underlyingType

Type of underlying data determining array behavior

Syntax

typename = underlyingType(X)

Description

typename = underlyingType(X) returns the name of the underlying MATLAB^® data type that determines how the array X behaves. typename is returned as a character vector.

The class function is useful to determine the class of a variable. For most classes, underlyingType(X) and class(X) return the same answer. However, some classes in MATLAB can contain underlying data that has a different type compared to what class returns. Example classes include gpuArray (Parallel Computing Toolbox), dlarray (Deep Learning Toolbox), and distributed (Parallel Computing Toolbox). The behavior of these classes is determined by the type of the underlying data they contain. For example, a distributed array containing double values behaves like a double array.

Examples

Query Underlying Data Type

Use underlyingType and class to test the data types of several variables.

Create a numeric variable that has a data type of single and then query the class and underlying data type.

x = single(5);
class(x)

ans =

    'single'

underlyingType(x)

ans =

    'single'

When x is a fundamental data type such as single, double, or string, both class(x) and underlyingType(x) return the same answer.

Next, create a table with two variables and then query the class and underlying data type.

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

ans =

    'table'

underlyingType(x)

ans =

    'table'

When x is a container data type such as table, timetable, or cell, the commands class(x) and underlyingType(x) still return the same answer. This is because container data types do not change their behavior based on the underlying data type.

Now, create a gpuArray (requires Parallel Computing Toolbox™) and then query the class and underlying data type.

x = gpuArray(1:10);
class(x)

ans =

    'gpuArray'

underlyingType(x)

ans =

    'double'

When x belongs to a class that can have underlying data that changes how the variable behaves, such as gpuArray, dlarray, or distributed, the commands class(x) and underlyingType(x) return different answers. 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: underlyingType(gpuArray(double(1))) returns 'double' since the gpuArray contains underlying data of type double.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

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 underlyingType 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