added README, added ability to specify output folder, and use 10 folds default, and calculate the fold size instead of the inverse

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+== This is the least square solution CCI calulator ==
+
+You'll need to run this in matlab, and have prepared your CSV's in advance. You
+should have two CSV's. One csv should be all of your concatenated feature
+vectors. One feature per column,  columns, and as many rows as you have
+specimen. The second CSV should include the actual CI data recorded and
+calculated by the biologists.
+
+In matlab the command may look like this: (note you need to be in the directory
+to call this)
+
+    [x, e] = LeastSquareSolution('../input/ALL_FVs.csv','ALL_CIs.csv',
+../output/);
+
+Alternatively you can run it without first invoking the compiler by running
+this:
+
+    matlab -r "LeastSquareSolution  ALL_FV.csv ALL_CIs.csv ../output"
+
+
+The ouput will an output specified, and will be in text files that are named
+like this: Fold_1.txt Fold_2.txt etc. You will find it somewhat annoying to have
+to concatenate all that data manually so here is a nice one-liner that allows
+you to concatenate all of your data. For the number of specimen in each fold,
+you need to setup your variables accordingly.
+
+If we have 21 specimen per fold, use number of folds + 2 for the head argument,
+and the number of folds for the tail. This will give you proper results!
+
+    for i in Fold_*.txt; do head $i -n 23 |  tail -n 21 >> output.txt; echo $i; done;