Hi Ilya

Here is a brief description of each annimation, created using your package.
This was a standard signal/background discrimination task in 2-dimensions.


1. fig_ptmet_DT.avi
	This shows the tree-structure in the 2 variables. Each frame 
	corresponds to be a different tree in the sequence of binary
	decision trees. This annimation illustrates the fact that a DT
	is just a clever way to histogram data (with non-uniform bins)
	in n-dimensions. Moreover, a BDT is just an average over these
	histograms.

2. fig_BDT_train.avi

	This shows the BDT output for signal and background. Each
	frame corresponds to the distribution created using a BDT with 
	increasing numbers of trees. (The number grows from 10 to 1000.) 
	The point here is the observation that if one has a sufficiently 
	large number of trees the AdaBoost algorithm will yield perfect 
	separation on the training sample.


3. fig_BDT_test.avi
	Same as 2. except using an independent test sample.