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  • regression

    %regression learning


     

    x=[23.8 27.6 31.6 32.4 33.7 34.9 43.2 52.8 63.8 73.4];
    y=[41.4 51.8 61.7 67.9 68.7 77.5 95.9 137.4 155 175];
    figure
    plot(x,y,'r*')
    xlabel('wages','fontsize',12)
    ylabel('total','fontsize',12)
    set(gca,'linewidth',2)

    %最小二乘法
    lxx=sum((x-mean(x)).^2);
    lxy=sum((x-mean(x)).*(y-mean(y)));
    lxy=sum((x-mean(x)).*(y-mean(y)));
    b1=lxy/lxx;
    b0=mean(y)-b1*mean(x);
    y1=b1*x+b0;
    hold on
    plot(x,y1,'linewidth',2)


    %LinearModel
    m2=LinearModel.fit(x,y);


     

    %regress
    yy=y';
    xx=[ones(size(x,2),1),x'];
    [b,bint,r,rint,s]=regress(yy,xx);


     

    %nonlinear model
    x=[1.5 4.5 7.5 10.5 13.5 16.5 19.5 22.5 25.5];
    y=[7 4.8 3.6 3.1 2.7 2.5 2.4 2. 2.2];
    figure
    plot(x,y,'o','linewidth',3);
    set(gca,'linewidth',2);
    xlabel('sellNUm','fontsize',6)
    ylabel('transfee','fontsize',12)


    m1=@(b,x)b(1)+b(2)*log(x);
    nonlinfit1=fitnlm(x,y,m1,[0.01;0.01]);
    b=nonlinfit1.Coefficients.Estimate;
    yy=b(1)+b(2)*log(x);
    hold on
    plot(x,yy,'--b*')


    m2='y~ b1*x^b2';
    nonlinfit2=fitnlm(x,y,m2,[1;1]);
    b2=nonlinfit2.Coefficients.Estimate;
    y2=b2(1)*x.^b2(2);
    hold on
    plot(x,y2,'-^r','linewidth',2)


     

     除此之外,还有fit函数。

     

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  • 原文地址:https://www.cnblogs.com/lancezliang/p/10423866.html
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