Code Generation

Generate C/C++ code for Statistics and Machine Learning Toolbox™ functions

MATLAB^® Coder™ generates readable and portable C and C++ code from Statistics and Machine Learning Toolbox functions that support code generation. For example, you can classify new observations on hardware devices that cannot run MATLAB by deploying a trained support vector machine (SVM) classification model to the device using code generation.

You can generate C/C++ code for these functions in several ways:

Use saveLearnerForCoder, loadLearnerForCoder, and codegen (MATLAB Coder) for an object function of a machine learning model.
Use a coder configurer created by learnerCoderConfigurer for predict and update object functions of a machine learning model. Configure code generation options by using the configurer and update model parameters in the generated code.
Use codegen for other functions that support code generation.
Train a machine learning model in the Classification Learner or Regression Learner app, and export the model to MATLAB Coder.

You can also generate fixed-point C/C++ code for the prediction of some machine learning models. This type of code generation requires Fixed-Point Designer™.

To learn about code generation, see Introduction to Code Generation.

For a list of functions that support code generation, see Function List (C/C++ Code Generation).

Functions

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Save and Load Model for Code Generation

`saveLearnerForCoder`	Save model object in file for code generation
`loadLearnerForCoder`	Reconstruct model object from saved model for code generation
`generateLearnerDataTypeFcn`	Generate function that defines data types for fixed-point code generation
`learnersize`	Compact size of trained machine learning model object (Since R2024b)

Code Generation Using Coder Configurer

Create Coder Configurer Object

learnerCoderConfigurer Create coder configurer of machine learning model

Work with Coder Configurer Object

`generateCode`	Generate C/C++ code using coder configurer
`generateFiles`	Generate MATLAB files for code generation using coder configurer
`validatedUpdateInputs`	Validate and extract machine learning model parameters to update
`update`	Update model parameters for code generation

Objects

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Classification Coder Configurer Objects

`ClassificationTreeCoderConfigurer`	Coder configurer of binary decision tree model for multiclass classification
`ClassificationSVMCoderConfigurer`	Coder configurer for support vector machine (SVM) for one-class and binary classification
`ClassificationLinearCoderConfigurer`	Coder configurer for linear binary classification of high-dimensional data
`ClassificationECOCCoderConfigurer`	Coder configurer for multiclass model using binary learners

Regression Coder Configurer Objects

`RegressionTreeCoderConfigurer`	Coder configurer of binary decision tree model for regression
`RegressionSVMCoderConfigurer`	Coder configurer for support vector machine (SVM) regression model
`RegressionLinearCoderConfigurer`	Coder configurer for linear regression model with high-dimensional data

Topics

Code Generation Workflows

Introduction to Code Generation
Learn how to generate C/C++ code for Statistics and Machine Learning Toolbox functions.
General Code Generation Workflow
Generate code for Statistics and Machine Learning Toolbox functions that do not use machine learning model objects.
Code Generation for Prediction of Machine Learning Model at Command Line
Generate code for the prediction of a classification or regression model at the command line.
Code Generation for Incremental Learning
Generate code that implements incremental learning for binary linear classification at the command line.
Code Generation for Prediction of Machine Learning Model Using MATLAB Coder App
Generate code for the prediction of a classification or regression model by using the MATLAB Coder app.
Code Generation for Prediction and Update Using Coder Configurer
Generate code for the prediction of a model using a coder configurer, and update model parameters in the generated code.
Specify Variable-Size Arguments for Code Generation
Generate code that accepts input arguments whose size might change at run time.
Generate Code to Classify Data in Table
Generate code for classifying data in a table containing numeric and categorical variables.
Create Dummy Variables for Categorical Predictors and Generate C/C++ Code
Convert categorical predictors to numeric dummy variables before fitting an SVM classifier and generating code.
Fixed-Point Code Generation for Prediction of SVM
Generate fixed-point code for the prediction of an SVM classification or regression model.
Code Generation for Nearest Neighbor Searcher
Generate code for finding nearest neighbors using a nearest neighbor searcher model.
Code Generation for Probability Distribution Objects
Generate code that fits a probability distribution object to sample data and evaluates the fitted distribution object.
Code Generation for Anomaly Detection
Generate single-precision code that detects anomalies in data using a trained isolation forest model or one-class SVM.

Code Generation Workflows in Machine Learning Apps

Export Classification Model to MATLAB Coder to Generate C/C++ Code
Train a model in Classification Learner, and then export the model to MATLAB Coder to generate C/C++ code for prediction.
Export Regression Model to MATLAB Coder to Generate C/C++ Code
Train a model in Regression Learner, and then export the model to MATLAB Coder to generate C/C++ code for prediction.
Generate Code at Command Line Using Model Exported from Machine Learning App
Train a classification model using the Classification Learner app, and generate C/C++ code for prediction at the MATLAB command line.
Code Generation for Binary GLM Logistic Regression Model Trained in Classification Learner
This example shows how to train a binary GLM logistic regression model using Classification Learner, and then generate C code that predicts labels using the exported classification model.

Code Generation Applications

System Objects for Classification and Code Generation
Generate code from a System object™ for making predictions using a trained classification model, and use the System object in a Simulink^® model.
Predict Class Labels Using MATLAB Function Block
Generate code from a Simulink model that classifies data using an SVM model.
Predict Class Labels Using Stateflow
Generate code from a Stateflow^® model that classifies data using a discriminant analysis classifier.

Featured Examples

Code Generation for Image Classification

Generate code from a MATLAB function that classifies images of digits using a trained classification model.

Open Live Script

Human Activity Recognition Simulink Model for Smartphone Deployment

Generate code from a classification Simulink model prepared for deployment to a smartphone.

Open Live Script