# ne, ~=

Determine inequality

## Description

returns a logical array
or a table of logical values with elements set to logical `A`

~= `B`

`1`

(`true`

) where inputs `A`

and
`B`

are not equal; otherwise, the element is logical
`0`

(`false`

). The test compares both real and
imaginary parts of numeric arrays. `ne`

returns logical
`1`

(`true`

) where `A`

or
`B`

have `NaN`

or undefined
`categorical`

elements.

## Examples

### Inequality of Two Vectors

Create two vectors containing both real and imaginary numbers, then compare the vectors for inequality.

A = [1+i 3 2 4+i]; B = [1 3+i 2 4+i]; A ~= B

`ans = `*1x4 logical array*
1 1 0 0

The `ne`

function tests both real and imaginary parts for inequality, and returns logical `1`

(`true`

) where one or both parts are not equal.

### Find Characters

Create a character vector.

`M = 'magenta';`

Test for the presence of a specific character using `~=`

.

`M ~= 'q'`

`ans = `*1x7 logical array*
1 1 1 1 1 1 1

The value of logical `1`

(`true`

) indicates the absence of the character `'n'`

. The character is not present in the vector.

### Find Values in Categorical Array

Create a categorical array with two values: `'heads'`

and `'tails'`

.

A = categorical({'heads' 'heads' 'tails'; 'tails' 'heads' 'tails'})

`A = `*2x3 categorical*
heads heads tails
tails heads tails

Find all values not in the `'heads'`

category.

`A ~= 'heads'`

`ans = `*2x3 logical array*
0 0 1
1 0 1

A value of logical `1`

(`true`

) indicates a value not in the category. Since `A`

only has two categories, `A ~= 'heads'`

returns the same answer as `A == 'tails'`

.

Compare the rows of `A`

for inequality.

A(1,:) ~= A(2,:)

`ans = `*1x3 logical array*
1 0 0

A value of logical `1`

(`true`

) indicates where the rows have unequal category values.

### Compare Floating-Point Numbers

Many numbers expressed in decimal text cannot be represented exactly as binary floating numbers. This leads to small differences in results that the `~=`

operator reflects.

Perform a few subtraction operations on numbers expressed in decimal and store the result in `C`

.

C = 0.5-0.4-0.1

C = -2.7756e-17

With exact decimal arithmetic, `C`

should be equal to *exactly* `0`

. Its small value is due to the nature of binary floating-point arithmetic.

Compare `C`

to `0`

for inequality.

C ~= 0

`ans = `*logical*
1

Compare floating-point numbers using a tolerance, `tol`

, instead of using `~=`

.

tol = eps(0.5); abs(C-0) > tol

`ans = `*logical*
0

The two numbers, `C`

and `0`

, are closer to one another than two consecutive floating-point numbers near `0.5`

. In many situations, `C`

may act like `0`

.

### Inequality of Two Datetime Arrays

Compare the elements of two `datetime`

arrays for inequality.

Create two `datetime`

arrays in different time zones.

t1 = [2014,04,14,9,0,0;2014,04,14,10,0,0]; A = datetime(t1,'TimeZone','America/Los_Angeles'); A.Format = 'd-MMM-y HH:mm:ss Z'

`A = `*2x1 datetime*
14-Apr-2014 09:00:00 -0700
14-Apr-2014 10:00:00 -0700

t2 = [2014,04,14,12,0,0;2014,04,14,12,30,0]; B = datetime(t2,'TimeZone','America/New_York'); B.Format = 'd-MMM-y HH:mm:ss Z'

`B = `*2x1 datetime*
14-Apr-2014 12:00:00 -0400
14-Apr-2014 12:30:00 -0400

Check where elements in `A`

and `B`

are not equal.

A~=B

`ans = `*2x1 logical array*
0
1

### Compare Tables

*Since R2023a*

Create two tables and compare them. The row names (if present in both) and variable names must be the same, but do not need to be in the same orders. Rows and variables of the output are in the same orders as the first input.

A = table([1;2],[3;4],VariableNames=["V1","V2"],RowNames=["R1","R2"])

`A=`*2×2 table*
V1 V2
__ __
R1 1 3
R2 2 4

B = table([4;2],[3;1],VariableNames=["V2","V1"],RowNames=["R2","R1"])

`B=`*2×2 table*
V2 V1
__ __
R2 4 3
R1 2 1

A ~= B

`ans=`*2×2 table*
V1 V2
_____ _____
R1 false true
R2 true false

## Input Arguments

`A`

, `B`

— Operands

scalars | vectors | matrices | multidimensional arrays | tables | timetables

Operands, specified as scalars, vectors, matrices, multidimensional
arrays, tables, or timetables. Inputs `A`

and
`B`

must either be the same size or have sizes that are
compatible (for example, `A`

is an
`M`

-by-`N`

matrix and
`B`

is a scalar or
`1`

-by-`N`

row vector). For more
information, see Compatible Array Sizes for Basic Operations.

You can compare numeric inputs of any type, and the comparison does not suffer loss of precision due to type conversion.

If one input is a

`categorical`

array, the other input can be a`categorical`

array, a cell array of character vectors, or a single character vector. A single character vector expands into a cell array of character vectors of the same size as the other input. If both inputs are ordinal`categorical`

arrays, they must have the same sets of categories, including their order. If both inputs are`categorical`

arrays that are not ordinal, they can have different sets of categories. See Compare Categorical Array Elements for more details.If one input is a

`datetime`

array, the other input can be a`datetime`

array, a character vector, or a cell array of character vectors.If one input is a

`duration`

array, the other input can be a`duration`

array or a numeric array. The operator treats each numeric value as a number of standard 24-hour days.If one input is a string array, the other input can be a string array, a character vector, or a cell array of character vectors. The corresponding elements of

`A`

and`B`

are compared lexicographically.

Inputs that are tables or timetables must meet the
following conditions:* (since R2023a)*

If an input is a table or timetable, then all its variables must have data types that support the operation.

If only one input is a table or timetable, then the other input must be a numeric or logical array.

If both inputs are tables or timetables, then:

Both inputs must have the same size, or one of them must be a one-row table.

Both inputs must have variables with the same names. However, the variables in each input can be in a different order.

If both inputs are tables and they both have row names, then their row names must be the same. However, the row names in each input can be in a different order.

If both inputs are timetables, then their row times must be the same. However, the row times in each input can be in a different order.

**Data Types: **`single`

| `double`

| `int8`

| `int16`

| `int32`

| `int64`

| `uint8`

| `uint16`

| `uint32`

| `uint64`

| `logical`

| `char`

| `string`

| `categorical`

| `datetime`

| `duration`

| `table`

| `timetable`

**Complex Number Support: **Yes

## 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™.

Usage notes and limitations:

Code generation does not support using

`ne`

to test inequality between an enumeration member and a string array, a character array, or a cell array of character arrays.

### GPU Code Generation

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

Usage notes and limitations:

Code generation does not support using

`ne`

to test inequality between an enumeration member and a string array, a character array, or a cell array of character arrays.

### HDL Code Generation

Generate Verilog and VHDL code for FPGA and ASIC designs using HDL 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™.

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).

## Version History

**Introduced before R2006a**

### R2023a: Perform operations directly on tables and timetables

The `ne`

operator supports operations directly on tables and
timetables without indexing to access their variables. All variables must have data types
that support the operation. For more information, see Direct Calculations on Tables and Timetables.

### R2020b: Implicit expansion change affects `categorical`

, `datetime`

, and `duration`

arrays

Starting in R2020b, `ne`

supports implicit expansion when the
arguments are `categorical`

, `datetime`

, or
`duration`

arrays. Between R2020a and R2016b, implicit expansion was
supported only for numeric and string data types.

### R2016b: Implicit expansion change affects arguments for operators

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 `try`

/`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.

## See Also

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