matlab中的peek
时间: 2023-11-02 22:21:57 浏览: 63
在MATLAB中,peaks函数是一个典型的多元函数,它本质上是一个二元高斯分布的概率密度函数。它的函数表达式为z = 3*(1-x).^2.*exp(-(x.^2) - (y - 1).^2)。如果没有给出输入参数,该函数使用默认的矩阵X和Y作为底层矩阵。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
相关问题
matlab peek函数
在MATLAB中,没有peek函数。也许你想问的是peekdata函数,它是MATLAB中用于读取串行端口缓冲区数据的函数。peekdata函数可以返回串行端口缓冲区中的数据,但不会将其从缓冲区中删除。
使用peekdata函数时,你需要先创建一个串行端口对象,并打开该端口。然后,你可以使用peekdata函数来读取缓冲区中的数据。下面是一个示例:
```matlab
% 创建串行端口对象
s = serial('COM1');
% 打开串行端口
fopen(s);
% 读取缓冲区中的数据
data = peekdata(s);
% 关闭串行端口
fclose(s);
```
请注意,上述示例仅演示了peekdata函数的基本用法。你可以根据自己的需求进行进一步的处理和操作。
有n种物品可以放入背包,每种物品重量为wi和价值为vi,每种物品可以放多个,背包的最大重量限制是b,求使得背包价值最大的物品选择方案,利用分支限界法的思想解决下列问题: 1.该问题的代价函数如何设计? 2.利用最大效益优先的方式进行遍历,写出算法的伪代码; 3.根据伪代码,写出matlab代码
1. 该问题的代价函数设计如下:对于每个节点,计算已经选择的物品的总重量和总价值,对于剩余的物品,估计其能够贡献的最大价值,将已经选择的总价值加上估计的最大价值,得到当前节点的价值。若当前节点的价值已经小于当前最优解,则剪枝。
2. 最大效益优先的方式进行遍历的伪代码如下:
```
initialize priority queue Q;
initialize current node u as the root of the search tree;
initialize the current upper bound as the total value of all items;
initialize the current best solution as an empty set;
while Q is not empty do
u = Q.pop(); // choose node with highest priority
if u is a leaf node then // check if the node is a solution
if the value of u is greater than the current best solution then
update the current best solution;
update the current upper bound to the value of u;
else if the value of u plus the estimated maximum value of the remaining items is greater than the current best solution then // check if it is worth exploring
create child nodes by selecting the next item or not selecting the next item;
calculate the estimated maximum value of each child node;
add the child nodes to Q with priority based on their estimated maximum value;
```
3. 根据伪代码,可以编写如下的 MATLAB 代码:
```matlab
% input data
n = 3; % number of item types
b = 50; % maximum weight of the knapsack
w = [10; 20; 30]; % weights of the items
v = [60; 100; 120]; % values of the items
% initialize priority queue
Q = PriorityQueue();
Q.push(Node(0, 0, 0, zeros(n, 1), ones(n, 1)));
% initialize current upper bound and best solution
ub = sum(v);
x_best = zeros(n, 1);
v_best = 0;
% search
while ~Q.isempty()
% choose node with highest priority
u = Q.pop();
% check if the node is a solution
if u.level == n
if u.value > v_best
v_best = u.value;
x_best = u.x;
ub = v_best;
end
% check if it is worth exploring
elseif u.value + ub - u.estimate > v_best
% create child nodes
x = u.x;
x(u.level+1) = 1;
Q.push(Node(u.level+1, u.weight+w(u.level+1), u.value+v(u.level+1), x));
x(u.level+1) = 0;
Q.push(Node(u.level+1, u.weight, u.value, x));
% calculate the estimated maximum value of each child node
for i = 1:2
child = Q.peek(i);
weight = child.weight;
estimate = child.value;
for j = child.level+1:n
if weight + w(j) <= b
weight = weight + w(j);
estimate = estimate + v(j);
else
estimate = estimate + (b - weight) / w(j) * v(j);
break;
end
end
Q.set_priority(i, estimate);
end
end
end
% output best solution
fprintf('Best value: %d\n', v_best);
fprintf('Best solution: ');
fprintf('%d ', x_best);
fprintf('\n');
```
其中,`PriorityQueue` 是优先队列类,`Node` 是搜索树节点类。
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