function [ classValue, errorRate ] = nnLearning( X, Y, hiddenLayerSize, category )

% nnLearning trains a nn classifier and outputs the predictions

%    Just using matlabs NN package. Make sure you have it (AILAB02 does

%    not).

%Gives outputs and predictions on everything. Assuming it is doing nFold

%validation

inputs = X';

targets = Y(:,category)'; %using wrong dimension? thats why getting 7s?

% Create a Fitting Network

net = fitnet(hiddenLayerSize);

% Setup Division of Data for Training, Validation, Testing

net.divideParam.trainRatio = 70/100;

net.divideParam.valRatio = 15/100;

net.divideParam.testRatio = 15/100;

% Train the Network

net.trainParam.showWindow = false; %Hide the GUI window

net.trainParam.showCommandLine = false; 

[net,tr] = train(net,inputs,targets);

% Test the Network

outputs = net(inputs);

classValue = outputs;

%install a max and min value

classValue = min(classValue, 5);

classValue = max(classValue, 1);

errors = gsubtract(targets,outputs);

performance = perform(net,targets,outputs); %MSE apparently

%RMSE Error

errorRate = RMSE(net(inputs)', targets');

close all

% View the Network

%view(net)

% Plots

% Uncomment these lines to enable various plots.

%figure, plotperform(tr)

%figure, plottrainstate(tr)

%figure, plotfit(net,inputs,targets)

%figure, plotregression(targets,outputs)

%figure, ploterrhist(errors)