function classifier(trainMatrixEnhanced)
%UNTITLED Summary of this function goes here
%   Detailed explanation goes here

% trainfunc,activfunc,learnfunc
trainfunc = "traingd";
activfunc = "hardlim"; % %logsig hardlim purelin
%learnfunc = "learnp";

W = rand(10,256); %Weights
b = rand(10,1); %bias

% targets = zeros(10, 500);
% for i=1:10
%     for j=1:50
%         targets(i,i) = 1;
%     end
% end

target_arrays= [[1 0 0 0 0 0 0 0 0 0];[0 1 0 0 0 0 0 0 0 0];[0 0 1 0 0 0 0 0 0 0];[0 0 0 1 0 0 0 0 0 0];[0 0 0 0 1 0 0 0 0 0];[0 0 0 0 0 1 0 0 0 0];[0 0 0 0 0 0 1 0 0 0];[0 0 0 0 0 0 0 1 0 0];[0 0 0 0 0 0 0 0 1 0];[0 0 0 0 0 0 0 0 0 1]];
%Função fornecida por um colega
targets = [];
A = targets;
for i=1:10
    for j=1:50
        targets= [A target_arrays(1:10,i)];
        A= targets;
    end
end

if(activfunc == "hardlim")
    net = perceptron; %Hard limit transfer function (default = 'hardlim') / Perceptron learning rule (default = 'learnp')
else
    net = network();

    net.numInputs = 1;
    net.inputs{1}.size = 256;
    net.numLayers = 1;
    net.layers{1}.size = 10;
    net.inputConnect(1) = 1;
    net.biasConnect(1) = 1;
    net.outputConnect(1) = 1;
    net.layers{1}.transferFcn = "purelin";
    net.trainFcn = "traingd"; %gradient / to see all training funcs - help nntrain

end

view(net)

net = configure(net, trainMatrixEnhanced, targets);

%Initialization
net.IW{1,1} = W;
net.b{1,1}= b;

%Training parameters
net.performParam.lr = 0.5; % learning rate
net.trainParam.epochs = 1000; % maximum epochs
net.trainParam.show = 35; % show
net.trainParam.goal = 1e-6; % goal=objective
net.performFcn = 'sse'; % criterion

%Training -> Trains a network net according to net.trainFcn and net.trainParam.
net = train(net, trainMatrixEnhanced, targets);

%Access weighs and bias
W = net.IW{1,1};
b = net.b{1,1};

load("validateData.mat")
% %Validation
a = sim(net, P, targets)

end