Index data division – neural network with MATLAB

Hello,

There are different manners to divide whole data into Training Set, Validation Set and Test Set using dividind.

divideind

Divide targets into three sets using specified indices

Syntax
[trainInd,valInd,testInd] = divideind(Q,trainInd,valInd,testInd)

Description

[trainInd,valInd,testInd] = divideind(Q,trainInd,valInd,testInd) separates targets 
into three sets: training, validation, and testing, according to indices provided. 
It actually returns the same indices it receives as arguments; its purpose is 
to allow the indices to be used for training, validation, and testing for a 
network to be set manually.

Examples:

1 – Divide the data by index so that successive samples are assigned to the training set, validation set, and test set successively:

Format: start_index:step_size:end_index

trainInd = 1:3:201; 
valInd = 2:3:201; 
testInd = 3:3:201; 

[trainP,valP,testP] = divideind(p,trainInd,valInd,testInd); 
[trainT,valT,testT] = divideind(t,trainInd,valInd,testInd);

2 – Divide using net which, in this example, assumes our neural network:

p = [0 1 2 3 4 5 6 7 8; 0 1 2 3 4 5 6 7 8];
t = [0 0.84 0.91 0.14 -0.77 -0.96 -0.28 0.66 0.99];

net = newff(p,t,5)

net.divideFcn = 'divideind';
net.divideParam.trainInd = 1:2:9;
net.divideParam.valInd = 2:2:9;
net.divideParam.testInd = 2:2:9;

That’s all,

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