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 a coder configurer created by
update object functions of a machine learning
model. Configure code generation options by using the configurer and
update model parameters in the generated code.
codegen for other functions that support code
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).
|Create coder configurer of machine learning model|
|Coder configurer of binary decision tree model for multiclass classification|
|Coder configurer for support vector machine (SVM) for one-class and binary classification|
|Coder configurer for linear binary classification of high-dimensional data|
|Coder configurer for multiclass model using binary learners|
Learn how to generate C/C++ code for Statistics and Machine Learning Toolbox functions.
Generate code for Statistics and Machine Learning Toolbox functions that do not use machine learning model objects.
Generate code for the prediction of a classification or regression model at the command line.
Generate code for the prediction of a classification or regression model by using the MATLAB Coder app.
Generate code for the prediction of a model using a coder configurer, and update model parameters in the generated code.
Train a classification model using the Classification Learner app, and generate C/C++ code for prediction.
Generate code for finding nearest neighbors using a nearest neighbor searcher model.
Generate code that accepts input arguments whose size might change at run time.
Convert categorical predictors to numeric dummy variables before fitting an SVM classifier and generating code.
Generate fixed-point code for the prediction of an SVM classification or regression model.
Generate code that fits a probability distribution object to sample data and evaluates the fitted distribution object.
Generate code for classifying numeric data in a table using a binary decision tree.
This example shows how to train a support vector machine (SVM) regression model using the Regression Learner app, and then use the RegressionSVM Predict block for response prediction in Simulink®.
This example shows how to use the ClassificationSVM Predict block for label prediction.
Generate code from a Simulink model that classifies data using an SVM model.
Generate code from a System object™ for making predictions using a trained classification model, and use the System object in a Simulink model.
Generate code from a Stateflow® model that classifies data using a discriminant analysis classifier.