基于Matlab实现监督法学习（源码）.rar

%方式1 当处理奇异数据时，需要设置判别类型为'pseudoLinear' 或者'pseudoQuadratic' clear; load popcorn X = popcorn(:,[1 2]); %设置第三列全为0，使矩阵奇异 X(:,3) = 0; % a zero-variance column Y = popcorn(:,3); ppcrn = fitcdiscr(X,Y); this = fit(temp,X,Y); %采用伪线性方法 ppcrn = fitcdiscr(X,Y,'discrimType','pseudoLinear'); meanpredict = predict(ppcrn,mean(X)) %方式2 检查判别误差和混淆矩阵 clear; load fisheriris obj = fitcdiscr(meas,species); resuberror = resubLoss(obj) resuberror * obj.NumObservations R = confusionmat(obj.Y,resubPredict(obj)) obj.ClassNames %方式3 交叉检验 clear; load fisheriris quadisc = fitcdiscr(meas,species,'DiscrimType','quadratic'); qerror = resubLoss(quadisc) cvmodel = crossval(quadisc,'kfold',5); cverror = kfoldLoss(cvmodel) %方式4 改变代价和优先级 clear; %原始分类结果 load fisheriris obj = fitcdiscr(meas,species); resuberror = resubLoss(obj) R = confusionmat(obj.Y,resubPredict(obj)) obj.ClassNames %修改代价矩阵后结果 obj1 = obj; obj1.Cost(2,3) = 10; R2 = confusionmat(obj1.Y,resubPredict(obj1)) %修改优先级后的结果 obj2 = obj; obj2.Prior = [1 1 5]; R2 = confusionmat(obj2.Y,resubPredict(obj2)) %方式5 简化判别式分类器 %设置多组模型参数，分别生成最优结果 load ovariancancer rng(1); % For reproducibility numPred = size(obs,2); obs = obs(:,randsample(numPred,ceil(numPred/3))); Mdl = fitcdiscr(obs,grp,'SaveMemory','on','FillCoeffs','off'); [err,gamma,delta,numpred] = cvshrink(Mdl,... 'NumGamma',24,'NumDelta',24,'Verbose',1); figure; plot(err,numpred,'k.') xlabel('Errorrate'); ylabel('Numberofpredictors'); %结果局部放大 axis([0 .1 0 1000]) %所有结果的最小误差 minerr = min(min(err)) %找到最小误差附近容许误差范围的参数组 % Subscripts of err producing minimal error [p q] = find(err < minerr + 1e-4); numel(p) %注意：这里需要将行列位置转换到线性索引，直接采用delta(p,q)不是想要的结果 % Convert from subscripts to linear indices idx = sub2ind(size(delta),p,q); [gamma(p) delta(idx)] %满足条件的参数组预测参数个数与原始预测数百分比 numpred(idx)/ceil(numPred/3)*100 %如果需要更少的预测数，那么需要忍耐更高的错误率 low200 = min(min(err(numpred <= 200))); lownum = min(min(numpred(err == low200))); [low200 lownum] %注意：此时r和s个数都为1时，delta(r,s)才等价于上面提到的delta(idx) [r,s] = find((err == low200) & (numpred == lownum)); [gamma(r); delta(r,s)] %采用这组参数设置模型参数 Mdl.Gamma = gamma(r); Mdl.Delta = delta(r,s); %比较在相同的Gamma参数情况下，不同的Delta参数，对于结果的影响 % Create the Delta index matrix indx = repmat(1:size(delta,2),size(delta,1),1); figure subplot(1,2,1) imagesc(err); colorbar; colormap('jet') title 'Classification error'; xlabel 'Delta index'; ylabel 'Gamma index'; subplot(1,2,2) imagesc(numpred); colorbar; title 'Number of predictors in the model'; xlabel 'Delta index' ; ylabel 'Gamma index' ;