多分类问题中，实现不同分类区域颜色填充的MATLAB代码（demo：Random Forest）_华东

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多分类问题中，实现不同分类区域颜色填充的MATLAB代码（demo：Random Forest）

(2016-03-06 20:01:54)

标签：

随机森林

多分类

matlab

分类：数据挖掘

多分类问题中，实现不同分类区域颜色填充的MATLAB代码（demo：Random Forest）

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。

http://images2015.cnblogs.com/blog/543473/201603/543473-20160306191535940-139414959.jpgForest）" />

下面是一个Demo代码：Demo.m

%% generate data

prettySpiral = 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

%% classify

rand('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 too

tic;

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 often

fprintf('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

fprintf('%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 results

xrange = [-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：https://github.com/karpathy/Random-Forest-Matlab

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