模式識別第二次作業(yè)

1、-8-6-4-20246式識別第二次作業(yè)1、調(diào)用seqminopt. m函數(shù)（matlab匸具箱函數(shù)）替代svm_matlab中的fmincon函數(shù)。seqminopt函數(shù)的調(diào)用方法如下形式：ALPHA OFFSET二seqminopt (TWINING, Y, BOXC, KERNELFUNC, SMOOPTIONS)輸入輸出各變量含義如下：TRAINING待訓(xùn)練數(shù)據(jù)Y列向量，代表響應(yīng)數(shù)據(jù)，為1BOXC松弛變量，列向量KERNELFUNC句柄SMOOPTIONS可選，用于設(shè)置迭代次數(shù)等ALPHA優(yōu)化得到的aOFFSET超平面偏移使用fmincon函數(shù)結(jié)果為:W_fmincon =0. 158

2、10.31000. 0900用seqminopt替換fmincon后結(jié)果為：W_seqminopt =0. 15810.31000. 0900可以發(fā)現(xiàn)兩種形式下結(jié)果是一樣的。設(shè)置100個(gè)離散點(diǎn)，分類結(jié)果為:8程序close all;clear all;clc%生成訓(xùn)練數(shù)據(jù)Objl.NTrain二100;Objl. mean = 1 * 3; 5;Obj1. SampTrain = randn (2,Objl. NTrain);Objl.NTest = 50;Objl. NTrain)+ kron(Objl. mean,ones (1,Obj1. SampTest = randn(2,Objl.

3、NTest);Objl.NTest)+ kron (Obj 1. mean,ones (1,0bj2. NTrain = 100;0bj2. mean = -1 * 3; 5;Obj2 SampTrain = randn(2, Obj2.NTrain);Obj2. NTrain)+ kron(Obj2 mean,ones (1,0bj2. NTest = 50;Obj2 .SampTest = randn(2,0bj2. NTest)+ kron (Obj2 mean,ones (1,Obj2.NTest);TestMatrix = zeros (2):cnt_cl = 1:cnt_c2 =

4、1:tt = 1 : 1000;tt = (tt一500) * 0. 01;figure;plot (Obj 1. SampTrain(l, :), Objl SampT:rdin(2, :), bo,Jlinewidth, 2,markersize, 5)hold on;plot (Obj2 SdmpT:rain(l, :), Obj2 SanipTrdin(2, :), ko，Jlinewidth, 2,markersize, 5)%要訓(xùn)練的數(shù)據(jù)及其類別ent = 1:X = zeros(Objl. NTrain + Objl. NTrain, 3);for iii = 1 : Objl.

5、 NTrainX(cnt, 1 : 2) = Obj 1. SampTrain(:, iii);X(cnt, 3) = 1;y(cnt, 1) = 1;ent = ent + 1;endfor iii = 1 : 0bj2. NTrainX(cnt, 1 : 2) = Obj2 SampTipinC, iii);X(cnt, 3) = 1;y(cnt, 1) = -1；ent = ent + 1;end%替換為seqminopt后不用下面這個(gè)ent = 1:Xsvm = zeros(Obj1. NTrain + Obj1 NTrain, 3);for iii = 1 : Obj1. NTrai

6、nXsvm (ent,Xsvm (ent,ysvm (ent,ent = ent1 ： 2) = 1 * Objl. SampTrain(:, iii);3) = -1 * 1;1) = -1；+ 1；endfor iii = 1 :Obj2. NTrainXsvm (ent, 1：2)= Obj2. SampTrain(：, iii):Xsvm (ent, 3) = 1；ysvm (ent, 1) = -1；ent = ent+ 1；end%下面就是替換為seqminopttraindata=X(：, 1 : 2) ;%用X而不是Xsvm trainlabel=y;%用y而非ysvmboxC

7、onstraints= ones (size (trainlabel) :%松弛變量，我默認(rèn)為1kfun = 1 inear_kernel;%線性函數(shù)句柄alphas, bias = seqminopt(traindata, tMinlabel, boxConstraints, kfun);svlndex = find (alphas sqrt (eps) ;%尋找支持向量的索引sv =traindata(svlndex,：);%對應(yīng)x(i)alphaHat = trainlabel (svlndex) alphas (svlndex) ;%對應(yīng)y(i)* a (i)weight二zeros(

8、1, 2) ;%W=sum(y(i)* a (i)*x(i)for i=l:length(svlndex)weight=weight+alphaHat(i)*sv(i,:);endW=zeros (3, 1);W(l:2)=weight;W(3)=bias;W_seqminopt=W%用來做對比%fmincon_options = optimsetCAlgorithm, interiorpoint,Maxlter, 50);Wl, val, exflag, output = fmincon(x) (norm(x(l : 2), 2), 0; 0;1，Xsvm, ysvm, ,，, , fmin

9、con_options);W_fmincon=Wl%做對比%for kkk = 1 : Objl. NTestTMP.Tr(1:2, 1) = Objl. SampTest(:, kkk);TMP_Tr(3, 1) = 1;yy二W. * TMP_Tr;if (yy 0)Result_cl(:, cnt_cl) = Obj1 SampTest(:, kkk); ent cl = ent cl + 1;TestMatrix(l, 1) = TestMatrix(l, 1) + 1; elseResult_c2(:, cnt_c2) = Objl. SampTest (:, kkk); ent c

10、2 = ent c2 + 1;TestMatrix(l, 2) = TestMatrix(l, 2) + 1;endendfor kkk = 1 : Obj2. NTestTMP_Tr(l:2, 1) = Obj2. SampTest(:, kkk):TMP_Tr(3, 1) = 1;yy二W. * TMP_Tr;if (yy 0)Resulted (:, cnt_cl) = Obj2 SampTest (:，kkk); ent cl = ent cl + 1;TestMatrix(2, 1) = TestMatrix (2, 1) + 1; elseResult_c2(:, cnt_c2)

11、= Obj2. SampTest (:, kkk); ent c2 = ent c2 + 1;TestMatrix(2, 2) = TestMatrix(2, 2) + 1;endendTestMatrix(1, :) = TestMatrix(1, :) / Objl. NTest;TestMatrix(2, :) = TestMatrix(2, :) / Obj2. NTest;TestMatrixfigure;tryplot (Resulted (1,：), Result_cl (2,：), bo, Tinewidth, 2markersize, 5)endhold ontryplot (Result_c2 (1,:), Result_c2 (2,:), ko，Tinewidth, 2,markersize, 5)end% plot (Objl SampTraind, :), Obj 1 SampTrain(2, :), r+, linewid th, 2,markersize, 10)% plot (Obj2.