x = data(:,1); y = data(:,2); % 定义柯西色散方程 f = @(coeffs, x) coeffs(1) + coeffs(2) ./ x.^2 + coeffs(3) ./ x.^4; % 定义初始系数矩阵 coeffs0 = [1,1,1]; % 进行非线性拟合 coeffs = nlinfit(x,y,f,coeffs0); % 输出结果 disp(['a = ', sprintf('%e', coeffs(1))]); disp(['b = ', sprintf('%e', coeffs(2))]); disp(['c = ', sprintf('%e', coeffs(3))]); x_fit = linspace(min(x), max(x), 1000); y_fit = f(coeffs, x_fit); % 绘制原始数据散点图和拟合曲线 figure; scatter(x, y); hold on; plot(x_fit, y_fit); xlabel('λ'); ylabel('n'); legend('原始数据', '拟合曲线');这段代码没有计算rmse，请帮我补充完整

可以通过以下代码计算出拟合曲线与原始数据之间的均方根误差（RMSE）：

y_fit = f(coeffs, x);  % 计算拟合值
rmse = sqrt(mean((y - y_fit).^2));  % 计算均方根误差
disp(['RMSE = ', sprintf('%e', rmse)]);  % 输出结果

将其添加到原有代码中：

x = data(:,1); y = data(:,2); % 定义柯西色散方程 f = @(coeffs, x) coeffs(1) + coeffs(2) ./ x.^2 + coeffs(3) ./ x.^4;  % 定义初始系数矩阵 coeffs0 = [1,1,1];  % 进行非线性拟合 coeffs = nlinfit(x,y,f,coeffs0); % 计算拟合曲线与原始数据之间的均方根误差 y_fit = f(coeffs, x);  % 计算拟合值 rmse = sqrt(mean((y - y_fit).^2));  % 计算均方根误差 输出结果 disp(['a = ', sprintf('%e', coeffs(1))]); disp(['b = ', sprintf('%e', coeffs(2))]); disp(['c = ', sprintf('%e', coeffs(3))]); disp(['RMSE = ', sprintf('%e', rmse)]);  % 输出结果 x_fit = linspace(min(x), max(x), 1000); y_fit = f(coeffs, x_fit);  % 绘制原始数据散点图和拟合曲线 figure; scatter(x, y); hold on; plot(x_fit, y_fit); xlabel('λ'); ylabel('n'); legend('原始数据', '拟合曲线');