一、簡單的多元線性回歸：

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

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回歸代碼：

% A=importdata('data.txt',' ',200);%????????A.dataa = 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;

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簡單的得到一個變換的公式

y=b(1)+b(2)*x1+b(3)*x2+b(3)*x3;

二、ridge regression嶺回歸

　　其實就是在回歸前對數據進行預處理，去掉一些偏差數據的影響。

1、一般線性回歸遇到的問題

? ? 在處理復雜的數據的回歸問題時，普通的線性回歸會遇到一些問題，主要表現在：

• 預測精度：這里要處理好這樣一對為題，即樣本的數量和特征的數量
  • 時，最小二乘回歸會有較小的方差
  • 時，容易產生過擬合
  • 時，最小二乘回歸得不到有意義的結果
• 模型的解釋能力：如果模型中的特征之間有相互關系，這樣會增加模型的復雜程度，并且對整個模型的解釋能力并沒有提高，這時，我們就要進行特征選擇。

以上的這些問題，主要就是表現在模型的方差和偏差問題上，這樣的關系可以通過下圖說明：

（摘自：機器學習實戰）

方差指的是模型之間的差異，而偏差指的是模型預測值和數據之間的差異。我們需要找到方差和偏差的折中。

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2、嶺回歸的概念

? ? 在進行特征選擇時，一般有三種方式：

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• 子集選擇
• 收縮方式(Shrinkage method)，又稱為正則化(Regularization)。主要包括嶺回歸個lasso回歸。
• 維數縮減

? ? 嶺回歸(Ridge Regression)是在平方誤差的基礎上增加正則項

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通過確定的值可以使得在方差和偏差之間達到平衡：隨著的增大，模型方差減小而偏差增大。

? ? 對求導，結果為

令其為0，可求得的值：

3、實驗的過程

? ? 我們去探討一下取不同的對整個模型的影響。

MATLAB代碼

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

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%% ???(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出來的圖像顯示，k=5的時候，出現了擬合，因此取k=5時的w值，

% resualt output ,i=5

?w = ridgeRegression(xMat, yMat, exp(5-10)); ?

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三、另外一個嶺回歸比較好的例子

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

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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=0.1:0.1:1count=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')

從運行結果及圖1可見，k≥0.7時每個變量相應

的嶺回歸系數變化較為穩定，因而可選k=0.7，建立 嶺回歸方程

y=-0.219 5x1-0.120 2x2-0.237 8x3- 0.244 6x4+0.203 6x5-0.249 4x6?

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轉載于:https://www.cnblogs.com/Anita9002/p/7699550.html

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