标签:ext == pac 实现 res nts tput 产生 学习
一、简单的多元线性回归:
data.txt
1,230.1,37.8,69.2,22.1 2,44.5,39.3,45.1,10.4 3,17.2,45.9,69.3,9.3 4,151.5,41.3,58.5,18.5 5,180.8,10.8,58.4,12.9 6,8.7,48.9,75,7.2 7,57.5,32.8,23.5,11.8 8,120.2,19.6,11.6,13.2 9,8.6,2.1,1,4.8 10,199.8,2.6,21.2,10.6 11,66.1,5.8,24.2,8.6 12,214.7,24,4,17.4 13,23.8,35.1,65.9,9.2 14,97.5,7.6,7.2,9.7 15,204.1,32.9,46,19 16,195.4,47.7,52.9,22.4 17,67.8,36.6,114,12.5 18,281.4,39.6,55.8,24.4 19,69.2,20.5,18.3,11.3 20,147.3,23.9,19.1,14.6 21,218.4,27.7,53.4,18 22,237.4,5.1,23.5,12.5 23,13.2,15.9,49.6,5.6 24,228.3,16.9,26.2,15.5 25,62.3,12.6,18.3,9.7 26,262.9,3.5,19.5,12 27,142.9,29.3,12.6,15 28,240.1,16.7,22.9,15.9 29,248.8,27.1,22.9,18.9 30,70.6,16,40.8,10.5 31,292.9,28.3,43.2,21.4 32,112.9,17.4,38.6,11.9 33,97.2,1.5,30,9.6 34,265.6,20,0.3,17.4 35,95.7,1.4,7.4,9.5 36,290.7,4.1,8.5,12.8 37,266.9,43.8,5,25.4 38,74.7,49.4,45.7,14.7 39,43.1,26.7,35.1,10.1 40,228,37.7,32,21.5 41,202.5,22.3,31.6,16.6 42,177,33.4,38.7,17.1 43,293.6,27.7,1.8,20.7 44,206.9,8.4,26.4,12.9 45,25.1,25.7,43.3,8.5 46,175.1,22.5,31.5,14.9 47,89.7,9.9,35.7,10.6 48,239.9,41.5,18.5,23.2 49,227.2,15.8,49.9,14.8 50,66.9,11.7,36.8,9.7 51,199.8,3.1,34.6,11.4 52,100.4,9.6,3.6,10.7 53,216.4,41.7,39.6,22.6 54,182.6,46.2,58.7,21.2 55,262.7,28.8,15.9,20.2 56,198.9,49.4,60,23.7 57,7.3,28.1,41.4,5.5 58,136.2,19.2,16.6,13.2 59,210.8,49.6,37.7,23.8 60,210.7,29.5,9.3,18.4 61,53.5,2,21.4,8.1 62,261.3,42.7,54.7,24.2 63,239.3,15.5,27.3,15.7 64,102.7,29.6,8.4,14 65,131.1,42.8,28.9,18 66,69,9.3,0.9,9.3 67,31.5,24.6,2.2,9.5 68,139.3,14.5,10.2,13.4 69,237.4,27.5,11,18.9 70,216.8,43.9,27.2,22.3 71,199.1,30.6,38.7,18.3 72,109.8,14.3,31.7,12.4 73,26.8,33,19.3,8.8 74,129.4,5.7,31.3,11 75,213.4,24.6,13.1,17 76,16.9,43.7,89.4,8.7 77,27.5,1.6,20.7,6.9 78,120.5,28.5,14.2,14.2 79,5.4,29.9,9.4,5.3 80,116,7.7,23.1,11 81,76.4,26.7,22.3,11.8 82,239.8,4.1,36.9,12.3 83,75.3,20.3,32.5,11.3 84,68.4,44.5,35.6,13.6 85,213.5,43,33.8,21.7 86,193.2,18.4,65.7,15.2 87,76.3,27.5,16,12 88,110.7,40.6,63.2,16 89,88.3,25.5,73.4,12.9 90,109.8,47.8,51.4,16.7 91,134.3,4.9,9.3,11.2 92,28.6,1.5,33,7.3 93,217.7,33.5,59,19.4 94,250.9,36.5,72.3,22.2 95,107.4,14,10.9,11.5 96,163.3,31.6,52.9,16.9 97,197.6,3.5,5.9,11.7 98,184.9,21,22,15.5 99,289.7,42.3,51.2,25.4 100,135.2,41.7,45.9,17.2 101,222.4,4.3,49.8,11.7 102,296.4,36.3,100.9,23.8 103,280.2,10.1,21.4,14.8 104,187.9,17.2,17.9,14.7 105,238.2,34.3,5.3,20.7 106,137.9,46.4,59,19.2 107,25,11,29.7,7.2 108,90.4,0.3,23.2,8.7 109,13.1,0.4,25.6,5.3 110,255.4,26.9,5.5,19.8 111,225.8,8.2,56.5,13.4 112,241.7,38,23.2,21.8 113,175.7,15.4,2.4,14.1 114,209.6,20.6,10.7,15.9 115,78.2,46.8,34.5,14.6 116,75.1,35,52.7,12.6 117,139.2,14.3,25.6,12.2 118,76.4,0.8,14.8,9.4 119,125.7,36.9,79.2,15.9 120,19.4,16,22.3,6.6 121,141.3,26.8,46.2,15.5 122,18.8,21.7,50.4,7 123,224,2.4,15.6,11.6 124,123.1,34.6,12.4,15.2 125,229.5,32.3,74.2,19.7 126,87.2,11.8,25.9,10.6 127,7.8,38.9,50.6,6.6 128,80.2,0,9.2,8.8 129,220.3,49,3.2,24.7 130,59.6,12,43.1,9.7 131,0.7,39.6,8.7,1.6 132,265.2,2.9,43,12.7 133,8.4,27.2,2.1,5.7 134,219.8,33.5,45.1,19.6 135,36.9,38.6,65.6,10.8 136,48.3,47,8.5,11.6 137,25.6,39,9.3,9.5 138,273.7,28.9,59.7,20.8 139,43,25.9,20.5,9.6 140,184.9,43.9,1.7,20.7 141,73.4,17,12.9,10.9 142,193.7,35.4,75.6,19.2 143,220.5,33.2,37.9,20.1 144,104.6,5.7,34.4,10.4 145,96.2,14.8,38.9,11.4 146,140.3,1.9,9,10.3 147,240.1,7.3,8.7,13.2 148,243.2,49,44.3,25.4 149,38,40.3,11.9,10.9 150,44.7,25.8,20.6,10.1 151,280.7,13.9,37,16.1 152,121,8.4,48.7,11.6 153,197.6,23.3,14.2,16.6 154,171.3,39.7,37.7,19 155,187.8,21.1,9.5,15.6 156,4.1,11.6,5.7,3.2 157,93.9,43.5,50.5,15.3 158,149.8,1.3,24.3,10.1 159,11.7,36.9,45.2,7.3 160,131.7,18.4,34.6,12.9 161,172.5,18.1,30.7,14.4 162,85.7,35.8,49.3,13.3 163,188.4,18.1,25.6,14.9 164,163.5,36.8,7.4,18 165,117.2,14.7,5.4,11.9 166,234.5,3.4,84.8,11.9 167,17.9,37.6,21.6,8 168,206.8,5.2,19.4,12.2 169,215.4,23.6,57.6,17.1 170,284.3,10.6,6.4,15 171,50,11.6,18.4,8.4 172,164.5,20.9,47.4,14.5 173,19.6,20.1,17,7.6 174,168.4,7.1,12.8,11.7 175,222.4,3.4,13.1,11.5 176,276.9,48.9,41.8,27 177,248.4,30.2,20.3,20.2 178,170.2,7.8,35.2,11.7 179,276.7,2.3,23.7,11.8 180,165.6,10,17.6,12.6 181,156.6,2.6,8.3,10.5 182,218.5,5.4,27.4,12.2 183,56.2,5.7,29.7,8.7 184,287.6,43,71.8,26.2 185,253.8,21.3,30,17.6 186,205,45.1,19.6,22.6 187,139.5,2.1,26.6,10.3 188,191.1,28.7,18.2,17.3 189,286,13.9,3.7,15.9 190,18.7,12.1,23.4,6.7 191,39.5,41.1,5.8,10.8 192,75.5,10.8,6,9.9 193,17.2,4.1,31.6,5.9 194,166.8,42,3.6,19.6 195,149.7,35.6,6,17.3 196,38.2,3.7,13.8,7.6 197,94.2,4.9,8.1,9.7 198,177,9.3,6.4,12.8 199,283.6,42,66.2,25.5 200,232.1,8.6,8.7,13.4
回归代码:
% A=importdata(‘data.txt‘,‘ ‘,200);%????????A.data a = load(‘data.txt‘); x1=a(:,[2]) ; x2=a(:,[3]) ; x3=a(:,[4]) ; y=a(:,[5]); X=[ones(length(y),1), x1,x2,x3]; [b,bint,r,rint,stats]=regress(y,X); b;bint;stats; rcoplot(r,rint) tx=[230.1,37.8,69.2]; b2=[b(2),b(3),b(4)]; ty=b(1)+b2*tx‘; ty;
简单的得到一个变换的公式
y=b(1)+b(2)*x1+b(3)*x2+b(3)*x3;
其实就是在回归前对数据进行预处理,去掉一些偏差数据的影响。
在处理复杂的数据的回归问题时,普通的线性回归会遇到一些问题,主要表现在:
以上的这些问题,主要就是表现在模型的方差和偏差问题上,这样的关系可以通过下图说明:
(摘自:机器学习实战)
方差指的是模型之间的差异,而偏差指的是模型预测值和数据之间的差异。我们需要找到方差和偏差的折中。
在进行特征选择时,一般有三种方式:
岭回归(Ridge Regression)是在平方误差的基础上增加正则项
,
通过确定的值可以使得在方差和偏差之间达到平衡:随着的增大,模型方差减小而偏差增大。
对求导,结果为
令其为0,可求得的值:
我们去探讨一下取不同的对整个模型的影响。
MATLAB代码
function [ w ] = ridgeRegression( x, y, lam ) xTx = x‘*x; [m,n] = size(xTx); temp = xTx + eye(m,n)*lam; if det(temp) == 0 disp(‘This matrix is singular, cannot do inverse‘); end w = temp^(-1)*x‘*y; end
%% ???(Ridge Regression) clc; %???? data = load(‘data.txt‘); [m,n] = size(data); dataX = data(:,2:4);%?? dataY = data(:,5);%?? %??? yMeans = mean(dataY); for i = 1:m yMat(i,:) = dataY(i,:)-yMeans; end xMeans = mean(dataX); xVars = var(dataX); for i = 1:m xMat(i,:) = (dataX(i,:) - xMeans)./xVars; end % ??30? testNum = 30; weights = zeros(testNum, n-2); for i = 1:testNum w = ridgeRegression(xMat, yMat, exp(i-10)); weights(i,:) = w‘; end % ??????lam hold on axis([-9 20 -1.0 2.5]); xlabel log(lam); ylabel weights; for i = 1:n-2 x = -9:20; y(1,:) = weights(:,i)‘; plot(x,y); end
plot出来的图像显示,k=5的时候,出现了拟合,因此取k=5时的w值,
% resualt output ,i=5
w = ridgeRegression(xMat, yMat, exp(5-10));
三、另外一个岭回归比较好的例子
function [b,bint,r,rint,stats] = ridge1(Y,X,k) [n,p] = size(X); mx = mean (X); my = mean (Y); stdx = std(X); stdy=std(Y); idx = find(abs(stdx) < sqrt(eps)); MX = mx(ones(n,1),:); STDX = stdx(ones(n,1),:); Z = (X - MX) ./ STDX;Y=(Y-my)./stdy; pseudo = sqrt(k*(n-1)) * eye(p); Zplus = [Z;pseudo]; Yplus = [Y;zeros(p,1)]; [b,bint,r,rint,stats] = regress(Yplus,Zplus); end
x=[71.35 22.90 3.76 1158.18 12.20 55.87; 67.92 34048 17.11 1494.38 19.82 56.60; 79.38 24.91 33.60 691.56 16.17 92.78; 87.97 10.18 0.73 923.04 12.15 24.66; 59.03 7.71 3.58 696.92 13.50 61.81; 55.23 22.94 1.34 1083.84 10.76 49.79; 58.30 12.78 5.25 1180.36 9.58 57.02; 67.43 9.59 2.92 797.72 16.82 38.29; 76.63 15.12 2.55 919.49 17.79 32.07]; y=[28.46;27.76;26.02;33.29;40.84;44.50;28.09;46.24; 45.21]; x‘*x; count=0; kvec=0.1:0.1:1; for k=0.1:0.1:1 count=count+1; [b,bint,r,rint,stats]=ridge1(y,x,k); bb(:,count)=b; stats1(count,:)=stats; end bb‘,stats1 plot(kvec‘,bb),xlabel(‘k‘),ylabel(‘b‘,‘FontName‘,‘Symbo l‘)
从运行结果及图1可见,k≥0.7时每个变量相应
的岭回归系数变化较为稳定,因而可选k=0.7,建立 岭回归方程
y=-0.219 5x1-0.120 2x2-0.237 8x3- 0.244 6x4+0.203 6x5-0.249 4x6
标签:ext == pac 实现 res nts tput 产生 学习
原文地址:http://www.cnblogs.com/Anita9002/p/7699550.html