Find logical exclusive-OR
C = xor( performs a logical
exclusive-OR of arrays
B and returns an
array containing elements set to either logical 1 (
logical 0 (
false). An element of the output array is set to
logical 1 (
but not both, contains a nonzero element at that same array location. Otherwise, the
array element is set to 0.
For bit-wise logical XOR operations, see
Exclusive-OR Truth Table
Create a truth table for
A = [true false]
A = 1x2 logical array 1 0
B = [true; false]
B = 2x1 logical array 1 0
C = xor(A,B)
C = 2x2 logical array 0 1 1 0
B — Operands
scalars | vectors | matrices | multidimensional arrays
Operands, specified as scalars, vectors, matrices, or multidimensional
B must either be
the same size or have sizes that are compatible (for example,
A is an
B is a scalar or
N row vector). For more
information, see Compatible Array Sizes for Basic Operations.
Implicit expansion change affects arguments for operators
Behavior changed in R2016b
Starting in R2016b with the addition of implicit expansion, some combinations of arguments for basic operations that previously returned errors now produce results. For example, you previously could not add a row and a column vector, but those operands are now valid for addition. In other words, an expression like
[1 2] + [1; 2] previously returned a size mismatch error, but now it executes.
If your code uses element-wise operators and relies on the errors that MATLAB® previously returned for mismatched sizes, particularly within a
catch block, then your code might no longer catch those errors.
For more information on the required input sizes for basic array operations, see Compatible Array Sizes for Basic Operations.
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 Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.
HDL Code Generation
Generate Verilog and VHDL code for FPGA and ASIC designs using HDL Coder™.
Run code in the background using MATLAB®
backgroundPool or accelerate code with Parallel Computing Toolbox™
This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.
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).
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).