close all, clear all, clc, tic
% help narnet
T = simplenar_dataset;
sizeT = size(T) % [ 1 100 ]
t = cell2mat(T);
[ O N ] = size(t) % [ 1 100 ]
minmaxt = minmax(t) % [ 0.16218 0.99991]
MSE00 = var(t',1) % 0.063306
MSE00a = var(t',0) % 0.063945
plot(t)
FD = 1:2, H = 10 % nonoptimal default
neto = narnet( FD , H ) % No semicolon
% divideFcn: 'dividerand'
% divideParam: .trainRatio, .valRatio, .testRatio
% divideMode: 'time'
neto.divideParam.valRatio = 0;
% The new defaults will be
newtestratio = 0.15 + ( 0.15/(0.7+0.15))*0.15 % 0.17647
newtrainratio = 0.70 + ( 0.70/(0.7+0.15))*0.15 % 0.82353
No = N - max(FD) % 98
Ntsto = round(newtestratio*No) % 17
Ntrno = No - Ntsto % 81
[ Xo, Xoi, Aoi, To ] = preparets( neto, {}, {}, T );
[ Oo No ] = size(To) % [ 1 98 ]
[ neto tro Yo Eo Xof Aof ] = train( neto, Xo, To, Xoi, Aoi );
tro = tro % No semicolon
% trainInd: [1x81 double]
% valInd: [1x0 double]
% testInd: [1x17 double]
% num_epochs: 313
% vperf: [1x314 double] NaN(1,314)
% val_fail: [1x314 double] zeros(1,314)
% best_perf: 4.3277e-10
% best_vperf: NaN
% best_tperf: 2.0093e-09
