u_x = randi([1,100],1,100); u_y = randi([1,100],1,100); v_x(u) = randi([0,100],1,10); v_y(u) = randi([0,100],1,10); N=2; M=3; randius=100; B_x_index = 0:1:N+1; B_y_index = 0:1:M+1; B_x = ( 0:1:N+1)*randius/N; B_y = ( 0:1:M+1)*randius/M; num = zeros(N+1,M+1); d_2=2*10^3/1.3; for i=1:1:N+1 for j= 1:1:M+1 for u=1:1:100 if((u_x(u)-B_x(i))^2+(u_y(u)-B_y(i))^2) <=d_2 num(i,j)=num(i,j)+1; end end end end manhandun = 1000*ones(N+1,M+1); for i=1:1:N+1 for j= 0:1:M+1 for u=1:1:10 x = abs(v_x(u)-B_x(i)) + abs(v_y(u)-B_y(j)); if manhandun(i,j) >= x manhandun(i,j) = x; end end end end [x,y] =find( max(num./manhadun));未定义函数或变量 'u'。 出错 Untitled (line 3) v_x(u) = randi([0,100],1,10);

根据代码中的错误提示，未定义变量 'u'，因此需要在代码中定义变量 'u'，例如：

u = 1:1:10;
v_x(u) = randi([0,100],1,10);
v_y(u) = randi([0,100],1,10);

这样就能够解决代码中的错误提示。另外，在运行代码时，也需要注意检查其他变量或函数是否已经定义或调用正确。

相关问题

\begin{bmatrix}u \v\end{bmatrix}=\begin{bmatrix} \cos\theta & \sin\theta \-\sin \theta &\cos\theta \end{bmatrix} \times \left { \begin{bmatrix}k {x} -1 &\gamma {x } \\gamma {y } &k {y} -1\end{bmatrix}\times \begin{bmatrix} x\y\end{bmatrix} +{\textstyle \sum{j=1}^{N}\begin{bmatrix}A{y}^{j} e^{\frac{-1}{2} \left ( \frac{y-y{1}^{j} }{\sigma {y{1} }^{j} } \right )^2-\frac{1}{2} \left (\frac{x-x{1}^{j} }{\sigma {x{1} }^{j} } \right ) ^2 } \A_{x}^{j} e^{\frac{-1}{2} \left ( \frac{x-x_{0}^{j} }{\sigma {x{0} }^{j} } \right )^2-\frac{1}{2} \left (\frac{y-y_{0}^{j} }{\sigma {y{0} }^{j} } \right ) ^2 }\end{bmatrix}} \right } +\begin{bmatrix}t_{x} \t_{y}\end{bmatrix}，N=1or2 X=y=512,tx,ty 范围：-4.0到4.0像素，有效最大位移：2.0像素；kx,ky 范围：0.96到1.04，有效最大位移：5.1；theta 范围：-0.01至0.01rad ， 有效最大位移： 2.4像素；gammax,gammay 范围：-0.03至0.03，有效最大位移： 3.8像素;Ax,Ay范围：0.003到0.6 ；sigmaх0,sigmay0,sigmax1,sigmay1范围：0.06到0.5，x0,y0,x1,y1范围：0到511，最终生成一个可视化的应边场图片(w.r.t最终图像大小：128x128 ）使用matlab实现上述方程

根据您的要求，以下是一个使用MATLAB实现上述方程并生成可视化应边场图片的示例代码：

% 参数设置
N = randi([1, 2]);  % 随机选择N的值为1或2
X = 512;  % 图像大小为512x512像素
tx_range = [-4.0, 4.0];
ty_range = [-4.0, 4.0];
kx_range = [0.96, 1.04];
ky_range = [0.96, 1.04];
theta_range = [-0.01, 0.01];
gammax_range = [-0.03, 0.03];
gammay_range = [-0.03, 0.03];
Ax_range = [0.003, 0.6];
Ay_range = [0.003, 0.6];
sigmax0_range = [0.06, 0.5];
sigmay0_range = [0.06, 0.5];
sigmax1_range = [0.06, 0.5];
sigmay1_range = [0.06, 0.5];
x0_range = [0, 511];
y0_range = [0, 511];
x1_range = [0, 511];
y1_range = [0, 511];

% 随机生成参数值
tx = rand(1)*(tx_range(2)-tx_range(1)) + tx_range(1);
ty = rand(1)*(ty_range(2)-ty_range(1)) + ty_range(1);
kx = rand(1)*(kx_range(2)-kx_range(1)) + kx_range(1);
ky = rand(1)*(ky_range(2)-ky_range(1)) + ky_range(1);
theta = rand(1)*(theta_range(2)-theta_range(1)) + theta_range(1);
gammax = rand(1)*(gammax_range(2)-gammax_range(1)) + gammax_range(1);
gammay = rand(1)*(gammay_range(2)-gammay_range(1)) + gammay_range(1);
Ax = rand(1)*(Ax_range(2)-Ax_range(1)) + Ax_range(1);
Ay = rand(1)*(Ay_range(2)-Ay_range(1)) + Ay_range(1);
sigmax0 = rand(1)*(sigmax0_range(2)-sigmax0_range(1)) + sigmax0_range(1);
sigmay0 = rand(1)*(sigmay0_range(2)-sigmay0_range(1)) + sigmay0_range(1);
sigmax1 = rand(1)*(sigmax1_range(2)-sigmax1_range(1)) + sigmax1_range(1);
sigmay1 = rand(1)*(sigmay1_range(2)-sigmay1_range(1)) + sigmay1_range(1);
x0 = randi([x0_range(1), x0_range(2)], 1);
y0 = randi([y0_range(1), y0_range(2)], 1);
x1 = randi([x1_range(1), x1_range(2)], 1);
y1 = randi([y1_range(1), y1_range(2)], 1);

% 计算数学方程
A = [cos(theta), sin(theta); -sin(theta), cos(theta)];
B = [kx-1, gammax; gammay, ky-1];
C = [Ax * exp(-0.5 * ((y-y1)/sigmay1).^2 - 0.5 * ((x-x1)/sigmax1).^2);
     Ay * exp(-0.5 * ((x-x0)/sigmax0).^2 - 0.5 * ((y-y0)/sigmay0).^2)];
D = A * (B * [x(:)'; y(:)']) + [tx; ty];
u = D(1, :);
v = D(2, :);

% 绘制图像
figure;
quiver(x, y, u, v);
xlim([0, 128]);
ylim([0, 128]);
grid on;

这段MATLAB代码使用了rand函数生成随机数来生成参数值，并根据这些参数计算出应边场图片的坐标点，然后使用quiver函数绘制出应边场图片。你可以根据需要自行调整参数范围和图像大小。

使用matlab进行编程，列出代码，并解释参数选择。输入为u，输出为y，共有296个数据。划分训练集和验证集，使得训练集有200个数据，验证集有96个数据。在RBM中输入归一化数据。并用随即搜索法确定超参数。其中，隐性单元的数量m从区间[10, 18]中得到，学习率η选自[0.1, 0.0001]，动量项选自从[0.1, 0.9]，固定权重惩罚为0.0002。CD使用一步式吉布斯抽样。使用早期停止标准以避免过拟合。列出matlab代码，实用工具箱创建BRBM。

以下是您所需要的Matlab代码，其中包括参数选择、数据划分、RBM参数设置、超参数搜索以及早期停止标准等内容：

% Load data
load('data.mat'); % assuming u and y are stored in data.mat

% Normalize data
u_norm = (u - mean(u)) / std(u);

% Split data into training and validation sets
train_u = u_norm(1:200);
train_y = y(1:200);
val_u = u_norm(201:end);
val_y = y(201:end);

% Set RBM parameters
params.numhid = 10; % start with 10 hidden units
params.pbias = zeros(params.numhid,1);
params.vbias = zeros(size(train_u,1),1);
params.W = 0.1*randn(size(train_u,1),params.numhid);

% Set hyperparameters
eta_range = logspace(-4,-1,50); % learning rate
mom_range = linspace(0.1,0.9,50); % momentum
m_range = 10:18; % number of hidden units
lambda = 0.0002; % weight penalty
CDsteps = 1; % CD using 1-step Gibbs sampling
maxepoch = 1000; % maximum number of epochs

% Random search for hyperparameters
best_err = Inf;
for i = 1:100 % try 100 different hyperparameter combinations
    % Randomly select hyperparameters
    eta = eta_range(randi(length(eta_range)));
    mom = mom_range(randi(length(mom_range)));
    m = m_range(randi(length(m_range)));

    % Train RBM with early stopping
    [rbm, err] = brbm(train_u, params, m, eta, mom, lambda, CDsteps, maxepoch, val_u, val_y);

    % Check if this is the best hyperparameter combination so far
    if err(end) < best_err
        best_eta = eta;
        best_mom = mom;
        best_m = m;
        best_rbm = rbm;
        best_err = err(end);
    end
end

% Final training and validation error
[train_err,~,~] = brbm_err(train_u, train_y, best_rbm.W, best_rbm.hidbiases, best_rbm.visbiases);
[val_err,~,~] = brbm_err(val_u, val_y, best_rbm.W, best_rbm.hidbiases, best_rbm.visbiases);

% Display best hyperparameter combination and errors
disp(['Best hyperparameter combination:']);
disp(['Learning rate: ', num2str(best_eta)]);
disp(['Momentum: ', num2str(best_mom)]);
disp(['Number of hidden units: ', num2str(best_m)]);
disp(['Training error: ', num2str(train_err)]);
disp(['Validation error: ', num2str(val_err)]);

% BRBM training function with early stopping
function [rbm, err] = brbm(train_u, params, m, eta, mom, lambda, CDsteps, maxepoch, val_u, val_y)
    % Initialize weights and biases
    W = params.W;
    vbias = params.vbias;
    hbias = params.pbias;
    numcases = size(train_u,2);
    err = [];

    % Early stopping parameters
    patience = 10; % number of epochs to wait before stopping
    best_err = Inf;
    count = 0;

    % Train RBM using CD with early stopping
    for epoch = 1:maxepoch
        % Positive phase
        poshidprobs = sigmoid(W'*train_u + repmat(hbias,1,numcases));
        posprods = train_u*poshidprobs';
        poshidact = sum(poshidprobs,2);
        posvisact = sum(train_u,2);

        % Negative phase
        neghidprobs = poshidprobs;
        for step = 1:CDsteps
            negvisprobs = sigmoid(W*neghidprobs + repmat(vbias,1,numcases));
            neghidprobs = sigmoid(W'*negvisprobs + repmat(hbias,1,numcases));
        end
        negprods = negvisprobs*neghidprobs';
        neghidact = sum(neghidprobs,2);
        negvisact = sum(negvisprobs,2);

        % Update weights and biases
        dW = (posprods - negprods) / numcases - lambda*W;
        dvbias = (posvisact - negvisact) / numcases;
        dhbias = (poshidact - neghidact) / numcases;
        W = W + eta*dW;
        vbias = vbias + eta*dvbias;
        hbias = hbias + eta*dhbias;

        % Compute training and validation error
        [train_err,~,~] = brbm_err(train_u, train_y, W, hbias, vbias);
        [val_err,~,~] = brbm_err(val_u, val_y, W, hbias, vbias);
        err = [err; train_err, val_err];

        % Check for early stopping
        if val_err < best_err
            best_err = val_err;
            count = 0;
        else
            count = count + 1;
            if count >= patience
                break;
            end
        end
    end

    % Return RBM parameters
    rbm.W = W;
    rbm.visbiases = vbias;
    rbm.hidbiases = hbias;
end

% BRBM error function
function [err, y, y_hat] = brbm_err(u, y, W, hbias, vbias)
    % Compute predicted output
    h = sigmoid(W'*u + repmat(hbias,1,size(u,2)));
    y_hat = sigmoid(W*h + repmat(vbias,1,size(u,2)));

    % Compute mean squared error
    err = mean((y - y_hat).^2);
end

% Sigmoid function
function s = sigmoid(x)
    s = 1 ./ (1 + exp(-x));
end

在这个代码中，我们首先加载数据并对输入进行归一化，然后将数据划分为训练集和验证集。接下来，我们设置RBM的参数，包括可见层偏置、隐藏层偏置和权重矩阵。然后，我们定义超参数的范围，并使用随机搜索方法找到最佳的超参数组合。对于每个超参数组合，我们使用早期停止标准来避免过拟合，并计算训练误差和验证误差。最后，我们返回最佳模型的参数和训练/验证误差。注意，我们使用了Matlab自带的sigmoid函数来计算激活值。