shuffle

Shuffle all data in datastore

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Syntax

dsrand = shuffle(ds)

Description

example

dsrand= shuffle(ds)returns a datastore object containing a random ordering of the data fromds。

Examples

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Shuffle Files

Open Script

Create anImageDatastoreobjectimds。洗牌创建的文件a new datastore containing the same files in random order.

imds = imageDatastore(fullfile(matlabroot,'toolbox','matlab', {'demos','imagesci'}),'LabelSource','foldernames','FileExtensions', {'.jpg','.png','.tif'})

imds = ImageDatastore with properties: Files: { ' ...\matlab\toolbox\matlab\demos\cloudCombined.jpg'; ' ...\matlab\toolbox\matlab\demos\example.tif'; ' ...\matlab\toolbox\matlab\demos\landOcean.jpg' ... and 5 more } Labels: [demos; demos; demos ... and 5 more categorical] ReadFcn: @readDatastoreImage

imdsrand = shuffle(imds)

imdsrand = ImageDatastore with properties: Files: { ' ...\matlab\toolbox\matlab\demos\street2.jpg'; ' ...\matlab\toolbox\matlab\demos\landOcean.jpg'; ' ...\matlab\toolbox\matlab\imagesci\corn.tif' ... and 5 more } Labels: [demos; demos; imagesci ... and 5 more categorical] ReadFcn: @readDatastoreImage

Input Arguments

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`ds`—Input datastore
object

Input datastore, specified as a datastore object. The input datastore can be any datastore for whichisShuffleablereturnstrue(such asimageDatastore). The input datastore also can be aCombinedDatastoreorTransformedDatastorefor which all underlying datastores are shuffleable.

Output Arguments

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`dsrand`— Output datastore
object

Output datastore, returned as a datastore object containing randomly ordered data fromds。

Extended Capabilities

Thread-Based Environment
Run code in the background using MATLAB®`backgroundPool`or accelerate code with Parallel Computing Toolbox™`ThreadPool`。

Usage notes and limitations:

In a thread-based environment, you can useshuffleonly with the following datastores:
- ImageDatastoreobjects
- CombinedDatastoreorTransformedDatastoreobjects you create fromImageDatastoreobjects by usingcombineortransform
You can useshufflewith other datastores if you have Parallel Computing Toolbox™. To do so, run the function using a process-backed parallel pool instead of usingbackgroundPoolorThreadPool(use eitherProcessPoolorClusterPool).