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In this tutorial, you use the MATLAB^{®}
Coder™
`codegen`

command to generate a static C
library for a MATLAB function. You first generate C code that can accept only inputs that have
fixed preassigned size. You then generate C code that can accept inputs of many
different sizes.

You can also generate code by using the MATLAB Coder app. For a tutorial on this workflow, see Generate C Code by Using the MATLAB Coder App.

Copy the tutorial files from the folder

to a local working folder. Here,
* matlabroot*\help\toolbox\coder\examples\euclidean

`matlabroot`

is the MATLAB installation folder, for example, ```
C:\Program
Files\MATLAB\R2019a
```

. To copy these files to your current folder, run
this MATLAB
command:copyfile(fullfile(matlabroot,'help','toolbox','coder','examples','euclidean'))

`euclidean_data.mat`

, `euclidean.m`

,
`test.m`

, `build_lib_fixed.m`

, and
`build_lib_variable.m`

files.
The MATLAB data file

`euclidean_data.mat`

contains two pieces of data: a single point in three-dimensional Euclidean space and a set of several other points in three-dimensional Euclidean space. More specifically:`x`

is a`3`

-by-`1`

column vector that represents a point in three-dimensional Euclidean space.`cb`

is a`3`

-by-`216`

array. Each column in`cb`

represents a point in three-dimensional Euclidean space.

The MATLAB file

`euclidean.m`

contains the function`euclidean`

that implements the*core algorithm*in this example. The function takes`x`

and`cb`

as inputs. It calculates the Euclidean distance between`x`

and each point in`cb`

and returns these quantities:The column vector

`y_min`

, which is equal to the column in`cb`

that represents the point that is closest to`x`

.The column vector

`y_max`

, which is equal to the column in`cb`

that represents the point that is farthest from`x`

.The 2-dimensional vector

`idx`

that contains the column indices of the vectors`y_min`

and`y_max`

in`cb`

.The 2-dimensional vector

`distance`

that contains the calculated smallest and largest distances to`x`

.

function [y_min,y_max,idx,distance] = euclidean(x,cb) % Initialize minimum distance as distance to first element of cb % Initialize maximum distance as distance to first element of cb idx(1)=1; idx(2)=1; distance(1)=norm(x-cb(:,1)); distance(2)=norm(x-cb(:,1)); % Find the vector in cb with minimum distance to x % Find the vector in cb with maximum distance to x for index=2:size(cb,2) d=norm(x-cb(:,index)); if d < distance(1) distance(1)=d; idx(1)=index; end if d > distance(2) distance(2)=d; idx(2)=index; end end % Output the minimum and maximum distance vectors y_min=cb(:,idx(1)); y_max=cb(:,idx(2)); end

The MATLAB script

`test.m`

loads the data file`euclidean_data.mat`

into the workspace. It then calls the function`euclidean`

to calculate`y_min`

,`y_max`

,`idx`

, and`distance`

. The script then displays the calculated quantities at the command line.Loading

`euclidean_data.mat`

is the preprocessing step that is executed before calling the core algorithm. Displaying the results is the post-processing step.% Load test data load euclidean_data.mat % Determine closest and farthest points and corresponding distances [y_min,y_max,idx,distance] = euclidean(x,cb); % Display output for the closest point disp('Coordinates of the closest point are: '); disp(num2str(y_min')); disp(['Index of the closest point is ', num2str(idx(1))]); disp(['Distance to the closest point is ', num2str(distance(1))]); disp(newline); % Display output for the farthest point disp('Coordinates of the farthest point are: '); disp(num2str(y_max')); disp(['Index of the farthest point is ', num2str(idx(2))]); disp(['Distance to the farthest point is ', num2str(distance(2))]);

The build scripts

`build_lib_fixed.m`

and`build_lib_variable.m`

contain commands for generating static C libraries from your MATLAB code that accept fixed-size and variable-size inputs, respectively. The contents of these scripts are shown later in the tutorial, when you generate the C code.

**Tip**

You can generate code from MATLAB functions by using MATLAB Coder. Code generation from MATLAB scripts is not supported.

Use test scripts to separate the pre- and post-processing steps from the function implementing the core algorithm. This practice enables you to easily reuse your algorithm. You generate code for the MATLAB function that implements the core algorithm. You do not generate code for the test script.

Run the test script `test.m`

in MATLAB. The output displays `y`

, `idx`

,
and `distance`

.

Coordinates of the closest point are: 0.8 0.8 0.4 Index of the closest point is 171 Distance to the closest point is 0.080374 Coordinates of the farthest point are: 0 0 1 Index of the farthest point is 6 Distance to the farthest point is 1.2923

To make your MATLAB code suitable for code generation, you use the Code Analyzer and the Code Generation Readiness Tool. The Code Analyzer in the MATLAB Editor continuously checks your code as you enter it. It reports issues and recommends modifications to maximize performance and maintainability. The Code Generation Readiness Tool screens the MATLAB code for features and functions that are not supported for code generation.

Certain MATLAB built-in functions and toolbox functions, classes, and System
objects that are supported for C/C++ code generation have specific code
generation limitations. These limitations and related usage notes are listed in
the **Extended Capabilities** sections of their corresponding
reference pages. For more information, see Functions and Objects Supported for C/C++ Code Generation.

Open

`euclidean.m`

in the MATLAB Editor. The Code Analyzer message indicator in the top right corner of the MATLAB Editor is green. The analyzer did not detect errors, warnings, or opportunities for improvement in the code.After the function declaration, add the

`%#codegen`

directive:Thefunction [y,idx,distance] = euclidean(x,cb) %#codegen

`%#codegen`

directive prompts the Code Analyzer to identify warnings and errors specific to code generation.The Code Analyzer message indicator becomes red, indicating that it has detected code generation issues.

To view the warning messages, move your cursor to the underlined code fragments. The warnings indicate that code generation requires the variables

`idx`

and`distance`

to be fully defined before subscripting them. These warnings appear because the code generator must determine the sizes of these variables at their first appearance in the code. To fix this issue, use the`ones`

function to simultaneously allocate and initialize these arrays.% Initialize minimum distance as distance to first element of cb % Initialize maximum distance as distance to first element of cb idx = ones(1,2); distance = ones(1,2)*norm(x-cb(:,1));

The Code Analyzer message indicator becomes green again, indicating that it does not detect any more code generation issues.

For more information about using the Code Analyzer, see Check Code for Errors and Warnings.

Save the file.

To run the Code Generation Readiness Tool, call the

`coder.screener`

function from the MATLAB command line.`coder.screener('euclidean')`

The tool does not detect any code generation issues for

`euclidean`

. For more information, see Code Generation Readiness Tool.The Code Generation Readiness Tool is not supported in MATLAB Online™.

**Note**The Code Analyzer and the Code Generation Readiness Tool might not detect all code generation issues. After eliminating the errors or warnings that these tools detect, generate code by using MATLAB Coder to determine if your MATLAB code has other compliance issues.

You are now ready to compile your code by using the MATLAB
Coder app. Here, *compilation* refers to the
generation of C/C++ code from your MATLAB code.

**Note**

Compilation of MATLAB code refers to the generation of C/C++ code from the MATLAB code. In other contexts, the term compilation could refer to the action of a C/C++ compiler.

Because C uses static typing, the code generator must determine the class,
size, and complexity of all variables in the
MATLAB files at code generation time, also known as *compile
time*. Therefore, when you generate code for the files, you must
specify the properties of all input arguments
to the entry-point functions. An *entry-point function* is
a top-level MATLAB function from which you generate code.

When you generate code by using the `codegen`

command, use the
`-args`

option to specify sample input parameters to the
entry-point functions. The code generator uses this information to determine the
properties of the input arguments.

In the next step, you use the `codegen`

command to generate
a MEX file from your entry-point function `euclidean`

.

You generate a MEX function from your entry-point function. A MEX function is generated code that can be called from inside MATLAB. You run the MEX function and check whether the generated MEX function and the original MATLAB function have the same functionality.

It is a best practice to perform this step because you can detect and fix run-time errors that are harder to diagnose in the generated C code. By default, the MEX function includes memory integrity checks. These checks perform array bounds and dimension checking. The checks detect violations of memory integrity in code generated for MATLAB functions. For more information, see Control Run-Time Checks.

To convert MATLAB code to efficient C/C++ source code, the code generator introduces optimizations that, in certain situations, cause the generated code to behave differently than the original source code. See Differences Between Generated Code and MATLAB Code.

Generate a MEX file for

`euclidean.m`

by using the`codegen`

command. To verify the MEX function, run the test script`test`

with calls to the MATLAB function`euclidean`

replaced with calls to the generated MEX function.codegen euclidean.m -args {x,cb} -test test

By default,

`codegen`

generates a MEX function named`euclidean_mex`

in the current folder.You use the

`-args`

option to specify sample input parameters to the entry-point function`euclidean`

. The code generator uses this information to determine the properties of the input arguments.You use the

`-test`

option to run the test file`test.m`

. This option replaces the calls to`euclidean`

in the test file with calls to`euclidean_mex`

.

The output is:

This output matches the output that was generated by the original MATLAB function and verifies the MEX function. Now you are ready to generate standalone C code forRunning test file: 'test' with MEX function 'euclidean_mex'. Coordinates of the closest point are: 0.8 0.8 0.4 Index of the closest point is 171 Distance to the closest point is 0.080374 Coordinates of the farthest point are: 0 0 1 Index of the farthest point is 6 Distance to the farthest point is 1.2923

`euclidean`

.

**Note**

Before generating standalone C/C++ code from your MATLAB code, generate a MEX function. Run the generated MEX function and make sure it has the same run-time behavior as your MATLAB function. If the generated MEX function produces answers that are different from MATLAB, or produces an error, you must fix these issues before proceeding to standalone code generation. Otherwise, the standalone code that you generate might be unreliable and have undefined behavior.

The build script `build_lib_fixed.m`

contains the commands
that you use to generate code for `euclidean.m`

.

% Load the test data load euclidean_data.mat % Generate code for euclidean.m with codegen. Use the test data as example input. codegen -report -config:lib euclidean.m -args {x, cb}

`codegen`

reads the file`euclidean.m`

and translates the MATLAB code into C code.The

`-report`

option instructs`codegen`

to generate a code generation report that you can use to debug code generation issues and verify that your MATLAB code is suitable for code generation.The

`-config:lib`

option instructs`codegen`

to generate a static C library instead of generating the default MEX function.The

`-args`

option instructs`codegen`

to generate code for`euclidean.m`

using the class, size, and complexity of the sample input parameters`x`

and`cb`

.

Instead of generating a C static library, you can choose to generate a MEX
function or other C/C++ build types by using suitable options with the
`codegen`

command. For more information on the various
code generation options, see `codegen`

.

Run the build script.

MATLAB processes the build file and outputs the message:

The code generator produces a standalone C static library`Code generation successful:`

*View report.*`euclidean`

in

. Here,\codegen\lib\euclidean`work`

is the folder that contains your tutorial files.`work`

To view the code generation report in the Report Viewer, click

**View report**.If the code generator detects errors or warnings during code generation, the report describes the issues and provides links to the problematic MATLAB code. See Code Generation Reports.

**Tip**

Use a build script to generate code at the command line. A build script automates a series of MATLAB commands that you perform repeatedly at the command line, saving you time and eliminating input errors.

To compare your generated C code to the original MATLAB code, open the C file, `euclidean.c`

, and the
`euclidean.m`

file in the MATLAB Editor.

Important information about the generated C code:

The function signature is:

void euclidean(const double x[3], const double cb[648], double y_min[3], double y_max[3], double idx[2], double distance[2])

`const double x[3]`

corresponds to the input`x`

in your MATLAB code. The size of`x`

is`3`

, which corresponds to the total size (3 x 1) of the example input you used when you generated code for your MATLAB code.`const double cb[648]`

corresponds to the input`cb`

in your MATLAB code. The size of`cb`

is`648`

, which corresponds to the total size (3 x 216) of the example input you used when you generated code for your MATLAB code. In this case, the generated code uses a one-dimensional array to represent a two-dimensional array in the MATLAB code.The generated code has four additional input arguments: the arrays

`y_min`

,`y_max`

,`idx`

, and`distance`

. These arrays are used to return the output values. They correspond to the output arguments`y_min`

,`y_max`

,`idx`

, and`distance`

in the original MATLAB code.The code generator preserves your function name and comments. When possible, the code generator preserves your variable names.

**Note**If a variable in your MATLAB code is set to a constant value, it does not appear as a variable in the generated C code. Instead, the generated C code contains the actual value of the variable.

With Embedded Coder^{®}, you can interactively trace between MATLAB code and generated C/C++ code. See Interactively Trace Between MATLAB Code and Generated C/C++ Code (Embedded Coder).

The C function that you generated for `euclidean.m`

can accept
only inputs that have the same size as the sample inputs that you specified during
code generation. However, the input arrays to the corresponding MATLAB function can be of any size. In
this part of the tutorial, you generate C code from `euclidean.m`

that accepts variable-size inputs.

Suppose that you want the dimensions of `x`

and
`cb`

in the generated C code to have these properties:

The first dimension of both

`x`

and`cb`

can vary in size up to`3`

.The second dimension of

`x`

is fixed and has the value`1`

.The second dimension of

`cb`

can vary in size up to`216`

.

To specify these input properties, use the `coder.typeof`

function. `coder.typeof(A,B,1)`

specifies a variable-size input with the same class and complexity as
`A`

and upper bounds given by the corresponding element of the
size vector `B`

. Use the build script
`build_lib_variable.m`

that uses
`coder.typeof`

to specify the properties of variable-size
inputs in the generated C
library.

% Load the test data load euclidean_data.mat % Use coder.typeof to specify variable-size inputs eg_x=coder.typeof(x,[3 1],1); eg_cb=coder.typeof(cb,[3 216],1); % Generate code for euclidean.m using coder.typeof to specify % upper bounds for the example inputs codegen -report -config:lib euclidean.m -args {eg_x,eg_cb}

You can now generate code by following the same steps as before. The function
signature for the generated C code in `euclidean.c`

now
reads:

void euclidean(const double x_data[], const int x_size[1], const double cb_data[], const int cb_size[2], double y_min_data[], int y_min_size[1], double y_max_data[], int y_max_size[1], double idx[2], double distance[2])

`x_data`

, `cb_data`

,
`y_min_data`

, and `y_max_data`

correspond to
the input arguments `x`

and `cb`

and the output
arguments `y_min`

and `y_max`

in the original
MATLAB function. The C function now accepts four additional input arguments
`x_size`

, `cb_size`

,
`y_min_size`

and `y_max_size`

that specify the
sizes of `x_data`

, `cb_data`

,
`y_min_data`

, and `y_max_data`

at run
time.Goal | More Information |
---|---|

Learn about code generation support for MATLAB built-in functions and toolbox functions, classes, and System objects | |

Generate C++ code | |

Create and edit input types interactively | Create and Edit Input Types by Using the Coder Type Editor |

Generate and modify an example C main function and use it to build a C executable program | |

Package generated files into a compressed file | |

Optimize the execution speed or memory usage of generated code | |

Integrate your custom C/C++ code into the generated code | |

Learn about the code generation report | Interactively Trace Between MATLAB Code and Generated C/C++ Code (Embedded Coder) |