ClassificationSVMCoderConfigurer

Coder configurer for support vector machine (SVM) for one-class and binary classification

Description

A ClassificationSVMCoderConfigurer object is a coder configurer of an SVM classification model (ClassificationSVM or CompactClassificationSVM).

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 of SVM model parameters by using object properties.
Generate C/C++ code for the predict and update functions of the SVM classification model by using generateCode. 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 classification model, see the Code Generation sections of CompactClassificationSVM, predict, and update.

Creation

After training an SVM classification model by using fitcsvm, 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

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`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 classification 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 input X.
VariableDimensions — This value is [0 0](default) or [1 0].
- [0 0] indicates that the array size is fixed as specified in SizeVector.
- [1 0] indicates that the array has variable-size rows and fixed-size columns. In this case, the first value of SizeVector is the upper bound for the number of rows, and the second value of SizeVector is the number of columns.
DataType — This value is single or double. The default data type depends on the data type of the input X.
Tunability — This value must be true, meaning that predict 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) | 2

Number of output arguments to return from the generated C/C++ code for the predict function of the SVM classification model, specified as 1 or 2.

The output arguments of predict are label (predicted class labels) and score (scores or posterior probabilities) in the order of listed. predict in the generated C/C++ code returns the first n outputs of the predict function, where n is the NumOutputs value.

After creating the coder configurer configurer, you can specify the number of outputs by using dot notation.

configurer.NumOutputs = 2;

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 classification 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: 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 trained classifier coefficients
`LearnerCoderInput` object

Coder attributes of the trained classifier coefficients (Alpha of an SVM classification model), specified as a LearnerCoderInput object.