ID3算法

function test_targets = ID3(train_patterns, train_targets, test_patterns, params)

% Classify using Quinlan's ID3 algorithm
% Inputs:
%  train_patterns - Train patterns
% train_targets - Train targets
%   test_patterns   - Test  patterns
% params     - [Number of bins for the data, Percentage of incorrectly assigned samples at a node]
%
% Outputs
% test_targets - Predicted targets

[Ni, M]    = size(train_patterns);

%Get parameters
[Nbins, inc_node] = process_params(params);
inc_node          = inc_node*M/100;

%First, bin the data and the decision region data
[H, binned_train_patterns, region]  = high_histogram(train_patterns, Nbins);
[H, binned_test_patterns]           = high_histogram(test_patterns, Nbins, region);

%Build the tree recursively
disp('Building tree')
tree        = make_tree(binned_train_patterns, train_targets, inc_node, Nbins);

%Classifying test patterns according to the tree
disp('Classifying test patterns using the tree')
test_targets    = use_tree(binned_test_patterns, 1:size(test_patterns,2), tree, Nbins, unique(train_targets));