代码的目的是创建一个与图中节点数量相同大小的零向量来跟踪节点的访问状态。
可能的原因是,您没有正确传递图形参数或图形参数的长度不正确。请确保`graph`变量包含正确的图形
相关问题
function dfsWithTimestamp(graph, startNode) visited = zeros(1, length(graph)); timestamp = 1; stack = startNode; timestamps = zeros(1, length(graph)); while ~isempty(stack) node = stack(end); stack(end) = []; if visited(node) == 0 visited(node) = 1; timestamps(node) = timestamp; timestamp = timestamp + 1; % 这里可以根据实际需求进行相应的操作,例如输出节点值 disp(['Visited Node ', num2str(node)]); neighbors = find(graph(node, :)); % 获取相邻节点 for i = length(neighbors):-1:1 if visited(neighbors(i)) == 0 stack(end+1) = neighbors(i); end end end end disp('Timestamps:'); disp(timestamps); end里边有出错 rng (line 2) visited = zeros(1, length(graph)); 这个问题
对不起,我之前的回答中有误导。错误实际上并不是在第2行的`rng`函数调用中。根据您提供的代码片段问题出现在第2行的`visited`数组初始化中,这是一个语法错误。
在MATLAB中,要创建一个全零的向量或矩阵,应该使用`zeros`函数而不是`rng`函数。因此,将第2行的代码修改为以下内容应该可以解决问题:
```matlab
visited = zeros(1, length(graph));
```
请注意,`rng`函数用于控制随机数生成的种子,与您在深度优先搜索算法中的需求无关。因此,与这个错误消息无关。非常抱歉给您带来困惑。请尝试修改代码并再次运行。
rng('default'); inputSize = 1; numEpochs = 200; batchSize = 16; nTraining = length(label);
As an AI language model, I do not have access to the full context of your code. However, I can explain what each line does:
- `rng('default')`: sets the random number generator to its default configuration.
- `inputSize = 1`: sets the size of the input data. In this case, it is a scalar value, but it could be a vector, matrix, or higher-dimensional tensor.
- `numEpochs = 200`: sets the number of epochs (passes through the entire training dataset) for the training process.
- `batchSize = 16`: sets the batch size for the training process. This is the number of samples that will be processed at once before updating the model parameters.
- `nTraining = length(label)`: calculates the number of samples in the training dataset, assuming that `label` is a vector containing the labels for each sample.