rce.m

来自「最新的模式识别分类工具箱,希望对朋友们有用!」· M 代码 · 共 50 行

function D = RCE(train_features, train_targets, lambda_m, region)

% Classify using the reduced coulomb energy algorithm
% Inputs:
% 	features	- Train features
%	targets	- Train targets
%	lambda_m	- Maximum radius 
%	region	- Decision region vector: [-x x -y y number_of_points]
%
% Outputs
%	D			- Decision sufrace

epsilon 	= 1e-4;
[Dim,Nf] = size(train_features);
N			= region(5);
x        = linspace (region(1),region(2),N);
y        = linspace (region(3),region(4),N);

%Train the classifier
W		 = train_features;
lambda = zeros(1,Nf);

for i = 1:Nf,
   dist         = sqrt(sum((train_features - train_features(:,i) * ones(1,Nf)).^2));
   [m, indices] = sort(dist);
   x_hat        = find(train_targets(indices) ~= train_targets(i));
   lambda(i)    = min(dist(x_hat(1))-epsilon,lambda_m);
end

%Build the decision surface using the classifier
D = zeros(N);
for i = 1:N,
   for j = 1:N,
      dist	 = sqrt(sum((train_features - [x(i) y(j)]' * ones(1,Nf)).^2));
      indices 	 = find(dist < lambda);
      %The decision is a little different from DH&S, since there an ambiguous result can
      %Occure. Here we do not allow this.
      if isempty(indices),
         D(j,i) = rand(1) > .5;
      else
         D(j,i) = sum(train_targets(indices))/length(indices) > .5;
      end
   end
   
   if (i/50 == floor(i/50)),
      disp(['Finished ' num2str(i) ' lines out of ' num2str(N) ' lines.']);
   end
end