Code Generation

Generate C/C++ code and MEX functions 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.

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 integrate the prediction of a machine learning model into Simulink^®, use a MATLAB Function block or the Simulink blocks in the Statistics and Machine Learning Toolbox library.

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

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

Blocks

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Classification Predict Blocks

ClassificationECOC Predict	Classify observations using error-correcting output codes (ECOC) classification model
ClassificationEnsemble Predict	Classify observations using ensemble of decision trees
ClassificationKNN Predict	Classify observations using nearest neighbor classification model
ClassificationLinear Predict	Classify observations using linear classification model
ClassificationNeuralNetwork Predict	Classify observations using neural network classification model
ClassificationSVM Predict	Classify observations using support vector machine (SVM) classifier for one-class and binary classification
ClassificationTree Predict	Classify observations using decision tree classifier

Regression Predict Blocks

RegressionEnsemble Predict	Predict responses using ensemble of decision trees for regression
RegressionGP Predict	Predict responses using Gaussian process (GP) regression model
RegressionLinear Predict	Predict responses using linear regression model
RegressionNeuralNetwork Predict	Predict responses using neural network regression model
RegressionSVM Predict	Predict responses using support vector machine (SVM) regression model
RegressionTree Predict	Predict responses using regression tree model

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 and Classification Learner App
Train a classification model using the Classification Learner app, and generate C/C++ code for prediction.
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 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 for Anomaly Detection
Generate single-precision code that detects anomalies in data using a trained isolation forest model or one-class SVM.

Classification and Regression Predict Blocks

Predict Class Labels Using ClassificationSVM Predict Block
This example shows how to use the ClassificationSVM Predict block for label prediction in Simulink®.
Predict Class Labels Using ClassificationTree Predict Block
Train a classification decision tree model using the Classification Learner app, and then use the ClassificationTree Predict block for label prediction.
Predict Class Labels Using ClassificationLinear Predict Block
This example shows how to use the ClassificationLinear Predict block for label prediction in Simulink®.
Predict Class Labels Using ClassificationECOC Predict Block
Train an ECOC classification model, and then use the ClassificationECOC Predict block for label prediction.
Predict Class Labels Using ClassificationEnsemble Predict Block
Train a classification ensemble model with optimal hyperparameters, and then use the ClassificationEnsemble Predict block for label prediction.
Predict Class Labels Using ClassificationNeuralNetwork Predict Block
Train a neural network classification model, and then use the ClassificationNeuralNetwork Predict block for label prediction.
Predict Class Labels Using ClassificationKNN Predict Block
Train a nearest neighbor classification model, and then use the ClassificationKNN Predict block for label prediction.
Predict Responses Using RegressionSVM Predict Block
Train a support vector machine (SVM) regression model using the Regression Learner app, and then use the RegressionSVM Predict block for response prediction.
Predict Responses Using RegressionTree Predict Block
This example shows how to use the RegressionTree Predict block for response prediction in Simulink®.
Predict Responses Using RegressionLinear Predict Block
This example shows how to use the RegressionLinear Predict block for response prediction in Simulink®.
Predict Responses Using RegressionEnsemble Predict Block
Train a regression ensemble model with optimal hyperparameters, and then use the RegressionEnsemble Predict block for response prediction.
Predict Responses Using RegressionNeuralNetwork Predict Block
Train a neural network regression model, and then use the RegressionNeuralNetwork Predict block for response prediction.
Predict Responses Using RegressionGP Predict Block
Train a Gaussian process (GP) regression model, and then use the RegressionGP Predict block for response prediction.

Code Generation Applications

Predict Class Labels Using MATLAB Function Block
Generate code from a Simulink model that classifies data using an SVM model.
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 Stateflow
Generate code from a Stateflow^® model that classifies data using a discriminant analysis classifier.
Human Activity Recognition Simulink Model for Fixed-Point Deployment
Generate code from a classification Simulink model prepared for fixed-point deployment.
Identify Punch and Flex Hand Gestures Using Machine Learning Algorithm on Arduino Hardware (Simulink Support Package for Arduino Hardware)
This example shows how to use the Simulink® Support Package for Arduino® Hardware to identify punch and flex hand gestures using a machine learning algorithm.
Deploy Neural Network Regression Model to FPGA/ASIC Platform
Predict in Simulink using a neural network regression model, and deploy the Simulink model to an FPGA/ASIC platform by using HDL code generation.