--- a
+++ b/Semantic Features/nnLearning.m
@@ -0,0 +1,49 @@
+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)