function [performance] = CrispOutputs(fis,trainingdata,validationdata,testingdata,indicesinputs,indicesoutputs,combination);

inputtrainingdata = trainingdata(:,indicesinputs(combination,:));

outputsdata = trainingdata(:,indicesoutputs(combination,:));

fisoutputstrainingdata = evaluate the performance with the inputtrainingdata variable.

calculate correlation coefficient (regression) using outputsdata and fisoutputtrainingdata;

calculate the square of the correlation coefficient (R2)

inputvalidationdata = validationdata(:,indicesinputs(combination,:));

outputsdata = validationdata(:,indicesoutputs(combination,:));

fisoutputsvalidationdata = evaluate the performance with the inputvalidationdata variable.

calculate correlation coefficient (regression) using outputsdata and fisoutputvalidationdata;

calculate the square of the correlation coefficient (R2)

inputtestingdata = testingdata(:,indicesinputs(combination,:));

outputsdata = testingdata(:,indicesoutputs(combination,:));

fisoutputstestingdata = evaluate the performance with the inputtestingdata variable.

calculate correlation coefficient (regression) using outputsdata and fisoutputtestingdata;

calculate the square of the correlation coefficient (R2);

Calculate the total performance using the complete dataset (training, validation, and testing).

Calculate correlation coefficient (regression) using totaloutputsdata and fisoutputtotaldata.

Calculate the square of the correlation coefficient (R2).

performance = [R2_total,R2_training,R2_validation,R2_testing];

end