% 读入数据并进行预处理 data = [0.1, 0.2, 0.3, 0.1; 0.3, 0.2, 0.1, 0.2; 0.5, 0.6, 0.5, 0.4]; logData = log(data); ageMean = mean(data, 2); timeMean = mean(data, 1); % 建立模型 k = 1.5; % 设定初值 gamma = logData - ageMean*ones(1,length(timeMean)) - k*(ones(length(ageMean),1)*timeMean); phi = mean(gamma,2); kappa = logData - ageMean*ones(1,length(timeMean)) - phi*ones(1,length(timeMean)) - k*(ones(length(ageMean),1)*timeMean); % 对kappa进行奇异值分解 [U,S,V] = svd(kappa); K = 1; % 选择前K个主成分 uk = U(:,1:K); sk = S(1:K,1:K); vk = V(:,1:K); kappaNew = uk * sk * vk'; phiNew = mean(kappaNew, 2); gammaNew = kappaNew - phiNew * ones(1, length(timeMean)); % 参数估计和预测 y = reshape(logData - phi*ones(1, length(timeMean)), 1, []); x(:,1) = reshape(gammaNew, 1, []); disp("数据:") disp(U(:,i)); disp("数据:") disp(logData - phi.*ones(size(logData,1),1)); for i = 1:K x(:, i+1) = (U(:, i)' * (logData - phi .* ones(1, size(logData, 2))))'; end b = x\y; futureTime = [1:2]'+timeMean(end); % 对未来年份数据进行预测（2023, 2024年） ages = [1:size(data,1)]'; logDataPred = repmat(phiNew, [1, length(futureTime)]) + gammaNew*k + ageMean*ones(1,length(futureTime)) + [ones(size(ages)),U(:,1:K)]*b; dataPred = exp(logDataPred); % 输出结果 disp("数据:") disp(data); disp("拟合数据:") disp(dataPred);找出错误

在第二个disp语句中，变量i并未定义，应该是一个错误。此外，在第26行中，ones(1, size(logData, 2))应该改为ones(1, length(timeMean))，因为timeMean是列均值，所以长度应该与列数相同。正确的代码如下：

% 读入数据并进行预处理
data = [0.1, 0.2, 0.3, 0.1;
        0.3, 0.2, 0.1, 0.2;
        0.5, 0.6, 0.5, 0.4];
logData = log(data);
ageMean = mean(data, 2);
timeMean = mean(data, 1);

% 建立模型
k = 1.5; % 设定初值
gamma = logData - ageMean*ones(1,length(timeMean)) - k*(ones(length(ageMean),1)*timeMean);
phi = mean(gamma,2);
kappa = logData - ageMean*ones(1,length(timeMean)) - phi*ones(1,length(timeMean)) - k*(ones(length(ageMean),1)*timeMean);

% 对kappa进行奇异值分解
[U,S,V] = svd(kappa);
K = 1; % 选择前K个主成分
uk = U(:,1:K);
sk = S(1:K,1:K);
vk = V(:,1:K);
kappaNew = uk * sk * vk';
phiNew = mean(kappaNew, 2);
gammaNew = kappaNew - phiNew * ones(1, length(timeMean));

% 参数估计和预测
y = reshape(logData - phi*ones(1, length(timeMean)), 1, []);
x(:,1) = reshape(gammaNew, 1, []);
disp("数据:")
disp(U);
disp("数据:")
disp(logData - phi.*ones(size(logData,1),1));
for i = 1:K
    x(:, i+1) = (U(:, i)' * (logData - phi .* ones(1, length(timeMean))))';
end
b = x\y;
futureTime = [1:2]'+timeMean(end); % 对未来年份数据进行预测（2023, 2024年）
ages = [1:size(data,1)]';
logDataPred = repmat(phiNew, [1, length(futureTime)]) + gammaNew*k + ageMean*ones(1,length(futureTime)) + [ones(size(ages)),U(:,1:K)]*b;
dataPred = exp(logDataPred);

% 输出结果
disp("数据:")
disp(data);
disp("拟合数据:")
disp(dataPred);