Update picolrn.c

How to calculate Weighted Mean Square Error, WMSE, to generate a decision tree?
Find the best attribute/feature to split the training set. Attribute/feature of an image is a two-pixel comparision.
So there are many possible splits of a training set by the any two-pixel pair, because there are many two-pixel pair in an image.
The training samples/set is divided into two classes/groups(+1,-1) by each feature generated from generate_binary_test()
the split is a "hypothesis" to face(+1) and non-face (-1), so it may be different to the ground-truth.
Each feature will be a root node of binary stump : http://en.wikipedia.org/wiki/Decision_stump
Each feature then splits the whole training set into two subtrees, then the Weighted Mean Sqaure Error can be calculated.
Then find the minimum WSME from all the calculated WSME for each node(feature).
The feature which has the minumum WMSE is the root node to split the training set.
This recursive iteration from tree top to the desired depth. It's not possible to use all possible features to generate the decision tree.