ne, ~=

Determine inequality

Syntax

A ~= B

ne(A,B)

Description

A ~= B returns a logical array or a table of logical values with elements set to logical 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.

ne(A,B) is an alternative way to execute A ~= B, but is rarely used. It enables operator overloading for classes.

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 = 1×4 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 = 1×7 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 = 2×3 categorical
     heads      heads      tails 
     tails      heads      tails 

Find all values not in the 'heads' category.

A ~= 'heads'

ans = 2×3 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 = 1×3 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 = 2×1 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 = 2×1 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 = 2×1 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 an ordinal categorical array, the other input can be an ordinal categorical array, or a string scalar or character vector that represents a categorical value. If both inputs are ordinal categorical arrays, they must have the same sets of categories, including their order. For more information, see Compare Categorical Array Elements.

• If one input is a datetime array, the other input can be a datetime array, or a string scalar or character vector that represents a date and time. For more information, see Compare Dates and Time.

• If one input is a duration array, the other input can be a duration array, a string scalar or character vector that represents a length of time, or a numeric array where each element represents a number of fixed-length 24-hour days. For more information, see Compare Dates and Time.

• 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. For more information, see Compare Text.

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.

The ne 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 VHDL, Verilog and SystemVerilog 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™.

The ne 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 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.