已知A=f(a,b,c,d,e),目前我有1000个样本，我想计算a,b,c,d,e四个元素对A的敏感性，请基于Matlab给出5种不同的敏感性计算代码。

以下是5种不同的敏感性计算代码：

1. 基于一阶近似的敏感性计算：

% 输入样本数据和函数A(f(a,b,c,d,e))
data = [a1, b1, c1, d1, e1; a2, b2, c2, d2, e2; ... ; a1000, b1000, c1000, d1000, e1000];
A_values = f(data(:,1), data(:,2), data(:,3), data(:,4), data(:,5));

% 计算每个元素的平均敏感度
sensitivity_a = mean(abs((f(data(:,1)+eps, data(:,2), data(:,3), data(:,4), data(:,5))-A_values)/eps));
sensitivity_b = mean(abs((f(data(:,1), data(:,2)+eps, data(:,3), data(:,4), data(:,5))-A_values)/eps));
sensitivity_c = mean(abs((f(data(:,1), data(:,2), data(:,3)+eps, data(:,4), data(:,5))-A_values)/eps));
sensitivity_d = mean(abs((f(data(:,1), data(:,2), data(:,3), data(:,4)+eps, data(:,5))-A_values)/eps));
sensitivity_e = mean(abs((f(data(:,1), data(:,2), data(:,3), data(:,4), data(:,5)+eps)-A_values)/eps));

2. 基于二阶近似的敏感性计算：

% 输入样本数据和函数A(f(a,b,c,d,e))
data = [a1, b1, c1, d1, e1; a2, b2, c2, d2, e2; ... ; a1000, b1000, c1000, d1000, e1000];
A_values = f(data(:,1), data(:,2), data(:,3), data(:,4), data(:,5));

% 计算每个元素的平均敏感度
hessian_matrix = hessian(f, [a,b,c,d,e]); % 计算海森矩阵
sensitivity_a = mean(abs((hessian_matrix(1,1)*eps^2 + hessian_matrix(1,2)*eps + hessian_matrix(2,1)*eps + hessian_matrix(1,3)*eps + hessian_matrix(3,1)*eps + hessian_matrix(1,4)*eps + hessian_matrix(4,1)*eps + hessian_matrix(1,5)*eps + hessian_matrix(5,1)*eps)/2));
sensitivity_b = mean(abs((hessian_matrix(2,2)*eps^2 + hessian_matrix(1,2)*eps + hessian_matrix(2,1)*eps + hessian_matrix(2,3)*eps + hessian_matrix(3,2)*eps + hessian_matrix(2,4)*eps + hessian_matrix(4,2)*eps + hessian_matrix(2,5)*eps + hessian_matrix(5,2)*eps)/2));
sensitivity_c = mean(abs((hessian_matrix(3,3)*eps^2 + hessian_matrix(1,3)*eps + hessian_matrix(3,1)*eps + hessian_matrix(3,2)*eps + hessian_matrix(2,3)*eps + hessian_matrix(3,4)*eps + hessian_matrix(4,3)*eps + hessian_matrix(3,5)*eps + hessian_matrix(5,3)*eps)/2));
sensitivity_d = mean(abs((hessian_matrix(4,4)*eps^2 + hessian_matrix(1,4)*eps + hessian_matrix(4,1)*eps + hessian_matrix(4,2)*eps + hessian_matrix(2,4)*eps + hessian_matrix(4,3)*eps + hessian_matrix(3,4)*eps + hessian_matrix(4,5)*eps + hessian_matrix(5,4)*eps)/2));
sensitivity_e = mean(abs((hessian_matrix(5,5)*eps^2 + hessian_matrix(1,5)*eps + hessian_matrix(5,1)*eps + hessian_matrix(5,2)*eps + hessian_matrix(2,5)*eps + hessian_matrix(5,3)*eps + hessian_matrix(3,5)*eps + hessian_matrix(5,4)*eps + hessian_matrix(4,5)*eps)/2));

3. 基于主成分分析的敏感性计算：

% 输入样本数据和函数A(f(a,b,c,d,e))
data = [a1, b1, c1, d1, e1; a2, b2, c2, d2, e2; ... ; a1000, b1000, c1000, d1000, e1000];
A_values = f(data(:,1), data(:,2), data(:,3), data(:,4), data(:,5));

% 计算每个元素的平均敏感度
[coeff,score,latent] = pca([data A_values]); % 进行主成分分析
sensitivity_a = abs(coeff(1,end)/coeff(end,end));
sensitivity_b = abs(coeff(2,end)/coeff(end,end));
sensitivity_c = abs(coeff(3,end)/coeff(end,end));
sensitivity_d = abs(coeff(4,end)/coeff(end,end));
sensitivity_e = abs(coeff(5,end)/coeff(end,end));

4. 基于逐步回归的敏感性计算：

% 输入样本数据和函数A(f(a,b,c,d,e))
data = [a1, b1, c1, d1, e1; a2, b2, c2, d2, e2; ... ; a1000, b1000, c1000, d1000, e1000];
A_values = f(data(:,1), data(:,2), data(:,3), data(:,4), data(:,5));

% 计算每个元素的平均敏感度
[b,se,pval,inmodel,stats,nextstep,history] = stepwisefit(data, A_values);
sensitivity_a = stats.t(end);
sensitivity_b = stats.t(end);
sensitivity_c = stats.t(end);
sensitivity_d = stats.t(end);
sensitivity_e = stats.t(end);

5. 基于随机抽样的敏感性计算：

% 输入样本数据和函数A(f(a,b,c,d,e))
data = [a1, b1, c1, d1, e1; a2, b2, c2, d2, e2; ... ; a1000, b1000, c1000, d1000, e1000];
A_values = f(data(:,1), data(:,2), data(:,3), data(:,4), data(:,5));

% 计算每个元素的平均敏感度
sensitivity_a = 0;
sensitivity_b = 0;
sensitivity_c = 0;
sensitivity_d = 0;
sensitivity_e = 0;
for i = 1:1000 % 随机抽样1000次
    random_index = randperm(1000, 900);
    A_values_temp = f(data(random_index,1), data(random_index,2), data(random_index,3), data(random_index,4), data(random_index,5));
    sensitivity_a = sensitivity_a + abs(mean(A_values_temp) - mean(A_values(random_index))) / 1000;
    sensitivity_b = sensitivity_b + abs(mean(A_values_temp) - mean(A_values(random_index))) / 1000;
    sensitivity_c = sensitivity_c + abs(mean(A_values_temp) - mean(A_values(random_index))) / 1000;
    sensitivity_d = sensitivity_d + abs(mean(A_values_temp) - mean(A_values(random_index))) / 1000;
    sensitivity_e = sensitivity_e + abs(mean(A_values_temp) - mean(A_values(random_index))) / 1000;
end