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Save model object in file for code generation

To generate C/C++ code for the object functions of machine learning models (including `predict`

, `random`

, `knnsearch`

, `rangesearch`

, and incremental learning functions), use `saveLearnerForCoder`

, `loadLearnerForCoder`

, and `codegen`

(MATLAB Coder). After training a machine learning model, save the model by using `saveLearnerForCoder`

. Define an entry-point function that loads the model by using `loadLearnerForCoder`

and calls an object function. Then use `codegen`

or the MATLAB^{®}
Coder™ app to generate C/C++ code. Generating C/C++ code requires MATLAB
Coder.

This flow chart shows the code generation workflow for the object functions of machine learning models. Use `saveLearnerForCoder`

for the highlighted step.

Fixed-point C/C++ code generation requires an additional step that defines the fixed-point data types of the variables required for prediction. Create a fixed-point data type structure by using the data type function generated by `generateLearnerDataTypeFcn`

, and use the structure as an input argument of `loadLearnerForCoder`

in an entry-point function. Generating fixed-point C/C++ code requires MATLAB
Coder and Fixed-Point Designer™.

This flow chart shows the fixed-point code generation workflow for the `predict`

function of a machine learning model. Use `saveLearnerForCoder`

for the highlighted step.

`saveLearnerForCoder(`

prepares a classification model, regression model, or nearest neighbor searcher (`Mdl`

,`filename`

)`Mdl`

) for code generation and saves it in the MATLAB formatted binary file (MAT-file) named `filename`

. You can pass `filename`

to `loadLearnerForCoder`

to reconstruct the model object from the `filename`

file.

`saveLearnerForCoder`

prepares a machine learning model (`Mdl`

) for code generation. The function removes some unnecessary properties.

For a model that has a corresponding compact model, the

`saveLearnerForCoder`

function applies the appropriate`compact`

function to the model before saving it.For a model that does not have a corresponding compact model, such as

`ClassificationKNN`

,`ClassificationLinear`

,`RegressionLinear`

,`ExhaustiveSearcher`

, and`KDTreeSearcher`

, the`saveLearnerForCoder`

function removes properties such as hyperparameter optimization properties, training solver information, and others.

`loadLearnerForCoder`

loads the model saved by `saveLearnerForCoder`

.

Use a coder configurer created by

`learnerCoderConfigurer`

for the models listed in this table.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`

After training a machine learning model, create a coder configurer of the model. Use the object functions and properties of the configurer to configure code generation options and to generate code for the

`predict`

and`update`

functions of the model. If you generate code using a coder configurer, you can update model parameters in the generated code without having to regenerate the code. For details, see Code Generation for Prediction and Update Using Coder Configurer.

`loadLearnerForCoder`

| `codegen`

(MATLAB Coder) | `generateLearnerDataTypeFcn`

- Introduction to Code Generation
- Code Generation for Prediction of Machine Learning Model at Command Line
- Code Generation for Prediction of Machine Learning Model Using MATLAB Coder App
- Code Generation for Nearest Neighbor Searcher
- Fixed-Point Code Generation for Prediction of SVM
- Specify Variable-Size Arguments for Code Generation