added features. yes its late, dont judge me

toni/octave/functions.m

@@ -167,3 +167,80 @@ windowedData = {};
   end  
 end
 
+function features = featureCalculation(data)
+
+features = [];
+
+  for k = 1:numel(data)
+    for j = 1:numel(data{k}.samples)
+      for i = 1:numel(data{k}.samples{j}.raw)
+        for m = 1:numel(data{k}.samples{j}.raw{i}.wins)
+          currentWindow = data{k}.samples{j}.raw{i}.wins{m};
+          currentWindow = currentWindow(:,1);
+          
+          #autocorrelation on window. split into 5 evenly spaced bins (frequencies are evenly spaced, not number of values ;) ) and calculate mean of bin. 
+          [autoCorr] = xcorr(currentWindow);
+          [binNum, binCenter] = hist(autoCorr, 5); #define 5 bins for the data.
+          binSize = abs(binCenter(end-1) - binCenter(end));
+          binEdges = linspace(binCenter(1)-(binSize/2), binCenter(end)+(binSize/2), 6);
+          [binNumc, binIdx] = histc(autoCorr, binEdges);
+          binMeans = getBinMean(autoCorr, binIdx, 5);
+          
+          #calculate the root-mean-square (RMS) of the signal
+          rms = sqrt(mean(currentWindow.^2));
+          
+          #power bands 0.5 to 25hz (useful if the windows are greater then 4s and window sizes to 256, 512..) 
+          [powerBand, w] = periodogram(currentWindow); #fills up fft with zeros            
+          powerEdges = logspace(log10(0.5), log10(25), 10 + 2);  #logarithmic bin spaces for 10 bins
+          triFilter = getTriangularFunction(powerEdges, length(powerBand)*2 - 2);    
+          for l = 1:numel(triFilter)
+            filteredBand = triFilter{l} .* powerBand;
+            psd(l) = sum(filteredBand); #sum freq (no log and no dct)
+          end
+          
+          #put everything together
+          classLabel = k; #what class?
+          features = [features; classLabel, binMeans, rms, psd];
+        end
+      end 
+    end
+  end
+end
+
+function value = getBinMean(data, idx, numBins)
+
+
+value = [];
+  for i = 1:numBins
+    flagBinMembers = (idx == i);
+    binMembers = data(flagBinMembers);
+    
+   # if length(binMembers) == 0
+     # idx
+      #data
+     # input = 'balala'
+    #endif
+    
+    value(i) = mean(binMembers);
+  end  
+end
+
+#triangular functions. (edges of the triangles; num fft values -> nfft.)
+function triFilter = getTriangularFunction(edges, nfft)
+
+  #get idx of the edges within the samples. thanks to fft each sample represents a frequency. 
+  # idx * samplerate / nfft = hertz of that idx
+  for i = 1:length(edges)
+    edgesByIdx(i) = floor((nfft + 1) * edges(i)/100); #100hz is the samplerate
+  end
+  
+  #generate the triangle filters
+  triFilter = {};
+  for i = 1:length(edgesByIdx)-2
+    diffCnt = edgesByIdx(i+2) - edgesByIdx(i) + 1;
+    tmp = zeros(nfft/2 + 1, 1);
+    triVec = triang(diffCnt);
+    tmp(edgesByIdx(i):edgesByIdx(i+2)) = triVec;
+    triFilter{i} = tmp;
+  end
+end