Matlab 中的多项式回归和绘图

Polynomial regression and plotting in Matlab

给定一组点 x 和一组值 y,我试图计算最小二乘意义上最适合 P(x) = y 的多项式。该函数应显示 Vandermonde 矩阵,输出多项式 c 应绘制为 p (x) = c0*x^0 + c1*x^1 + c2*x^2 + .. 形式的函数... + cn-1^(n-1).

我想清楚地看到绘制函数的同一个图上的点 (xi,yi)。

这是我到目前为止尝试过的方法:

function c = interpolation(x, y)
    n = length(x);
    V = ones(n);
    for j = n:-1:2
         V(:,j-1)  = x.*V(:,j);
    end
     c = V \ y;
     disp(V) 
    for i = 0:n-1
      fprintf('c%d= %.3f\n', i, c(i+1));
    end

    x = linspace(-1,2,-100);
    y = polyval(c,x);

    x0 = x;
    y0 = polyval(c,x0);

    plot(x,y,'b-')
    hold on; 
    plot(x0,y0,'ro') 
    hold off;

您想看一下 polyval and linspace if you don't already know it. Also take a look at polyfit,它会根据给定的度数为您进行插值。这是您更正后的代码:

function [p,V] = interpolation(x0,y0,N)

    % format the inputs as columns
    x0 = x0(:);
    y0 = y0(:);

    % Build up the vandermonde matrix
    n = numel(x0);
    disp('Vandermonde matrix:');
    V = fliplr(bsxfun( @power, x0, 0:(n-1) ))

    % compute the coefficients of the fitting polynomial
    p = V \ y0;

    % plot the polynomial using N values
    x = linspace( min(x0), max(x0), N );
    y = polyval(p,x);

    plot(x,y,'b-'); hold on;
    plot(x0',y0','ro'); hold off;

end

注意:返回为 p 的多项式系数与您的索引相比是相反的,即它们按降幂排序。