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Array of random integers


R = randi(valrange,sz,arraytype)
R = randi(valrange,sz,datatype,arraytype)

R = randi(valrange,sz,'like',P)
R = randi(valrange,sz,datatype,'like',P)

C = randi(valrange,sz,codist)
C = randi(valrange,sz,datatype,codist)
C = randi(valrange,sz,___,codist,'noCommunication')
C = randi(valrange,sz,___,codist,'like',P)


R = randi(valrange,sz,arraytype) creates a matrix with underlying class of double, with randi integer values in all elements.

R = randi(valrange,sz,datatype,arraytype) creates a matrix with underlying class of datatype, with randi values in all elements.

The size and type of array are specified by the argument options according to the following table.

valrangemax or [min max]Specifies integer value range from 1 to max, or from min to max..
sznSpecifies size as an n-by-n matrix.
m,n or [m n]Specifies size as an m-by-n matrix.
m,n,...,k or [m n ... k]Specifies size as an m-by-n-by-...-by-k array.
arraytype'distributed'Specifies distributed array.
'codistributed'Specifies codistributed array, using the default distribution scheme.
'gpuArray'Specifies gpuArray.
datatype'double' (default), 'single', 'int8', 'uint8', 'int16', 'uint16', 'int32', 'uint32', 'int64', or 'uint64'Specifies underlying class of the array, i.e., the data type of its elements.

R = randi(valrange,sz,'like',P) creates an array of randi values with the same type and underlying class (data type) as array P.

R = randi(valrange,sz,datatype,'like',P) creates an array of randi values with the specified underlying class (datatype), and the same type as array P.

C = randi(valrange,sz,codist) or C = randi(valrange,sz,datatype,codist) creates a codistributed array of randi values with the specified size and underlying class (the default datatype is 'double'). The codistributor object codist specifies the distribution scheme for creating the codistributed array. For information on constructing codistributor objects, see the reference pages for codistributor1d and codistributor2dbc. To use the default distribution scheme, you can specify a codistributor constructor without arguments. For example:

    C = randi(8,codistributor1d());

C = randi(valrange,sz,___,codist,'noCommunication') specifies that no interworker communication is to be performed when constructing a codistributed array, skipping some error checking steps.

C = randi(valrange,sz,___,codist,'like',P) creates a codistributed array of randi values with the specified range, size, underlying class, and distribution scheme. If either the class or codistributor argument is omitted, the characteristic is acquired from the codistributed array P.


Create Distributed Randi Matrix

Create a 1000-by-1000 distributed array of randi values from 1 to 100, with underlying class double:

D = randi(100,1000,'distributed');

Create Codistributed Randi Matrix

Create a 1000-by-1000 codistributed double matrix of randi values from 0 to 12, distributed by its second dimension (columns).

    C = randi([0 12],1000,'codistributed');

With four workers, each worker contains a 1000-by-250 local piece of C.

Create a 1000-by-1000 codistributed single matrix of randi values from 1 to 4, distributed by its columns.

    codist = codistributor('1d',2,100*[1:numlabs]);
    C = randi(4,1000,1000,'single',codist);

Each worker contains a 100-by-labindex local piece of C.

Create gpuArray Randi Matrix

Create a 1000-by-1000 gpuArray of randi values from —50 to 50, with underlying class double:

G = randi([-50 50],1000,'double','gpuArray');

See Also

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