Generación de código
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 apoyo (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
ycodegen
(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 objetopredict
yupdate
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.
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 integrar la predicción de un modelo de machine learning en Simulink®, utilice un bloque de funciones de MATLAB o los bloques de Simulink de la biblioteca de Statistics and Machine Learning Toolbox.
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
Objetos
Bloques
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 and Classification Learner App
Train a classification model using the Classification Learner app, and generate C/C++ code for prediction. - 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 Logistic Regression Model Trained in Classification Learner
This example shows how to train a logistic regression model using Classification Learner, and then generate C code that predicts labels using the exported classification model.
Bloques de predicción de clasificación y regresión
- Predict Class Labels Using ClassificationSVM Predict Block
This example shows how to use the ClassificationSVM Predict block for label prediction in Simulink®. - Predict Class Labels Using ClassificationTree Predict Block
Train a classification decision tree model using the Classification Learner app, and then use the ClassificationTree Predict block for label prediction. - Predict Class Labels Using ClassificationEnsemble Predict Block
Train a classification ensemble model with optimal hyperparameters, and then use the ClassificationEnsemble Predict block for label prediction. - Predict Class Labels Using ClassificationNeuralNetwork Predict Block
Train a neural network classification model, and then use the ClassificationNeuralNetwork Predict block for label prediction. - Predict Responses Using RegressionSVM Predict Block
Train a support vector machine (SVM) regression model using the Regression Learner app, and then use the RegressionSVM Predict block for response prediction. - Predict Responses Using RegressionTree Predict Block
This example shows how to use the RegressionTree Predict block for response prediction in Simulink®. - Predict Responses Using RegressionEnsemble Predict Block
Train a regression ensemble model with optimal hyperparameters, and then use the RegressionEnsemble Predict block for response prediction. - Predict Responses Using RegressionNeuralNetwork Predict Block
Train a neural network regression model, and then use the RegressionNeuralNetwork Predict block for response prediction. - Predict Responses Using RegressionGP Predict Block
Train a Gaussian process (GP) regression model, and then use the RegressionGP Predict block for response prediction.
Aplicaciones de generación de código
- Predict Class Labels Using MATLAB Function Block
Generate code from a Simulink model that classifies data using an SVM model. - 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 Stateflow
Generate code from a Stateflow® model that classifies data using a discriminant analysis classifier. - Human Activity Recognition Simulink Model for Fixed-Point Deployment
Generate code from a classification Simulink model prepared for fixed-point deployment. - Identify Punch and Flex Hand Gestures Using Machine Learning Algorithm on Arduino Hardware (Simulink Support Package for Arduino Hardware)
This example shows how to use the Simulink® Support Package for Arduino® Hardware to identify punch and flex hand gestures using a machine learning algorithm.