Generación de código

Generar código C/C++ para funciones de Statistics and Machine Learning Toolbox™

MATLAB^® Coder™ genera código C y C++ portátil y legible a partir de las funciones de Statistics and Machine Learning Toolbox compatibles con la generación de código. Por ejemplo, puede clasificar nuevas observaciones en dispositivos de hardware que no pueden ejecutar MATLAB mediante la implementación de un modelo de clasificación de máquina de vectores de soporte (SVM, por sus siglas en inglés) entrenado en el dispositivo que utiliza la generación de código.

Puede generar código C/C++ para estas funciones de varias maneras:

Utilice saveLearnerForCoder, loadLearnerForCoder y codegen (MATLAB Coder) para una función de objeto de un modelo de machine learning.
Utilice un configurador de codificadores creado por learnerCoderConfigurer para las funciones de objeto predict y update de un modelo de machine learning. Configure las opciones de generación de código mediante el configurador y actualice los parámetros del modelo en el código generado.
Utilice codegen para otras funciones compatibles con la generación de código.
Entrene un modelo de machine learning en la app Classification Learner o Regression Learner y exporte el modelo a MATLAB Coder.

También puede generar código C/C++ de punto fijo para la predicción de algunos modelos de machine learning. Este tipo de generación de código requiere Fixed-Point Designer™.

Para obtener información sobre la generación de código, consulte Introduction to Code Generation.

Para obtener una lista de las funciones compatibles con la generación de código, consulte Lista de funciones (generación de código C/C++).

Funciones

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Guardar y cargar un modelo para la generación de código

`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
`learnersize`	Compact size of trained machine learning model object (Desde R2024b)

Generación de código mediante el configurador de codificadores

Crear un objeto de configurador de codificadores

learnerCoderConfigurer Create coder configurer of machine learning model

Trabajar con un objeto de configurador de codificadores

`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

Objetos

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Objetos de configurador de codificadores de clasificación

`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

Objetos de configurador de codificadores de regresión

`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

Temas

Flujos de trabajo de generación de código

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 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 Anomaly Detection
Generate single-precision code that detects anomalies in data using a trained isolation forest model or one-class SVM.

Flujos de trabajo de generación de código en apps de machine learning

Export Classification Model to MATLAB Coder to Generate C/C++ Code
Train a model in Classification Learner, and then export the model to MATLAB Coder to generate C/C++ code for prediction.
Export Regression Model to MATLAB Coder to Generate C/C++ Code
Train a model in Regression Learner, and then export the model to MATLAB Coder to generate C/C++ code for prediction.
Generate Code at Command Line Using Model Exported from Machine Learning App
Train a classification model using the Classification Learner app, and generate C/C++ code for prediction at the MATLAB command line.
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.

Aplicaciones de generación de código

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 MATLAB Function Block
Generate code from a Simulink model that classifies data using an SVM model.
Predict Class Labels Using Stateflow
Generate code from a Stateflow^® model that classifies data using a discriminant analysis classifier.

Ejemplos destacados

Code Generation for Image Classification

Generate code from a MATLAB function that classifies images of digits using a trained classification model.

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Human Activity Recognition Simulink Model for Smartphone Deployment

Generate code from a classification Simulink model prepared for deployment to a smartphone.

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