bsxfun

Apply element-wise operation to two arrays with implicit expansion enabled

Syntax

C = bsxfun(fun,A,B)

Description

C = bsxfun(fun,A,B) applies the element-wise binary operation specified by the function handle fun to arrays A and B.

Examples

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Deviation of Matrix Elements from Column Mean

Open Live Script

Subtract the column mean from the corresponding column elements of a matrix A. Then normalize by the standard deviation.

A = [1 2 10; 3 4 20; 9 6 15];
C = bsxfun(@minus, A, mean(A));
D = bsxfun(@rdivide, C, std(A))

D = 3×3

   -0.8006   -1.0000   -1.0000
   -0.3203         0    1.0000
    1.1209    1.0000         0

In MATLAB® R2016b and later, you can directly use operators instead of bsxfun, since the operators independently support implicit expansion of arrays with compatible sizes.

(A - mean(A))./std(A)

ans = 3×3

   -0.8006   -1.0000   -1.0000
   -0.3203         0    1.0000
    1.1209    1.0000         0

Compare Vector Elements

Open Live Script

Compare the elements in a column vector and a row vector. The result is a matrix containing the comparison of each combination of elements from the vectors. An equivalent way to execute this operation is with A > B.

A = [8; 17; 20; 24]

B = [0 10 21]

B = 1×3

     0    10    21

C = bsxfun(@gt,A,B)

C = 4x3 logical array

   1   0   0
   1   1   0
   1   1   0
   1   1   1

Expansion with Custom Function

Open Live Script

Create a function handle that represents the function $f (a, b) = a - e^{b}$ .

fun = @(a,b) a - exp(b);

Use bsxfun to apply the function to vectors a and b. The bsxfun function expands the vectors into matrices of the same size, which is an efficient way to evaluate fun for many combinations of the inputs.

a = 1:7;
b = pi*[0 1/4 1/3 1/2 2/3 3/4 1].';
C = bsxfun(fun,a,b)

C = 7×7

         0    1.0000    2.0000    3.0000    4.0000    5.0000    6.0000
   -1.1933   -0.1933    0.8067    1.8067    2.8067    3.8067    4.8067
   -1.8497   -0.8497    0.1503    1.1503    2.1503    3.1503    4.1503
   -3.8105   -2.8105   -1.8105   -0.8105    0.1895    1.1895    2.1895
   -7.1205   -6.1205   -5.1205   -4.1205   -3.1205   -2.1205   -1.1205
   -9.5507   -8.5507   -7.5507   -6.5507   -5.5507   -4.5507   -3.5507
  -22.1407  -21.1407  -20.1407  -19.1407  -18.1407  -17.1407  -16.1407

Input Arguments

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`fun` — Binary function to apply
function handle

Binary function to apply, specified as a function handle. fun must be a binary (two-input) element-wise function of the form C = fun(A,B) that accepts arrays A and B with compatible sizes. For more information, see Compatible Array Sizes for Basic Operations. fun must support scalar expansion, such that if A or B is a scalar, then C is the result of applying the scalar to every element in the other input array.

In MATLAB^® R2016b and later, the built-in binary functions listed in this table independently support implicit expansion. With these functions, you can call the function or operator directly instead of using bsxfun. For example, you can replace C = bsxfun(@plus,A,B) with A+B.

Function	Symbol	Description
`plus`	`+`	Plus
`minus`	`-`	Minus
`times`	`.*`	Array multiply
`rdivide`	`./`	Right array divide
`ldivide`	`.\`	Left array divide
`power`	`.^`	Array power
`eq`	`==`	Equal
`ne`	`~=`	Not equal
`gt`	`>`	Greater than
`ge`	`>=`	Greater than or equal to
`lt`	`<`	Less than
`le`	`<=`	Less than or equal to
`and`	`&`	Element-wise logical AND
`or`	`\|`	Element-wise logical OR
`xor`	N/A	Logical exclusive OR
`max`	N/A	Binary maximum
`min`	N/A	Binary minimum
`mod`	N/A	Modulus after division
`rem`	N/A	Remainder after division
`atan2`	N/A	Four-quadrant inverse tangent; result in radians
`atan2d`	N/A	Four-quadrant inverse tangent; result in degrees
`hypot`	N/A	Square root of sum of squares

Example: C = bsxfun(@plus,[1 2],[2; 3])

Data Types: function_handle

`A,B` — Input arrays
scalars | vectors | matrices | multidimensional arrays

Input arrays, specified as scalars, vectors, matrices, or multidimensional arrays. Inputs A and B must have compatible sizes. For more information, see Compatible Array Sizes for Basic Operations. Whenever a dimension of A or B is singleton (equal to one), bsxfun virtually replicates the array along that dimension to match the other array. In the case where a dimension of A or B is singleton, and the corresponding dimension in the other array is zero, bsxfun virtually diminishes the singleton dimension to zero.

Tips

It is recommended that you replace most uses of bsxfun with direct calls to the functions and operators that support implicit expansion. Compared to using bsxfun, implicit expansion offers faster speed of execution, better memory usage, and improved readability of code. For more information, see Compatible Array Sizes for Basic Operations.

Extended Capabilities

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

Usage notes and limitations:

The specified function must not rely on persistent variables.

For more information, see Tall Arrays.

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

Usage notes and limitations:

Code generation does not support sparse matrix inputs for this function.

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

Usage notes and limitations:

Code generation does not support sparse matrix inputs for this function.

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

Usage notes and limitations:

See bsxfun (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).

Documentation

bsxfun

Syntax

Description

Examples

Deviation of Matrix Elements from Column Mean

Compare Vector Elements

Expansion with Custom Function

Input Arguments

`fun` — Binary function to apply
function handle

`A,B` — Input arrays
scalars | vectors | matrices | multidimensional arrays

Tips

Extended Capabilities

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

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

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

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

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

See Also

Topics

MATLAB Documentation

Support

Documentation

bsxfun

Syntax

Description

Examples

Deviation of Matrix Elements from Column Mean

Compare Vector Elements

Expansion with Custom Function

Input Arguments

fun — Binary function to apply function handle

A,B — Input arrays scalars | vectors | matrices | multidimensional arrays

Tips

Extended Capabilities

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

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

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

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

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

See Also

Topics

MATLAB Documentation

Support

`fun` — Binary function to apply
function handle

`A,B` — Input arrays
scalars | vectors | matrices | multidimensional arrays

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

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

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

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

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