view phase2/rfFeatureSelection.m @ 3:3ff93b42d454

create random forest feature selection function

function features = rfFeatureSelection(data, labels, numFeatures, iterMethod, numTrees)
%% rfFeatureSelection(data, labels, numFeatures, iterMethod, numTrees)
%
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%

if(length(labels) ~= size(data,1))
    error('labels and data do not match up');
end

if(nargin < 2)
    error('must pass data and labels into function')
end
if(nargin < 3)
    numFeatures = 5;
end
if(nargin < 4)
    iterMethod = 'onePass';
end
if(nargin < 5)
    numTrees = 200;
end


options = statset('UseParallel', true);
b = TreeBagger(numTrees, data, labels,'OOBVarImp','On',...
    'SampleWithReplacement', 'Off','FBoot', 0.632,'Options', options);
[FI,I] = sort(b.OOBPermutedVarDeltaError,'descend'); 
features = I;

if(strcmp(iterMethod,'onePass'))
    disp('cut')
    features = features(1:numFeatures);
elseif(strcmp(iterMethod,'cut10'))
    disp('cut10')
    cutSize = max(floor(length(features)/10),1);
    features = features(1:end-cutSize);
    data = data(:,I);
    features = rfFeatureSelection(data, labels, numFeatures, iterMethod, numTrees);
elseif(strcmp(iterMethod,'cut5'))
    disp('cut5')
    cutSize = max(floor(length(features)/20),1);
    features = features(1:end-cutSize);
    data = data(:,I);
    features = rfFeatureSelection(data, labels, numFeatures, iterMethod, numTrees);
end
end