之前建立了一个SVM-based Ordinal regression模型，一种特殊的多分类模型，就想通过可视化的方式展示模型分类的效果，对各个分类区域用不同颜色表示。可是，也看了很多代码，但基本都是展示二分类，当扩展成多分类时就会出现问题，所以我的论文最后就只好画了boundary的图了。今天在研究Random Forest时，找到了下面的demo的MATLAB代码，该代码很好的实现了各分类区域的颜色填充，效果非常漂亮。

 

下面是一个Demo代码：Demo.m

%% generate dataprettySpiral = true;if ~prettySpiral    % generate some random gaussian like data    rand('state', 0);    randn('state', 0);    N= 50;    D= 2;    X1 = mgd(N, D, [4 3], [2 -1;-1 2]);    X2 = mgd(N, D, [1 1], [2 1;1 1]);    X3 = mgd(N, D, [3 -3], [1 0;0 4]);    X= [X1; X2; X3];    X= bsxfun(@rdivide, bsxfun(@minus, X, mean(X)), var(X));    Y= [ones(N, 1); ones(N, 1)*2; ones(N, 1)*3];    scatter(X(:,1), X(:,2), 20, Y)else    % generate twirl data!    N= 50;    t = linspace(0.5, 2*pi, N);    x = t.*cos(t);    y = t.*sin(t);    t = linspace(0.5, 2*pi, N);    x2 = t.*cos(t+2);    y2 = t.*sin(t+2);    t = linspace(0.5, 2*pi, N);    x3 = t.*cos(t+4);    y3 = t.*sin(t+4);    X= [[x' y']; [x2' y2']; [x3' y3']];    X= bsxfun(@rdivide, bsxfun(@minus, X, mean(X)), var(X));    Y= [ones(N, 1); ones(N, 1)*2; ones(N, 1)*3];    scatter(X(:,1), X(:,2), 20, Y)end%% classifyrand('state', 0);randn('state', 0);opts= struct;opts.depth= 9;opts.numTrees= 100;opts.numSplits= 5;opts.verbose= true;opts.classifierID= 2; % weak learners to use. Can be an array for mix of weak learners tootic;m= forestTrain(X, Y, opts);timetrain= toc;tic;yhatTrain = forestTest(m, X);timetest= toc;% Look at classifier distribution for fun, to see what classifiers were% chosen at split nodes and how oftenfprintf('Classifier distributions:\n');classifierDist= zeros(1, 4);unused= 0;for i=1:length(m.treeModels)    for j=1:length(m.treeModels{i}.weakModels)        cc= m.treeModels{i}.weakModels{j}.classifierID;        if cc>1 %otherwise no classifier was used at that node            classifierDist(cc)= classifierDist(cc) + 1;        else            unused= unused+1;        end    endendfprintf('%d nodes were empty and had no classifier.\n', unused);for i=1:4    fprintf('Classifier with id=%d was used at %d nodes.\n', i, classifierDist(i));end%% plot resultsxrange = [-1.5 1.5];yrange = [-1.5 1.5];inc = 0.02;[x, y] = meshgrid(xrange(1):inc:xrange(2), yrange(1):inc:yrange(2));image_size = size(x);xy = [x(:) y(:)];[yhat, ysoft] = forestTest(m, xy);decmap= reshape(ysoft, [image_size 3]);decmaphard= reshape(yhat, image_size);subplot(121);imagesc(xrange,yrange,decmaphard);hold on;set(gca,'ydir','normal');cmap = [1 0.8 0.8; 0.95 1 0.95; 0.9 0.9 1];colormap(cmap);plot(X(Y==1,1), X(Y==1,2), 'o', 'MarkerFaceColor', [.9 .3 .3], 'MarkerEdgeColor','k');plot(X(Y==2,1), X(Y==2,2), 'o', 'MarkerFaceColor', [.3 .9 .3], 'MarkerEdgeColor','k');plot(X(Y==3,1), X(Y==3,2), 'o', 'MarkerFaceColor', [.3 .3 .9], 'MarkerEdgeColor','k');hold off;title(sprintf('%d trees, Train time: %.2fs, Test time: %.2fs\n', opts.numTrees, timetrain, timetest));subplot(122);imagesc(xrange,yrange,decmap);hold on;set(gca,'ydir','normal');plot(X(Y==1,1), X(Y==1,2), 'o', 'MarkerFaceColor', [.9 .3 .3], 'MarkerEdgeColor','k');plot(X(Y==2,1), X(Y==2,2), 'o', 'MarkerFaceColor', [.3 .9 .3], 'MarkerEdgeColor','k');plot(X(Y==3,1), X(Y==3,2), 'o', 'MarkerFaceColor', [.3 .3 .9], 'MarkerEdgeColor','k');hold off;title(sprintf('Train accuracy: %f\n', mean(yhatTrain==Y)));

 

以上具体代码见：