Divide targets into three sets using random indices
[trainInd,valInd,testInd] = dividerand(Q,trainRatio,valRatio,testRatio)
[trainInd,valInd,testInd] = dividerand(Q,trainRatio,valRatio,testRatio) separates targets into three sets: training, validation, and testing. It takes the following inputs,
Number of targets to divide up.
Ratio of vectors for training. Default = 0.7.
Ratio of vectors for validation. Default = 0.15.
Ratio of vectors for testing. Default = 0.15.
Here are the network properties that define which data division function to use, what its parameters are, and what aspects of targets are divided up, when train is called.
net.divideFcn net.divideParam net.divideMode