function [knowledgeRuleBase] = knowledgeRB(round,training, validation,testing,numvarinputs)

for a = 2:numvarinputs percent, it could be the same numvarinputs if you work with the same number of input variables.

numvars = a;

n = 1:1:numvarinputs;

combinations = nchoosek(n,numvars);

for i = 1:size(combinations,1)

for j = 1:size(combinations,2)

clustersinputsindices(i,j) = combinations(i,j) *2;

end

end

calculate the clustersoutputsindices;

for i = 1:size(combinations,1)

[RuleBaseInputs,ia,ic] = unique(training(:,clustersinputsindices(i,:)),’rows’,’sorted’); %pivot tables

RuleBaseOutputs = training(ia,clustersoutputsindices(i,:));

RuleBase = [RuleBaseInputs RuleBaseOutputs];

weightsandconnections = ones(size(RuleBase,1),2);

[knowledgeRuleBase] = [RuleBase weightsandconnections];

InferenceEngineandDefuzzification(combination,trainingdata,validationdata,testdata,knowledgeRuleBase,indicesinputs,indicesoutputs);

end

end

end