T=textread('E:\datasets-orreview\discretized-regression\10bins\abalone10\matlab\test_abalone10.2');

X=textread('E:\datasets-orreview\discretized-regression\10bins\abalone10\matlab\train_abalone10.2');

%nTree = round(sqrt(size(X,2)-1));

nTree = 50;

train_data = X(:,1:end-1);train_label = X(:,end); test_data = T(:,1:end-1);

Factor = TreeBagger(nTree, train_data, train_label,'Method','regression');

[Predict_label,Scores] = predict(Factor, test_data);

%Predict_label=cellfun(@str2num,Predict_label(1:end));

MZE = mean(round(Predict_label) ~= T(:,end))

MAE = mean(abs(round(Predict_label) - T(:,end)))

T=textread('E:\datasets-orreview\ordinal-regression\ERA\matlab\test_ERA.1');

X=textread('E:\datasets-orreview\ordinal-regression\ERA\matlab\train_ERA.1');

opts= struct;

opts.depth= 9;

opts.numTrees= 60;

opts.numSplits= 5;

opts.verbose= true;

opts.classifierID= 2; % weak learners to use. Can be an array for mix of weak learners too

train_data = X(:,1:end-1);train_label = X(:,end); test_data = T(:,1:end-1);

tic;

m= forestTrain(train_data, train_label, opts);

timetrain= toc;

tic;

yhatTrain = forestTest(m, test_data);

timetest= toc;

MZE = mean(round(yhatTrain) ~= T(:,end))

MAE = mean(abs(round(yhatTrain) - T(:,end)))