matlab实现的ID3 分类决策树 算法.docx

function D = ID3(train_features, train_targets, params, region)% Classify using Quinlans 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); %·μ??DDêyNioíáDêyM%Get parameters[Nbins, inc_node] = process_params(params);inc_node = inc_node*M/100;%For the decision regionN = region(5);mx = ones(N,1) * linspace (region(1),region(2),N); %linspace(?eê??μ￡????1?μ￡??a????êy)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 recursivelydisp(Building tree)tree = make_tree(binned_features, train_targets, inc_node, Nbins);%Make the decision region according to the treedisp(Building decision surface using the tree)targets = use_tree(binned_xy, 1:N^2, tree, Nbins, unique(train_targets));D = reshape(targets,N,N);%ENDfunction targets = use_tree(features, indices, tree, Nbins, Uc)%Classify recursively using a treetargets = zeros(1, size(features,2)); %size(features,2)·μ??featuresμ?áDêyif (size(features,1) == 1),%Only one dimension left, so work on itfor 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 n = 1 + floor(rand(1)*length(Uc));