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.

Code generation workflow for the predict and update functions with a coder configurer

After training a model, create a coder configurer by using learnerCoderConfigurer, generate code by using generateCode, and then verify the generated code.
After you retrain the model with new data or settings, use validatedUpdateInputs to validate and extract the model parameters. If the retrained model is not eligible for an update, then validatedUpdateInputs returns an error, and you can then create a coder configurer. Otherwise, you can update model parameters in the generated C/C++ code without having to regenerate the code.

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.

Code Generation for Prediction and Update Using Coder Configurer

See Also

Related Topics