Create a datastore from the sample file, airlinesmall.csv,
which contains tabular data.
Partition the datastore into three parts.
subds =
TabularTextDatastore with properties:
Files: {
' ...\matlab\toolbox\matlab\demos\airlinesmall.csv'
}
ReadVariableNames: true
VariableNames: {'Year', 'Month', 'DayofMonth' ... and 26 more}
Text Format Properties:
NumHeaderLines: 0
Delimiter: ','
RowDelimiter: '\r\n'
TreatAsMissing: ''
MissingValue: NaN
Advanced Text Format Properties:
TextscanFormats: {'%f', '%f', '%f' ... and 26 more}
ExponentCharacters: 'eEdD'
CommentStyle: ''
Whitespace: ' \b\t'
MultipleDelimitersAsOne: false
Properties that control the table returned by preview, read, readall:
SelectedVariableNames: {'Year', 'Month', 'DayofMonth' ... and 26 more}
SelectedFormats: {'%f', '%f', '%f' ... and 26 more}
ReadSize: 20000 rowsCreate a datastore from the sample file, mapredout.mat,
which is the output file of the mapreduce function.
Get the default number of partitions for ds.
Partition the datastore into the default number of partitions
and return the datastore corresponding to the first partition.
Read the data in subds.
Create a datastore that contains three image files.
ds =
ImageDatastore with properties:
Files: {
' ...\matlab\toolbox\matlab\demos\street1.jpg';
' ...\matlab\toolbox\matlab\imagesci\peppers.png';
' ...\matlab\toolbox\matlab\imagesci\corn.tif'
}
ReadFcn: @readDatastoreImagePartition the datastore by files and return the part corresponding
to the second file.
subds =
ImageDatastore with properties:
Files: {
' ...\matlab\toolbox\matlab\imagesci\peppers.png'
}
ReadFcn: @readDatastoreImagesubds contains one file.
Create a datastore from the sample file, mapredout.mat,
which is the output file of the mapreduce function.
Partition the datastore into three parts on three workers
in a parallel pool.