Code Generation for Prediction and Update Using Coder Configurer

A coder configurer offers convenient features to configure code generation options, generate C/C++ code, and update model parameters in the generated code.

• Configure code generation options and specify the coder attributes of model parameters using object properties.

• Generate C/C++ code for the predict and update functions of the model by using generateCode. This requires MATLAB^® Coder™.

• Update model parameters in the generated C/C++ code without having to regenerate the code. This feature reduces the effort required to regenerate, redeploy, and reverify C/C++ code when you retrain the model with new data or settings. Before updating model parameters, use validatedUpdateInputs to validate and extract the model parameters to update.

This flow chart shows the code generation workflow for the predict and update functions using a coder configurer.

This table shows coder configurer objects corresponding to the supported machine learning models.

Model	Coder Configurer Object
Binary decision tree for multiclass classification	ClassificationTreeCoderConfigurer
SVM for one-class and binary classification	ClassificationSVMCoderConfigurer
Linear model for binary classification	ClassificationLinearCoderConfigurer
Multiclass model for SVMs and linear models	ClassificationECOCCoderConfigurer
Binary decision tree for regression	RegressionTreeCoderConfigurer
Support vector machine (SVM) regression	RegressionSVMCoderConfigurer
Linear regression	RegressionLinearCoderConfigurer

For details and examples, see the reference pages for the coder configurer objects.