问题如题啦，急求能在matlab运行的（最好是matlab7.0），ID3决策树源码，最好里面有注释或者说明，能让人看明白的。非常非常感谢啦！！！
或者，帮忙给下面这段源码加上注释，同样感激不尽！！！
function D = ID3(train_features, train_targets, params, region)
% Classify using Quinlan´s ID3 algorithm
% Inputs:
% features - Train features
% targets - Train targets
% params - [Number of bins for the data, Percentage of incorrectly assigned samples at a node]
% region - Decision region vector: [-x x -y y number_of_points]
%
% Outputs
% D - Decision sufrace
[Ni, M] = size(train_features);
%Get parameters
[Nbins, inc_node] = process_params(params);
inc_node = inc_node*M/100;
%For the decision region
N = region(5);
mx = ones(N,1) * linspace (region(1),region(2),N);
my = linspace (region(3),region(4),N)´ *ones(1,N);
flatxy = [mx(:), my(:)]´;
%Preprocessing
[f, t, UW, m] = PCA(train_features, train_targets, Ni, region);
train_features = UW * (train_features - m*ones(1,M));;
flatxy = UW * (flatxy - m*ones(1,N^2));;
%First, bin the data and the decision region data
[H, binned_features]= high_histogram(train_features, Nbins, region);
[H, binned_xy] = high_histogram(flatxy, Nbins, region);
%Build the tree recursively
disp(´Building tree´)
tree = make_tree(binned_features, train_targets, inc_node, Nbins);
%Make the decision region according to the tree
disp(´Building decision surface using the tree´)
targets = use_tree(binned_xy, 1:N^2, tree, Nbins, unique(train_targets));
D = reshape(targets,N,N);
%END
function targets = use_tree(features, indices, tree, Nbins, Uc)
%Classify recursively using a tree
targets = zeros(1, size(features,2));
if (size(features,1) == 1),
%Only one dimension left, so work on it
for i = 1:Nbins,
in = indices(find(features(indices) == i));
if ~isempty(in),
if isfinite(tree.child(i)),
targets(in) = tree.child(i);
else
%No data was found in the training set for this bin, so choose it randomally