코드 생성

Statistics and Machine Learning Toolbox™ 함수에 대한 C/C++ 코드 및 MEX 함수 생성

MATLAB^® Coder™는 코드 생성을 지원하는 Statistics and Machine Learning Toolbox 함수에서, 읽을 수 있고 이식 가능한 C 및 C++ 코드를 생성합니다. 예를 들어, 코드 생성을 사용하여 MATLAB을 실행할 수 없는 하드웨어 장치에 훈련된 서포트 벡터 머신(SVM) 분류 모델을 배포해 이 장치에서 새 관측값을 분류할 수 있습니다.

여러 가지 방법으로 다음 함수에 대한 C/C++ 코드를 생성할 수 있습니다.

머신러닝 모델의 객체 함수에 대해서는 saveLearnerForCoder, loadLearnerForCoder, codegen (MATLAB Coder)을 사용합니다.
머신러닝 모델의 predict 및 update 객체 함수에 대해서는 learnerCoderConfigurer로 생성된 코더 구성기를 사용합니다. 이 구성기를 사용하여 코드 생성 옵션을 구성하고 생성된 코드에서 모델 파라미터를 업데이트하십시오.
코드 생성을 지원하는 다른 함수에 대해서는 codegen을 사용합니다.

일부 머신러닝 모델의 예측을 위해 고정소수점 C/C++ 코드를 생성할 수도 있습니다. 이 유형의 코드를 생성하려면 Fixed-Point Designer™가 필요합니다.

머신러닝 모델의 예측을 Simulink^®에 통합하려면 Statistics and Machine Learning Toolbox 라이브러리에서 MATLAB Function 블록이나 Simulink 블록을 사용하십시오.

코드 생성에 대해 알아보려면 Introduction to Code Generation 항목을 참조하십시오.

코드 생성을 지원하는 함수 목록은 함수 목록(C/C++ 코드 생성)을 참조하십시오.

함수

모두 확장

코드 생성을 위한 모델을 저장하고 불러오기

`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

코더 구성기를 사용하여 코드 생성

코더 구성기 객체 생성

learnerCoderConfigurer Create coder configurer of machine learning model

코더 구성기 객체 사용

`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

객체

모두 확장

분류 코더 구성기 객체

`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

회귀 코더 구성기 객체

`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

블록

모두 확장

분류 예측 블록

ClassificationSVM Predict	Classify observations using support vector machine (SVM) classifier for one-class and binary classification
ClassificationTree Predict	Classify observations using decision tree classifier
ClassificationEnsemble Predict	Classify observations using ensemble of decision trees
ClassificationNeuralNetwork Predict	Classify observations using neural network classification model

회귀 예측 블록

RegressionSVM Predict	서포트 벡터 머신(SVM) 회귀 모델을 사용하여 응답 변수 예측
RegressionTree Predict	Predict responses using regression tree model
RegressionEnsemble Predict	Predict responses using ensemble of decision trees for regression
RegressionNeuralNetwork Predict	Predict responses using neural network regression model
RegressionGP Predict	Predict responses using Gaussian process (GP) regression model

도움말 항목

코드 생성 워크플로

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.

분류와 회귀 예측 블록

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.

코드 생성 응용 사례

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.