%% Script function:  Example of time series prediction using a lag matrix

% Build the sample data set:
t=linspace(0,4*pi,200);
y=sin(t);
X=lag(y,4,1);
[dim,numpts]=size(X);

PtoKeep=10;   %Approximate percent of the data to use for the model
PtsTrain=ceil((PtoKeep/100)*numpts);  %Number of points to use for training
PtsTest=numpts-PtsTrain;              %Number of points to use for testing
tempidx=randperm(numpts);             %Randomize the order of the data going in

Patterns=X(1:4,tempidx(1:PtsTrain));  % Domain vectors- Use these to train the network
Targets=X(5,tempidx(1:PtsTrain));     % Desired targets- Use for training.
TestPattern=X(1:4,tempidx(PtsTrain+1:end));  %Put these into the network after training
TestTargets=X(5,tempidx(PtsTrain+1:end));    %See if TestPattern modeled these values

%
%  Now call the LinearNet routine, and see how well the Linear Network does
%  using the TestPattern data as input (compare with TestTargets).
%