RegressionSVMCoderConfigurer
Coder configurer for support vector machine (SVM) regression model
Description
A RegressionSVMCoderConfigurer
object is a coder configurer of an
SVM regression model (RegressionSVM
or CompactRegressionSVM
).
A coder configurer offers convenient features to configure code generation options, generate C/C++ code, and update model parameters in the generated code.
Configure code generation options and specify the coder attributes for SVM model parameters by using object properties.
Generate C/C++ code for the
predict
andupdate
functions of the SVM regression model by usinggenerateCode
. Generating C/C++ code requires MATLAB® Coder™.Update model parameters in the generated C/C++ code without having to regenerate the code. This feature reduces the effort required to regenerate, redeploy, and reverify C/C++ code when you retrain the SVM model with new data or settings. Before updating model parameters, use
validatedUpdateInputs
to validate and extract the model parameters to update.
This flow chart shows the code generation workflow using a coder configurer.
For the code generation usage notes and limitations of an SVM regression model, see the
Code Generation sections of CompactRegressionSVM
, predict
, and update
.
Creation
After training an SVM regression model by using fitrsvm
, create a coder configurer for the model by using learnerCoderConfigurer
. Use the properties of a coder configurer to specify the
coder attributes of predict
and update
arguments. Then,
use generateCode
to generate C/C++ code based on the specified coder
attributes.
Properties
predict
Arguments
The properties listed in this section specify the coder attributes of the predict
function arguments in the generated code.
X
— Coder attributes of predictor data
LearnerCoderInput
object
Coder attributes of predictor data to pass to the generated C/C++ code for the
predict
function of the SVM regression
model, specified as a LearnerCoderInput
object.
When you create a coder configurer by using the learnerCoderConfigurer
function, the input argument X
determines
the default values of the LearnerCoderInput
coder attributes:
SizeVector
— The default value is the array size of the inputX
.VariableDimensions
— This value is[0 0]
(default) or[1 0]
.[0 0]
indicates that the array size is fixed as specified inSizeVector
.[1 0]
indicates that the array has variable-size rows and fixed-size columns. In this case, the first value ofSizeVector
is the upper bound for the number of rows, and the second value ofSizeVector
is the number of columns.
DataType
— This value issingle
ordouble
. The default data type depends on the data type of the inputX
.Tunability
— This value must betrue
, meaning thatpredict
in the generated C/C++ code always includes predictor data as an input.
You can modify the coder attributes by using dot notation. For example, to generate C/C++ code
that accepts predictor data with 100 observations of three predictor variables, specify
these coder attributes of X
for the coder configurer
configurer
:
configurer.X.SizeVector = [100 3];
configurer.X.DataType = 'double';
configurer.X.VariableDimensions = [0 0];
[0
0]
indicates that the first and second dimensions of X
(number of observations and number of predictor variables, respectively) have fixed
sizes.To allow the generated C/C++ code to accept predictor data with up to 100 observations,
specify these coder attributes of
X
:
configurer.X.SizeVector = [100 3];
configurer.X.DataType = 'double';
configurer.X.VariableDimensions = [1 0];
[1
0]
indicates that the first dimension of X
(number of
observations) has a variable size and the second dimension of X
(number
of predictor variables) has a fixed size. The specified number of observations, 100 in this
example, becomes the maximum allowed number of observations in the generated C/C++ code. To
allow any number of observations, specify the bound as Inf
.
NumOutputs
— Number of outputs in predict
1 (default)
Number of output arguments to return from the generated C/C++ code for the
predict
function of the SVM regression
model, specified as 1. predict
returns yfit
(predicted responses) in the generated C/C++ code.
The NumOutputs
property is equivalent to the
'-nargout'
compiler option of codegen
(MATLAB Coder). This option specifies the number of output arguments in the
entry-point function of code generation. The object function generateCode
generates two entry-point
functions—predict.m
and update.m
for the
predict
and update
functions of an SVM regression model, respectively—and generates C/C++ code for the
two entry-point functions. The specified value for the NumOutputs
property corresponds to the number of output arguments in the entry-point function
predict.m
.
Data Types: single
| double
update
Arguments
The properties listed in this section specify the coder
attributes of the update
function
arguments in the generated code. The update
function takes a trained model
and new model parameters as input arguments, and returns an updated version of the model that
contains the new parameters. To enable updating the parameters in the generated code, you need
to specify the coder attributes of the parameters before generating code. Use a LearnerCoderInput
object to specify the coder attributes of each parameter. The default attribute values are based
on the model parameters in the input argument Mdl
of learnerCoderConfigurer
.
Alpha
— Coder attributes of dual problem coefficients
LearnerCoderInput
object
Coder attributes of the dual problem coefficients (Alpha
of an SVM regression
model), specified as a LearnerCoderInput
object.
The default attribute values of the
LearnerCoderInput
object are based on the input argument
Mdl
of learnerCoderConfigurer
:
SizeVector
— The default value is[s,1]
, wheres
is the number of support vectors inMdl
.VariableDimensions
— This value is[0 0]
(default) or[1 0]
.[0 0]
indicates that the array size is fixed as specified inSizeVector
.[1 0]
indicates that the array has variable-size rows and fixed-size columns. In this case, the first value ofSizeVector
is the upper bound for the number of rows, and the second value ofSizeVector
is the number of columns.
DataType
— This value is'single'
or'double'
. The default data type is consistent with the data type of the training data you use to trainMdl
.Tunability
— If you train a model with a linear kernel function and discard support vectors by usingdiscardSupportVectors
, this value must befalse
. Otherwise, this value must betrue
.
Beta
— Coder attributes of primal linear problem coefficients
LearnerCoderInput
object
Coder attributes of the primal linear problem coefficients (Beta
of an SVM regression model),
specified as a LearnerCoderInput
object.
The default attribute values of the
LearnerCoderInput
object are based on the input argument
Mdl
of learnerCoderConfigurer
:
SizeVector
— This value must be[p 1]
, wherep
is the number of predictors inMdl
.VariableDimensions
— This value must be[0 0]
, indicating that the array size is fixed as specified inSizeVector
.DataType
— This value is'single'
or'double'
. The default data type is consistent with the data type of the training data you use to trainMdl
.Tunability
— If you train a model with a linear kernel function and discard support vectors by usingdiscardSupportVectors
, this value must betrue
. Otherwise, this value must befalse
.
Bias
— Coder attributes of bias term
LearnerCoderInput
object
Coder attributes of the bias term (Bias
of an SVM regression model),
specified as a LearnerCoderInput
object.
The default attribute values of the
LearnerCoderInput
object are based on the input argument
Mdl
of learnerCoderConfigurer
:
SizeVector
— This value must be[1 1]
.VariableDimensions
— This value must be[0 0]
, indicating that the array size is fixed as specified inSizeVector
.DataType
— This value is'single'
or'double'
. The default data type is consistent with the data type of the training data you use to trainMdl
.Tunability
— This value must betrue
.
Mu
— Coder attributes of predictor means
LearnerCoderInput
object
Coder attributes of the predictor means (Mu
of an SVM regression model), specified as a LearnerCoderInput
object.
The default attribute values of the
LearnerCoderInput
object are based on the input argument
Mdl
of learnerCoderConfigurer
:
SizeVector
— If you trainMdl
using standardized predictor data by specifying
, this value must be'Standardize'
,true[1,p]
, wherep
is the number of predictors inMdl
. Otherwise, this value must be[0,0]
.VariableDimensions
— This value must be[0 0]
, indicating that the array size is fixed as specified inSizeVector
.DataType
— This value is'single'
or'double'
. The default data type is consistent with the data type of the training data you use to trainMdl
.Tunability
— If you trainMdl
using standardized predictor data by specifying
, the default value is'Standardize'
,truetrue
. Otherwise, this value must befalse
.
Scale
— Coder attributes of kernel scale parameter
LearnerCoderInput
object
Coder attributes of the kernel scale parameter (KernelParameters
.Scale
of an SVM regression model),
specified as a LearnerCoderInput
object.
The default attribute values of the
LearnerCoderInput
object are based on the input argument
Mdl
of learnerCoderConfigurer
:
SizeVector
— This value must be[1 1]
.VariableDimensions
— This value must be[0 0]
, indicating that the array size is fixed as specified inSizeVector
.DataType
— This value is'single'
or'double'
. The default data type is consistent with the data type of the training data you use to trainMdl
.Tunability
— The default value istrue
.
Sigma
— Coder attributes of predictor standard deviations
LearnerCoderInput
object
Coder attributes of the predictor standard deviations (Sigma
of an SVM regression model),
specified as a LearnerCoderInput
object.
The default attribute values of the
LearnerCoderInput
object are based on the input argument
Mdl
of learnerCoderConfigurer
:
SizeVector
— If you trainMdl
using standardized predictor data by specifying
, this value must be'Standardize'
,true[1,p]
, wherep
is the number of predictors inMdl
. Otherwise, this value must be[0,0]
.VariableDimensions
— This value must be[0 0]
, indicating that the array size is fixed as specified inSizeVector
.DataType
— This value is'single'
or'double'
. The default data type is consistent with the data type of the training data you use to trainMdl
.Tunability
— If you trainMdl
using standardized predictor data by specifying
, the default value is'Standardize'
,truetrue
. Otherwise, this value must befalse
.
SupportVectors
— Coder attributes of support vectors
LearnerCoderInput
object
Coder attributes of the support vectors (SupportVectors
of
an SVM regression model), specified as a LearnerCoderInput
object.
The default attribute values of the
LearnerCoderInput
object are based on the input argument
Mdl
of learnerCoderConfigurer
:
SizeVector
— The default value is[s,p]
, wheres
is the number of support vectors, andp
is the number of predictors inMdl
.VariableDimensions
— This value is[0 0]
(default) or[1 0]
.[0 0]
indicates that the array size is fixed as specified inSizeVector
.[1 0]
indicates that the array has variable-size rows and fixed-size columns. In this case, the first value ofSizeVector
is the upper bound for the number of rows, and the second value ofSizeVector
is the number of columns.
DataType
— This value is'single'
or'double'
. The default data type is consistent with the data type of the training data you use to trainMdl
.Tunability
— If you train a model with a linear kernel function and discard support vectors by usingdiscardSupportVectors
, this value must befalse
. Otherwise, this value must betrue
.
Other Configurer Options
OutputFileName
— File name of generated C/C++ code
'RegressionSVMModel'
(default) | character vector
File name of the generated C/C++ code, specified as a character vector.
The object function generateCode
of
RegressionSVMCoderConfigurer
generates C/C++ code using this file name.
The file name must not contain spaces because they can lead to code generation failures in certain operating system configurations. Also, the name must be a valid MATLAB function name.
After creating the coder configurer configurer
, you can specify the file
name by using dot
notation.
configurer.OutputFileName = 'myModel';
Data Types: char
Verbose
— Verbosity level
true
(logical 1) (default) | false
(logical 0)
Verbosity level, specified as true
(logical 1) or
false
(logical 0). The verbosity level controls the display of
notification messages at the command line.
Value | Description |
---|---|
true (logical 1) | The software displays notification messages when your changes to the coder attributes of a parameter result in changes for other dependent parameters. |
false (logical
0) | The software does not display notification messages. |
To enable updating machine learning model parameters in the generated code, you need to configure the coder attributes of the parameters before generating code. The coder attributes of parameters are dependent on each other, so the software stores the dependencies as configuration constraints. If you modify the coder attributes of a parameter by using a coder configurer, and the modification requires subsequent changes to other dependent parameters to satisfy configuration constraints, then the software changes the coder attributes of the dependent parameters. The verbosity level determines whether or not the software displays notification messages for these subsequent changes.
After creating the coder configurer configurer
, you can modify the
verbosity level by using dot
notation.
configurer.Verbose = false;
Data Types: logical
Options for Code Generation Customization
To customize the code generation workflow, use the generateFiles
function and the following three properties with codegen
(MATLAB Coder), instead of using the generateCode
function.
After generating the two entry-point function files (predict.m
and
update.m
) by using the generateFiles
function, you can modify these files according to your code generation workflow. For
example, you can modify the predict.m
file to include data preprocessing,
or you can add these entry-point functions to another code generation project. Then, you can
generate C/C++ code by using the codegen
(MATLAB Coder) function and the
codegen
arguments appropriate for the modified entry-point
functions or code generation project. Use the three properties described in this section as
a starting point to set the codegen
arguments.
CodeGenerationArguments
— codegen
arguments
cell array
This property is read-only.
codegen
(MATLAB Coder) arguments, specified as a cell array.
This property enables you to customize the code generation workflow. Use the generateCode
function if you do not need to customize your
workflow.
Instead of using generateCode
with the coder configurer configurer
,
you can generate C/C++ code as
follows:
generateFiles(configurer) cgArgs = configurer.CodeGenerationArguments; codegen(cgArgs{:})
cgArgs
accordingly
before calling codegen
.
If you modify other properties of configurer
, the software updates
the CodeGenerationArguments
property accordingly.
Data Types: cell
PredictInputs
— Input argument of predict
cell array of a coder.PrimitiveType
object
This property is read-only.
Input argument of the entry-point function predict.m
for code generation,
specified as a cell array of a coder.PrimitiveType
(MATLAB Coder) object. The
coder.PrimitiveType
object includes the coder attributes of the
predictor data stored in the X
property.
If you modify the coder attributes of the predictor data, then the software updates
the coder.PrimitiveType
object accordingly.
The coder.PrimitiveType
object in PredictInputs
is equivalent to configurer.CodeGenerationArguments{6}
for the coder
configurer configurer
.
Data Types: cell
UpdateInputs
— List of tunable input arguments of update
cell array of a structure including coder.PrimitiveType
objects
This property is read-only.
List of the tunable input arguments of the entry-point function update.m
for code generation, specified as a cell array of a structure including coder.PrimitiveType
(MATLAB Coder) objects. Each coder.PrimitiveType
object includes the coder attributes of a tunable machine learning model
parameter.
If you modify the coder attributes of a model parameter by using the coder configurer
properties (update
Arguments properties), then the software
updates the corresponding coder.PrimitiveType
object accordingly. If
you specify the Tunability
attribute of a machine learning model
parameter as false
, then the software removes the corresponding
coder.PrimitiveType
object from the
UpdateInputs
list.
The structure in UpdateInputs
is equivalent to
configurer.CodeGenerationArguments{3}
for the coder configurer
configurer
.
Data Types: cell
Object Functions
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 |
Examples
Generate Code Using Coder Configurer
Train a machine learning model, and then generate code for the predict
and update
functions of the model by using a coder configurer.
Load the carsmall
data set and train a support vector machine (SVM) regression model.
load carsmall
X = [Horsepower,Weight];
Y = MPG;
Mdl = fitrsvm(X,Y);
Mdl
is a RegressionSVM
object, which is a linear SVM model. The predictor coefficients in a linear SVM model provide enough information to predict responses for new observations. Removing the support vectors reduces memory usage in the generated code. Remove the support vectors from the linear SVM model by using the discardSupportVectors
function.
Mdl = discardSupportVectors(Mdl);
Create a coder configurer for the RegressionSVM
model by using learnerCoderConfigurer
. Specify the predictor data X
. The learnerCoderConfigurer
function uses the input X
to configure the coder attributes of the predict
function input.
configurer = learnerCoderConfigurer(Mdl,X)
Warning: Default response is removed to support learnerCoderConfigurer. The model will predict NaNs for observations with missing values.
configurer = RegressionSVMCoderConfigurer with properties: Update Inputs: Beta: [1x1 LearnerCoderInput] Scale: [1x1 LearnerCoderInput] Bias: [1x1 LearnerCoderInput] Predict Inputs: X: [1x1 LearnerCoderInput] Code Generation Parameters: NumOutputs: 1 OutputFileName: 'RegressionSVMModel'
configurer
is a RegressionSVMCoderConfigurer
object, which is a coder configurer of a RegressionSVM
object.
To generate C/C++ code, you must have access to a C/C++ compiler that is configured properly. MATLAB Coder locates and uses a supported, installed compiler. You can use mex
-setup
to view and change the default compiler. For more details, see Change Default Compiler.
Generate code for the predict
and update
functions of the SVM regression model (Mdl
) with default settings.
generateCode(configurer)
generateCode creates these files in output folder: 'initialize.m', 'predict.m', 'update.m', 'RegressionSVMModel.mat' Code generation successful.
The generateCode
function completes these actions:
Generate the MATLAB files required to generate code, including the two entry-point functions
predict.m
andupdate.m
for thepredict
andupdate
functions ofMdl
, respectively.Create a MEX function named
RegressionSVMModel
for the two entry-point functions.Create the code for the MEX function in the
codegen\mex\RegressionSVMModel
folder.Copy the MEX function to the current folder.
Display the contents of the predict.m
, update.m
, and initialize.m
files by using the type
function.
type predict.m
function varargout = predict(X,varargin) %#codegen % Autogenerated by MATLAB, 20-Jul-2024 17:19:44 [varargout{1:nargout}] = initialize('predict',X,varargin{:}); end
type update.m
function update(varargin) %#codegen % Autogenerated by MATLAB, 20-Jul-2024 17:19:44 initialize('update',varargin{:}); end
type initialize.m
function [varargout] = initialize(command,varargin) %#codegen % Autogenerated by MATLAB, 20-Jul-2024 17:19:44 coder.inline('always') persistent model if isempty(model) model = loadLearnerForCoder('RegressionSVMModel.mat'); end switch(command) case 'update' % Update struct fields: Beta % Scale % Bias model = update(model,varargin{:}); case 'predict' % Predict Inputs: X X = varargin{1}; if nargin == 2 [varargout{1:nargout}] = predict(model,X); else PVPairs = cell(1,nargin-2); for i = 1:nargin-2 PVPairs{1,i} = varargin{i+1}; end [varargout{1:nargout}] = predict(model,X,PVPairs{:}); end end end
Update Parameters of SVM Regression Model in Generated Code
Train a support vector machine (SVM) model using a partial data set and create a coder configurer for the model. Use the properties of the coder configurer to specify coder attributes of the SVM model parameters. Use the object function of the coder configurer to generate C code that predicts responses for new predictor data. Then retrain the model using the whole data set and update parameters in the generated code without regenerating the code.
Train Model
Load the carsmall
data set.
load carsmall
X = [Horsepower,Weight];
Y = MPG;
Train an SVM regression model using the first 50 observations and a Gaussian kernel function with an automatic kernel scale.
Mdl = fitrsvm(X(1:50,:),Y(1:50), ... 'KernelFunction','gaussian','KernelScale','auto');
Mdl
is a RegressionSVM
object.
Create Coder Configurer
Create a coder configurer for the RegressionSVM
model by using learnerCoderConfigurer
. Specify the predictor data X
. The learnerCoderConfigurer
function uses the input X
to configure the coder attributes of the predict
function input.
configurer = learnerCoderConfigurer(Mdl,X(1:50,:));
Warning: Default response is removed to support learnerCoderConfigurer. The model will predict NaNs for observations with missing values.
configurer
is a RegressionSVMCoderConfigurer
object, which is a coder configurer of a RegressionSVM
object.
Specify Coder Attributes of Parameters
Specify the coder attributes of the SVM regression model parameters so that you can update the parameters in the generated code after retraining the model. This example specifies the coder attributes of predictor data that you want to pass to the generated code and the coder attributes of the support vectors of the SVM regression model.
First, specify the coder attributes of X
so that the generated code accepts any number of observations. Modify the SizeVector
and VariableDimensions
attributes. The SizeVector
attribute specifies the upper bound of the predictor data size, and the VariableDimensions
attribute specifies whether each dimension of the predictor data has a variable size or fixed size.
configurer.X.SizeVector = [Inf 2]; configurer.X.VariableDimensions = [true false];
The size of the first dimension is the number of observations. In this case, the code specifies that the upper bound of the size is Inf
and the size is variable, meaning that X
can have any number of observations. This specification is convenient if you do not know the number of observations when generating code.
The size of the second dimension is the number of predictor variables. This value must be fixed for a machine learning model. X
contains two predictors, so the value of the SizeVector
attribute must be two and the value of the VariableDimensions
attribute must be false
.
If you retrain the SVM model using new data or different settings, the number of support vectors can vary. Therefore, specify the coder attributes of SupportVectors
so that you can update the support vectors in the generated code.
configurer.SupportVectors.SizeVector = [250 2];
SizeVector attribute for Alpha has been modified to satisfy configuration constraints.
configurer.SupportVectors.VariableDimensions = [true false];
VariableDimensions attribute for Alpha has been modified to satisfy configuration constraints.
If you modify the coder attributes of SupportVectors
, then the software modifies the coder attributes of Alpha
to satisfy configuration constraints. If the modification of the coder attributes of one parameter requires subsequent changes to other dependent parameters to satisfy configuration constraints, then the software changes the coder attributes of the dependent parameters.
Generate Code
To generate C/C++ code, you must have access to a C/C++ compiler that is configured properly. MATLAB Coder locates and uses a supported, installed compiler. You can use mex
-setup
to view and change the default compiler. For more details, see Change Default Compiler.
Use generateCode
to generate code for the predict
and update
functions of the SVM regression model (Mdl
) with default settings.
generateCode(configurer)
generateCode creates these files in output folder: 'initialize.m', 'predict.m', 'update.m', 'RegressionSVMModel.mat' Code generation successful.
generateCode
generates the MATLAB files required to generate code, including the two entry-point functions predict.m
and update.m
for the predict
and update
functions of Mdl
, respectively. Then generateCode
creates a MEX function named RegressionSVMModel
for the two entry-point functions in the codegen\mex\RegressionSVMModel
folder and copies the MEX function to the current folder.
Verify Generated Code
Pass some predictor data to verify whether the predict
function of Mdl
and the predict
function in the MEX function return the same predicted responses. To call an entry-point function in a MEX function that has more than one entry point, specify the function name as the first input argument.
yfit = predict(Mdl,X);
yfit_mex = RegressionSVMModel('predict',X);
yfit_mex
might include round-off differences compared with yfit
. In this case, compare yfit
and yfit_mex
, allowing a small tolerance.
find(abs(yfit-yfit_mex) > 1e-6)
ans = 0x1 empty double column vector
The comparison confirms that yfit
and yfit_mex
are equal within the tolerance 1e–6
.
Retrain Model and Update Parameters in Generated Code
Retrain the model using the entire data set.
retrainedMdl = fitrsvm(X,Y, ... 'KernelFunction','gaussian','KernelScale','auto');
Extract parameters to update by using validatedUpdateInputs
. This function detects the modified model parameters in retrainedMdl
and validates whether the modified parameter values satisfy the coder attributes of the parameters.
params = validatedUpdateInputs(configurer,retrainedMdl);
Warning: Default response is removed to support learnerCoderConfigurer. The model will predict NaNs for observations with missing values.
Update parameters in the generated code.
RegressionSVMModel('update',params)
Verify Generated Code
Compare the outputs from the predict
function of retrainedMdl
and the predict
function in the updated MEX function.
yfit = predict(retrainedMdl,X);
yfit_mex = RegressionSVMModel('predict',X);
find(abs(yfit-yfit_mex) > 1e-6)
ans = 0x1 empty double column vector
The comparison confirms that yfit
and yfit_mex
are equal within the tolerance 1e-6
.
More About
LearnerCoderInput
Object
A coder configurer uses a LearnerCoderInput
object to
specify the coder attributes of predict
and update
input arguments.
A LearnerCoderInput
object has the following attributes to specify the
properties of an input argument array in the generated code.
Attribute Name | Description |
---|---|
SizeVector | Array size if the corresponding
Upper bound of the array
size if the corresponding |
VariableDimensions | Indicator specifying whether each dimension of the array has a
variable size or fixed size, specified as
|
DataType | Data type of the array |
Tunability | Indicator specifying whether or not
If you specify other attribute values when
|
After creating a coder configurer, you can modify the coder
attributes by using dot notation. For example, specify the coder attributes of the coefficients
Alpha
of the coder configurer configurer
as
follows:
configurer.Alpha.SizeVector = [100 1];
configurer.Alpha.VariableDimensions = [1 0];
configurer.Alpha.DataType = 'double';
Verbose
) as true
(default), then the software displays notification messages when you modify the coder attributes
of a machine learning model parameter and the modification changes the coder attributes of other
dependent parameters.Version History
Introduced in R2018b
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