dividerand

(To be removed) Divide targets into three sets using random indices

dividerand will be removed in a future release. For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.

For advice on updating your code, see Version History.

Syntax

[trainInd,valInd,testInd] = dividerand(Q,trainRatio,valRatio,testRatio)

Description

[trainInd,valInd,testInd] = dividerand(Q,trainRatio,valRatio,testRatio) takes the number of targets to divide up, the ratio of vectors for training, the ratio of vectors for validation, and the ratio of vectors for testing, and returns the training indices, the validation indices, and the test indices.

Examples

Obtain Training, Validation, and Test Indices Using 'dividerand' Function

This example shows how to obtain the training, validation, and test indices using the dividerand function.

Divide 3000 samples into 60% for training, 20% for validation, and 20% for testing.

[trainInd,valInd,testInd] = dividerand(3000,0.6,0.2,0.2)

Input Arguments

Q — Targets number

scalar

Number of targets to divide up, specified as a scalar.

trainRatio — Training ratio

0.7 (default) | scalar

Ratio of vectors for training, specified as a scalar.

valRatio — Validation ratio

0.15 (default) | scalar

Ratio of vectors for validation, specified as a scalar.

testRatio — Testing ratio

0.15 (default) | scalar

Ratio of vectors for testing, specified as a scalar.

Output Arguments

trainInd — Training indices

vector

Training indices, returned as a row vector.

valInd — Validation indices

vector

Validation indices, returned as a row vector.

testInd — Testing indices

vector

Testing indices, returned as a row vector.

More About

Network Use

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

Version History

Introduced in R2008a

R2026a: dividerand will be removed

dividerand will be removed in a future release.

To learn more about data processing for neural networks, see these examples and topics:

• Preprocess Data for Domain-Specific Deep Learning Applications

• Datastores for Deep Learning

• Choose an App to Label Ground Truth Data

For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.