使用SA和ACO的固定特征选择

% % Copyright (c) 2015, Yarpiz (www.yarpiz.com) % All rights reserved. Please read the "license.txt" for license terms. % % Project Code: YPML122 % Project Title: Feature Selection using SA and ACO (Fixed Number of Features) % Publisher: Yarpiz (www.yarpiz.com) % % Developer: S. Mostapha Kalami Heris (Member of Yarpiz Team) % % Contact Info: sm.kalami@gmail.com, info@yarpiz.com % function results=CreateAndTrainANN(x,t) if ~isempty(x) % Choose a Training Function % For a list of all training functions type: help nntrain % 'trainlm' is usually fastest. % 'trainbr' takes longer but may be better for challenging problems. % 'trainscg' uses less memory. NFTOOL falls back to this in low memory situations. trainFcn = 'trainlm'; % Levenberg-Marquardt % Create a Fitting Network hiddenLayerSize = 10; net = fitnet(hiddenLayerSize,trainFcn); % Choose Input and Output Pre/Post-Processing Functions % For a list of all processing functions type: help nnprocess net.input.processFcns = {'removeconstantrows','mapminmax'}; net.output.processFcns = {'removeconstantrows','mapminmax'}; % Setup Division of Data for Training, Validation, Testing % For a list of all data division functions type: help nndivide net.divideFcn = 'dividerand'; % Divide data randomly net.divideMode = 'sample'; % Divide up every sample net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100; % Choose a Performance Function % For a list of all performance functions type: help nnperformance net.performFcn = 'mse'; % Mean squared error % Choose Plot Functions % For a list of all plot functions type: help nnplot net.plotFcns = {}; % net.plotFcns = {'plotperform','plottrainstate','ploterrhist', 'plotregression', 'plotfit'}; net.trainParam.showWindow=false; % Train the Network [net,tr] = train(net,x,t); % Test the Network y = net(x); e = gsubtract(t,y); E = perform(net,t,y); else y=inf(size(t)); e=inf(size(t)); E=inf; tr.trainInd=[]; tr.valInd=[]; tr.testInd=[]; end % All Data Data.x=x; Data.t=t; Data.y=y; Data.e=e; Data.E=E; % Train Data TrainData.x=x(:,tr.trainInd); TrainData.t=t(:,tr.trainInd); TrainData.y=y(:,tr.trainInd); TrainData.e=e(:,tr.trainInd); if ~isempty(x) TrainData.E=perform(net,TrainData.t,TrainData.y); else TrainData.E=inf; end % Validation and Test Data TestData.x=x(:,[tr.testInd tr.valInd]); TestData.t=t(:,[tr.testInd tr.valInd]); TestData.y=y(:,[tr.testInd tr.valInd]); TestData.e=e(:,[tr.testInd tr.valInd]); if ~isempty(x) TestData.E=perform(net,TestData.t,TestData.y); else TestData.E=inf; end % Export Results if ~isempty(x) results.net=net; else results.net=[]; end results.Data=Data; results.TrainData=TrainData; % results.ValidationData=ValidationData; results.TestData=TestData; end