Add a dummy generated decisionTree.py
Correctly compute chi-square deviation value
Finish prune_rec. Succesful test on the xor data
Fix bugs with subexs. Now testDecisionTree F1 goes from 0.72 to 0.74
Update testDecisionTree to automatically select the best validation
Complete and verified dt.count_nodes()
Complete the python if-elif decision code gen
Add matplotlib graph the learning curve
Prune subtrees first, then itself
Add graphing
make [0,2,3,4] be range(1,5)