Fix error where error metric is not computed for unconditional selection

compute_spectrum_error.m

@@ -3,26 +3,22 @@
 % spectrum
 
 
-
 % Calculate the deviation from the target
-if selectionParams.optType == 0
-    if selectionParams.cond == 1 || (selectionParams.cond == 0 && selectionParams.isScaled == 0)
-        % Compute deviations from target
-        sampleMean = sum(sampleSmall)/size(sampleSmall,1);
-        sampleVar = sum((sampleSmall - ones(size(sampleSmall,1),1)*sampleMean).^2) / size(sampleSmall,1);
-        devMean = sampleMean - targetSa.meanReq;
-        devSig = sqrt(sampleVar) - targetSa.stdevs;
-        devTotal = selectionParams.weights(1) * sum(devMean.^2) + selectionParams.weights(2) * sum(devSig.^2);
-    end
+if selectionParams.optType == 0 % computed weighted sum of squared errors of mean and standard deviation
+    % Compute deviations from target
+    sampleMean = sum(sampleSmall)/size(sampleSmall,1);
+    sampleVar = sum((sampleSmall - ones(size(sampleSmall,1),1)*sampleMean).^2) / size(sampleSmall,1);
+    devMean = sampleMean - targetSa.meanReq;
+    devSig = sqrt(sampleVar) - targetSa.stdevs;
+    devTotal = selectionParams.weights(1) * sum(devMean.^2) + selectionParams.weights(2) * sum(devSig.^2);
 
     % Penalize bad spectra (set penalty to zero if this is not required)
     if selectionParams.penalty ~= 0
         for m=1:size(sampleSmall,2) % for each period
             devTotal = devTotal + sum(abs(exp(sampleSmall(:,m))>exp(targetSa.meanReq(m)+3*targetSa.stdevs(m)))) * selectionParams.penalty;
         end
     end
-
-elseif selectionParams.optType == 1
+elseif selectionParams.optType == 1 % compute Kolmogorov-Smirnov error metric
     sortedlnSa = [min(sampleSmall); sort(sampleSmall)];
     norm_cdf = normcdf(sortedlnSa,repmat(targetSa.meanReq,selectionParams.nGM+1,1),repmat(targetSa.stdevs,selectionParams.nGM+1,1));
     emp_cdf = linspace(0,1,selectionParams.nGM+1);