MATLAB ANN artificial neural network train test

sample1: 1 2 3 4  label: A
sample2: 1 5 7 7  label: B

  Every sample must be put in a column
=============================
featureIn
1 1
2 5
3 7
4 7
featureOut
A
B

function [yPredict] = doBP(trainFeature,trainValue)

trainFeature = trainFeature'; % to fit matlab format
trainValue = trainValue';% to fit matlab format



% % version 2010a
 net=newff(trainFeature,trainValue,[13 1],{'tansig' 'purelin'}); % tansig purelin

% version 2009a
% net=newff(trainFeature,trainValue,[13 1]);
% net.layers{1}.transferFcn = 'tansig';
% net.layers{2}.transferFcn = 'purelin';



net=init(net);

net.trainParam.epochs = 99999999;
net.trainParam.goal = 0.0000001; %(stop training if the error goal hit)
net.trainParam.lr= 0.000001; % (learning rate, not default trainlm) [0.01]
net.trainParam.epochs = 99999999;
net.trainParam.goal = 0.0000001; %(stop training if the error goal hit)
net.trainParam.lr= 0.000001; % (learning rate, not default trainlm) [0.01]
% net.trainParam.lr_dec = 0.000001;
% net.trainParam.mc = 0.9;
% net.trainParam.min_grad = 1e-10;
net.trainParam.show=1 ; %(no. epochs between showing error) [25]
net.trainParam.time =100000; %    (Max time to train in sec) [inf]
net.trainFcn = 'trainlm'; % trainrp trainbfg  trainlm

net.divideParam.trainRatio = 80/100;  % Adjust as desired
net.divideParam.valRatio = 20/100;  % Adjust as desired
net.divideParam.testRatio = 0/100;  % Adjust as desired

% TRAIN

[net,tr,Ytrain,E,Pf,Af] = train(net,trainFeature,trainValue);  %train(net,subset_active_input',subset_active_output');
plotperf(tr);

save net; % will save the network (net) as net.mat.

end

function doTesting(testFeatureIn)

      testFeatureIn = testFeatureIn';  % to fit matlab format
      testFeatureOut = testFeatureOut';  % to fit matlab format
      load net ;% will retrive the network and put it in your workspace

      [predictedY,Pf,Af,E,perf] = sim(net,testFeatureIn);
end