isUnderlyingType

Determine whether input has specified underlying data type

Since R2020b

Syntax

tf = isUnderlyingType(X,typename)

Description

tf = isUnderlyingType(X,typename) returns logical 1 (true) if the underlying data type of X is equal to typename, as returned by underlyingType(X). Otherwise, the result is logical 0 (false).

Examples

Test Underlying Data Type

Use isUnderlyingType to query the data type of several variables.

Create a numeric variable that has a data type of single and then test whether the underlying data type is also single.

x = single(5);
tf = isUnderlyingType(x,"single")

tf =

  logical

   1

The result is true because underlyingType(x) returns 'single'.

Next, create a table with two variables and then determine whether the underlying data type is string.

x = table([1; 2; 3],["a"; "b"; "c"]);
tf = isUnderlyingType(x,"string")

tf =

  logical

   0

In this case, the result is false because underlyingType(x) returns 'table'.

Now, create a distributed array (requires Parallel Computing Toolbox™) and then determine whether the underlying data type is double.

x = distributed(1:10);
tf = isUnderlyingType(x,"double")

Starting parallel pool (parpool) using the 'local' profile ...
Connected to the parallel pool (number of workers: 6).

tf =

  logical

   1

The result is true because underlyingType(x) returns 'double'. In this case, class(x) returns the name of the class for the variable, while underlyingType(x) returns the name of the data type for the underlying data.

Input Arguments

X — Input array or object

variable | expression

Input array or object, specified as a variable or expression.

Example: isUnderlyingType(gpuArray(double(1)),"single") tests whether the input gpuArray contains underlying data of type single.

typename — Name of data type to test

character vector | string scalar

Name of data type to test, specified as a character vector or string scalar.

Example: isUnderlyingType(X,"double") tests whether X has underlying type double.

Data Types: char | string

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

• Does not support input argument X that is of dlarray (Deep Learning Toolbox) type.

Thread-Based Environment

Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

The isUnderlyingType function fully supports GPU arrays. To run the function on a GPU, specify the input data as a gpuArray (Parallel Computing Toolbox). For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Distributed Arrays

Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

This function fully supports distributed arrays. For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).

Version History

Introduced in R2020b