决策树算法code

function D = CART(train_features, train_targets, params, region) 
 
% Classify using classification and regression trees 
% Inputs: 
% features - Train features 
% targets    - Train targets 
% params - [Impurity type, Percentage of incorrectly assigned samples at a node] 
%                   Impurity can be: Entropy, Variance (or Gini), or Missclassification 
% region    - Decision region vector: [-x x -y y number_of_points] 
% 
% Outputs 
% D - Decision sufrace 
 
 
[Ni, M]   = size(train_features); 
 
%Get parameters 
[split_type, inc_node] = process_params(params); 
 
%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));;