gt,>

Determine greater than

Syntax

A > B

gt(A,B)

Description

A>Breturns a logical array with elements set to logical 1 (true) whereAis greater thanB; otherwise, the element is logical0(false). The test compares only the real part of numeric arrays.gtreturns logical0(false) whereAorBhave NaN or undefinedcategoricalelements.

gt(A,B)is an alternate way to executeA > B, but is rarely used. It enables operator overloading for classes.

Examples

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Test Vector Elements

Open Live Script

Determine if vector elements are greater than a given value.

Create a numeric vector.

A = [1 12 18 7 9 11 2 15];

Test the vector for elements that are greater than10.

A > 10

ans =1x8 logical array0 1 1 0 0 1 0 1

The result is a vector with values of logical1(true) where the elements ofAsatisfy the expression.

Use the vector of logical values as an index to view the values inAthat are greater than10.

A(A > 10)

ans =1×412 18 11 15

The result is a subset of the elements inA.

Replace Elements of Matrix

Open Live Script

Create a matrix.

A = magic(4)

A =4×416 2 3 13 5 11 10 8 9 7 6 12 4 14 15 1

Replace all values greater than9with the value10.

A(A > 9) = 10

A =4×410 2 3 10 5 10 10 8 9 7 6 10 4 10 10 1

The result is a new matrix whose largest element is10.

Compare Values in Categorical Array

Open Live Script

Create an ordinal categorical array.

A = categorical({'large''medium''small';'medium'...'small''large'},{'small''medium''large'},'Ordinal',1)

A =2x3 categoricallarge medium small medium small large

The array has three categories:'small','medium', and'large'.

Find all values greater than the category'medium'.

A >'medium'

ans =2x3 logical array1 0 0 0 0 1

A value of logical1(true) indicates a value greater than the category'medium'.

Compare the rows ofA.

A(1,:) > A(2,:)

ans =1x3 logical array1 1 0

The function returns logical1(true) where the first row has a category value greater than the second row.

Test Complex Numbers

Open Live Script

Create a vector of complex numbers.

A = [1+i 2-2i 1+3i 1-2i 5-i];

Find the values that are greater than2.

A(A > 2)

ans = 5.0000 - 1.0000i

gtcompares only the real part of the elements inA.

Useabsto find which elements are outside a radius of2from the origin.

A(abs(A) > 2)

ans =1×4 complex2.0000 - 2.0000i 1.0000 + 3.0000i 1.0000 - 2.0000i 5.0000 - 1.0000i

The result has more elements sinceabsaccounts for the imaginary part of the numbers.

Compare Dates

Open Live Script

Create a vector of dates.

A = datetime([2014,05,01;2014,05,31])

A =2x1 datetime01-May-2014 31-May-2014

Find the dates that occur after May 10, 2014.

A(A >'2014-05-10')

ans =datetime31-May-2014

Input Arguments

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

操作数,指定为标量、向量,矩阵,or multidimensional arrays. InputsAandBmust either be the same size or have sizes that are compatible (for example,Ais anM-by-Nmatrix andBis a scalar or1-by-Nrow vector). For more information, seeCompatible 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 ordinalcategoricalarray, the other input can be an ordinalcategoricalarray, 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 ordinalcategoricalarrays, they must have the same sets of categories, including their order. SeeCompare Categorical Array Elementsfor more details.
If one input is adatetimearray, the other input can be adatetimearray, a character vector, or a cell array of character vectors.
If one input is adurationarray, the other input can be adurationarray 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 ofAandBare compared lexicographically.

Compatibility Considerations

expand all

Implicit expansion change affects arguments for operators

Behavior changed in R2016b

与一个R2016b开始ddition 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 atry/catchblock, then your code might no longer catch those errors.

For more information on the required input sizes for basic array operations, seeCompatible Array Sizes for Basic Operations.

Implicit expansion change affects ordinal`categorical`arrays,`datetime`arrays, and`duration`arrays

Behavior changed in R2020b

Starting in R2020b,gtsupports implicit expansion when the arguments are ordinalcategoricalarrays,datetimearrays, ordurationarrays. Between R2020a and R2016b, implicit expansion was supported only for numeric and string data types.

Extended Capabilities

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

This function fully supports tall arrays. For more information, seeTall 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™.

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, seeRun 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, seeRun 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, seeRun MATLAB Functions with Distributed Arrays(Parallel Computing Toolbox).

gt,>

Syntax

Description

Examples

Test Vector Elements

Replace Elements of Matrix

Compare Values in Categorical Array

Test Complex Numbers

Compare Dates

Input Arguments

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

Compatibility Considerations

Implicit expansion change affects arguments for operators

Implicit expansion change affects ordinal`categorical`arrays,`datetime`arrays, and`duration`arrays

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™.

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`.

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

金宝app

Introducing Deep Learning with MATLAB

gt,>

Syntax

Description

Examples

Test Vector Elements

Replace Elements of Matrix

Compare Values in Categorical Array

Test Complex Numbers

Compare Dates

Input Arguments

A,B—Operandsscalars|vectors|matrices|multidimensional arrays

Compatibility Considerations

Implicit expansion change affects arguments for operators

Implicit expansion change affects ordinalcategoricalarrays,datetimearrays, anddurationarrays

Extended Capabilities

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

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

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

HDL Code GenerationGenerate Verilog and VHDL code for FPGA and ASIC designs using HDL Coder™.

Thread-Based EnvironmentRun code in the background using MATLAB®backgroundPoolor accelerate code with Parallel Computing Toolbox™ThreadPool.

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

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

See Also

Topics

MATLAB Documentation

金宝app

Introducing Deep Learning with MATLAB

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

Implicit expansion change affects ordinal`categorical`arrays,`datetime`arrays, and`duration`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™.

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`.

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™.