代码生成

为 Statistics and Machine Learning Toolbox™ 函数生成 C/C++ 代码

MATLAB^® Coder™ 可从支持代码生成的 Statistics and Machine Learning Toolbox 函数中生成可读且可移植的 C 代码和 C++ 代码。例如，您可以利用代码生成，将经过训练的支持向量机 (SVM) 分类模型部署到不能运行 MATLAB 的硬件设备上，在这些硬件设备上对新观测值进行分类。

您可以通过多种方式为这些函数生成 C/C++ 代码：

对机器学习模型的对象函数使用 saveLearnerForCoder、loadLearnerForCoder 和 codegen (MATLAB Coder)。
对机器学习模型的 predict 和 update 对象函数使用由 learnerCoderConfigurer 创建的代码生成器配置器。使用配置器配置代码生成选项，并在生成代码中更新模型参数。
对于支持代码生成的其他函数，使用 codegen。
在分类学习器或回归学习器中训练机器学习模型，并将模型导出到 MATLAB Coder。

您还可以生成定点 C/C++ 代码，用于预测一些机器学习模型。这种类型的代码生成需要 Fixed-Point Designer™。

要了解代码生成，请参阅Introduction to Code Generation。

有关支持代码生成的函数的列表，请参阅函数列表（C/C++ 代码生成）。

函数

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保存和加载模型用于进行代码生成

`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 (自 R2024b 起)

使用代码生成器配置器进行代码生成

创建代码生成器配置器对象

learnerCoderConfigurer Create coder configurer of machine learning model

使用代码生成器配置器对象

`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

对象

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分类代码生成器配置器对象

`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

回归代码生成器配置器对象

`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

主题

代码生成工作流

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.

机器学习 App 中的代码生成工作流

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.

代码生成应用程序

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.

精选示例

Code Generation for Image Classification

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

打开实时脚本

Human Activity Recognition Simulink Model for Smartphone Deployment

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

打开实时脚本