Ho_Kashyap.rar_ MHKS_Ho-Kashyap算法_Ho-kashyap_Ho_Kashyap_ho kashy

function [test_targets, w_percept, b] = Ho_Kashyap(train_patterns, train_targets, test_patterns, params) % Classify using the using the Ho-Kashyap algorithm % Inputs: % train_patterns - Train patterns % train_targets - Train targets % test_patterns - Test patterns % params - [Type(Basic/Modified), Maximum iteration, Convergence criterion, Convergence rate] % % Outputs % test_targets - Predicted targets % w_percept - Classifier weights % b - Margin [c, n] = size(train_patterns); [type, Max_iter, b_min, eta] = process_params(params); train_patterns = [train_patterns ; ones(1,n)]; train_zero = find(train_targets == 0); %Preprocessing (Needed so that b>0 for all patterns) processed_patterns = train_patterns; processed_patterns(:,train_zero) = -processed_patterns(:,train_zero); processed_targets = 2*train_targets-1; b = ones(1,n); Y = processed_patterns; a = pinv(Y')*b'; k = 0; e = 1e3; found = 0; while ((sum(abs(e) > b_min)>0) & (k < Max_iter) &(~found)) %k <- (k+1) mod n k = k+1; %e <- Ya - b e = (Y' * a)' - b; %e_plus <- 1/2(e+abs(e)) e_plus = 0.5*(e + abs(e)); %b <- b + 2*eta*e_plus b = b + 2*eta*e_plus; if strcmp(type,'Basic'), %a <- pinv(Y)*b; a = pinv(Y')*b'; else %a <- a + eta*pinv(Y)*|e_plus|; a = a + eta*pinv(Y')*abs(e_plus)'; end end if (k == Max_iter), disp(['No solution found']); else disp(['Did ' num2str(k) ' iterations']) end test_targets = a' * [test_patterns; ones(1, size(test_patterns,2))]; if (length(unique(train_targets)) == 2) test_targets = test_targets > 0; end