double

Double-precision arrays

Description

By default, MATLAB^® stores all numeric variables as double-precision floating-point values that are 8 bytes (64 bits). These variables have data type (class) double. For example:

x = 10;
whos x

  Name      Size            Bytes  Class     Attributes

  x         1x1                 8  double  

For more information on floating-point values, see Floating-Point Numbers.

Creation

You can create a double-precision array using the [] operator, such as A = [1 2 3; 4 5 6]. In addition, many functions return double-precision arrays, such as sin.

If you have an array of a different type, such as single or int8, then you can convert that array to double precision using the double function.

Syntax

Y = double(X)

Description

Y = double(X) converts the values in X to double precision.

Input Arguments

X — Input array

scalar | vector | matrix | multidimensional array

Input array, specified as a scalar, vector, matrix, or multidimensional array.

Data Types: single | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical | char | string

Examples

Create Double-Precision Variable

By default, numbers in MATLAB are of the data type double. You can use the class function to verify a variable's type.

x = 100;
xtype = class(x)

xtype = 
'double'

Use the double function to convert variables that are not double precision to type double.

y = true

y = logical
   1

ydouble = double(y);
ynewtype = class(ydouble)

ynewtype = 
'double'

Tips

• When you are creating a class, overload double when it makes sense to convert an object of that class to a double-precision value.

Extended Capabilities

Tall Arrays

Calculate with arrays that have more rows than fit in memory.

This function fully supports tall arrays. For more information, see Tall Arrays.

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

GPU Arrays

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

This function fully supports GPU arrays. 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).