--- a
+++ b/Ensemble Learning/AdaBoost/predAdaBoost.m
@@ -0,0 +1,26 @@
+function [Label, Err] = predAdaBoost(abClassifier, X, Y)
+N = size(X, 1);
+
+if nargin < 3
+    Y = [];
+end
+
+M = abClassifier.nWC;
+LabM = zeros(N, M);
+for i = 1:M
+    LabM(:,i) = abClassifier.Weight(i)*predStump(X, abClassifier.WeakClas{i});
+end
+
+% 
+Label = zeros(N, 1);
+LabM = sum(LabM, 2);
+idx = logical(LabM > 0);
+Label(idx) = 1;
+Label(~idx) = -1;
+
+% 
+if ~isempty(Y)
+    Err = logical(Label ~= Y);
+    Err = sum(Err)/N;
+end
+end