Fit a generalized linear regression model, and then save the model by using saveLearnerForCoder
. Define an entry-point function that loads the model by using loadLearnerForCoder
and calls the predict
function of the fitted model. Then use codegen
(MATLAB Coder) to generate C/C++ code. Note that generating C/C++ code requires MATLAB® Coder™.
This example briefly explains the code generation workflow for the prediction of linear regression models at the command line. For more details, see Code Generation for Prediction of Machine Learning Model at Command Line. You can also generate code using the MATLAB Coder app. For details, see Code Generation for Prediction of Machine Learning Model Using MATLAB Coder App.
Train Model
Generate sample data using Poisson random numbers with two underlying predictors X(:,1)
and X(:,2)
.
Create a generalized linear regression model. Specify the Poisson distribution for the response.
Save Model
Save the fitted generalized linear regression model to the file GLMMdl.mat
by using saveLearnerForCoder
.
Define Entry-Point Function
In your current folder, define an entry-point function named mypredictGLM.m
that does the following:
Accept new predictor input and valid name-value pair arguments.
Load the fitted generalized linear regression model in GLMMdl.mat
by using loadLearnerForCoder
.
Return predictions and confidence interval bounds.
function [yhat,ci] = mypredictGLM(x,varargin) %#codegen
%MYPREDICTGLM Predict responses using GLM model
% MYPREDICTGLM predicts responses for the n observations in the n-by-1
% vector x using the GLM model stored in the MAT-file GLMMdl.mat,
% and then returns the predictions in the n-by-1 vector yhat.
% MYPREDICTGLM also returns confidence interval bounds for the
% predictions in the n-by-2 vector ci.
CompactMdl = loadLearnerForCoder('GLMMdl');
narginchk(1,Inf);
[yhat,ci] = predict(CompactMdl,x,varargin{:});
end
Add the %#codegen
compiler directive (or pragma) to the entry-point function after the function signature to indicate that you intend to generate code for the MATLAB algorithm. Adding this directive instructs the MATLAB Code Analyzer to help you diagnose and fix violations that would result in errors during code generation.
Generate Code
Generate code for the entry-point function using codegen
(MATLAB Coder). Because C and C++ are statically typed languages, you must determine the properties of all variables in the entry-point function at compile time. To specify the data type and exact input array size, pass a MATLAB® expression that represents the set of values with a certain data type and array size. Use coder.Constant
(MATLAB Coder) for the names of name-value pair arguments.
Create points for prediction.
Generate code and specify returning 90% simultaneous confidence intervals on the predictions.
codegen
generates the MEX function mypredictGLM_mex
with a platform-dependent extension.
If the number of observations is unknown at compile time, you can also specify the input as variable-size by using coder.typeof
(MATLAB Coder). For details, see Specify Variable-Size Arguments for Code Generation and Specify Properties of Entry-Point Function Inputs (MATLAB Coder).
Verify Generated Code
Compare predictions and confidence intervals using predict
and mypredictGLM_mex
. Specify name-value pair arguments in the same order as in the -args
argument in the call to codegen
.
The returned values from mypredictGLM_mex
might include round-off differences compared to the values from predict
. In this case, compare the values allowing a small tolerance.
ans =
0x1 empty double column vector
ans =
0x1 empty double column vector
The comparison confirms that the returned values are equal within the tolerance 1e–6
.