How Can I Creating a feature vector & target vector to feed into neural network

This is my Source Code:

% Solve a Pattern Recognition Problem with a Neural Network

% Script generated by Neural Pattern Recognition app

% Created Mon Mar 30 15:53:06 IST 2015

%

% This script assumes these variables are defined:

%

% Input - input data.

% Target - target data.

clc;

close all;

clear all;

x = Input;

t = Target;

% Create a Pattern Recognition Network

hiddenLayerSize = 10;

net = patternnet(hiddenLayerSize);

% 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;

% For help on training function 'trainscg' type: help trainscg

% For a list of all training functions type: help nntrain

net.trainFcn = 'trainscg'; % Scaled conjugate gradient

% Choose a Performance Function

% For a list of all performance functions type: help nnperformance

net.performFcn = 'crossentropy'; % Cross-entropy

% Choose Plot Functions

% For a list of all plot functions type: help nnplot

net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...

'plotregression', 'plotfit'};

% Train the Network

[net,tr] = train(net,x,t);

% Test the Network

y = net(x);

e = gsubtract(t,y);

tind = vec2ind(t);

yind = vec2ind(y);

percentErrors = sum(tind ~= yind)/numel(tind);

performance = perform(net,t,y)

% Recalculate Training, Validation and Test Performance

trainTargets = t .* tr.trainMask{1};

valTargets = t .* tr.valMask{1};

testTargets = t .* tr.testMask{1};

trainPerformance = perform(net,trainTargets,y)

valPerformance = perform(net,valTargets,y)

testPerformance = perform(net,testTargets,y)

% View the Network

view(net)

% Plots

% Uncomment these lines to enable various plots.

%figure, plotperform(tr)

%figure, plottrainstate(tr)

%figure, plotconfusion(t,y)

%figure, plotroc(t,y)

%figure, ploterrhist(e)

% Deployment

% Change the (false) values to (true) to enable the following code blocks.

if (false)

% Generate MATLAB function for neural network for application deployment

% in MATLAB scripts or with MATLAB Compiler and Builder tools, or simply

% to examine the calculations your trained neural network performs.

genFunction(net,'myNeuralNetworkFunction');

y = myNeuralNetworkFunction(x);

end

if (false)

% Generate a matrix-only MATLAB function for neural network code

% generation with MATLAB Coder tools.

genFunction(net,'myNeuralNetworkFunction','MatrixOnly','yes');

y = myNeuralNetworkFunction(x);

end

if (false)

% Generate a Simulink diagram for simulation or deployment with.

% Simulink Coder tools.

gensim(net);

end