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ClassificationECOCCoderConfigurer

Coder configurer for multiclass model using support vector machines (SVMs)

Description

A ClassificationECOCCoderConfigurer object is a coder configurer of a multiclass error-correcting output codes (ECOC) classification model (ClassificationECOC or CompactClassificationECOC) that uses SVM binary learners.

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 model parameters using object properties.

  • Generate C/C++ code for the predict and update functions of the ECOC 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 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 a multiclass ECOC classification model, see the Code Generation sections of CompactClassificationECOC, predict, and update.

Creation

After training a multiclass ECOC classification model with SVM binary learners by using fitcecoc, 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.

Coder attributes of predictor data to pass to the generated C/C++ code for the predict function of the ECOC 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, if you want to generate C/C++ code that accepts predictor data with 100 observations of three predictor variables, specify the coder attributes of X of the coder configurer configurer as follows:

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 the coder attributes of X as follows:

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.

Coder attributes of the binary learner loss function ('BinaryLoss' name-value pair argument of predict), specified as an EnumeratedInput object.

The default attribute values of the EnumeratedInput object are based on the default values of the predict function:

  • Value — Binary learner loss function, specified as one of the character vectors in BuiltInOptions or a character vector designating a custom function name. The default value is 'hinge'.

    To use a custom option, define a custom function on the MATLAB search path, and specify Value as the name of the custom function.

  • SelectedOption — This value is 'Built-in'(default) or 'Custom'. The software sets SelectedOption according to Value. This attribute is read-only.

  • BuiltInOptions — Cell array of 'hamming', 'linear', 'quadratic', 'exponential', 'binodeviance', 'hinge', and 'logit'. This attribute is read-only.

  • IsConstant — This value must be true.

  • Tunability — The default value is false. If you specify other attribute values when Tunability is false, the software sets Tunability to true.

Coder attributes of the decoding scheme ('Decoding' name-value pair argument of predict), specified as an EnumeratedInput object.

The default attribute values of the EnumeratedInput object are based on the default values of the predict function:

  • Value — Decoding scheme value, specified as 'lossweighted'(default), 'lossbased', or a LearnerCoderInput object.

    If you set IsConstant to false, then the software changes Value to a LearnerCoderInput object with these read-only coder attribute values:

    • SizeVector[1,12]

    • VariableDimensions[0,1]

    • DataType'char'

    • Tunability — 1

    The input in the generated code is a variable-size, tunable character vector that is either 'lossweighted' or 'lossbased'.

  • SelectedOption — This value is 'Built-in'(default) or 'NonConstant'. The software sets SelectedOption according to Value. This attribute is read-only.

  • BuiltInOptions — Cell array of 'lossweighted' and 'lossbased'. This attribute is read-only.

  • IsConstant — The default value is true. If you set this value to false, the software changes Value to a LearnerCoderInput object.

  • Tunability — The default value is false. If you specify other attribute values when Tunability is false, the software sets Tunability to true.

Coder attributes of the predictor data observation dimension ('ObservationsIn' name-value pair argument of predict), specified as an EnumeratedInput object.

The default attribute values of the EnumeratedInput object are based on the default values of the predict function:

  • Value — Predictor data observation dimension, specified as 'rows'(default) or 'columns'. This value must be 'rows' for a model that uses SVM binary learners.

  • SelectedOption — This value is always 'Built-in'. This attribute is read-only.

  • BuiltInOptions — Cell array of 'rows' and 'columns'. This attribute is read-only.

  • IsConstant — This value must be true.

  • Tunability — The default value is false. If you specify other attribute values when Tunability is false, the software sets Tunability to true.

Number of output arguments to return from the generated C/C++ code for the predict function of the ECOC classification model, specified as 1, 2, or 3.

The output arguments of predict are, in order: label (predicted class labels), NegLoss (negated average binary losses), and PBScore (positive-class scores). 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. 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 ECOC 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.

Coder attributes of the trained binary learners (BinaryLearners of an ECOC classification model), specified as a ClassificationSVMCoderConfigurer object.

Use the update arguments of the ClassificationSVMCoderConfigurer object to specify the coder attributes of all binary learners. The update arguments are Alpha, Beta, Bias, Cost, Mu, Prior, Scale, Sigma, SupportVectorLabels, and SupportVectors. For the configuration of BinaryLearners, the software uses only the update argument properties and ignores the other properties of ClassificationSVMCoderConfigurer.

Each learner can have a different number of support vectors. Therefore, the software configures the default attribute values of the LearnerCoderInput objects for Alpha, SupportVectorLabels, and SupportVectors to accommodate all binary learners, based on the input argument Mdl of learnerCoderConfigurer.

  • SizeVector

    • This value is [s,1] for Alpha and SupportVectorLabels, where s is the largest number of support vectors in the binary learners.

    • This value is [s,p] for SupportVectors, where p is the number of predictors.

  • VariableDimensions — This value is [0,0] or [1,0]. If each learner has the same number of support vectors, the default value is [0,0]. Otherwise, this value must be [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 is consistent with the data type of the training data you use to train Mdl.

  • Tunability — If you train a model with a linear kernel function, and the model stores the linear predictor coefficients (Beta) without the support vectors and related values, then this value must be false. Otherwise, this value must be true.

For details about the other update arguments, see update arguments of ClassificationSVMCoderConfigurer.

Coder attributes of the misclassification cost (Cost of an ECOC classification 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 [c,c], where c is the number of classes.

  • VariableDimensions — This value must be [0,0], indicating that the array size is fixed as specified in SizeVector.

  • DataType — This value is single or double. The default data type is consistent with the data type of the training data you use to train Mdl.

  • Tunability — The default value is true.

Coder attributes of the prior probabilities (Prior of an ECOC classification 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,c], where c is the number of classes.

  • VariableDimensions — This value must be [0,0], indicating that the array size is fixed as specified in SizeVector.

  • DataType — This value is single or double. The default data type is consistent with the data type of the training data you use to train Mdl.

  • Tunability — The default value is true.

Other Configurer Options

File name of the generated C/C++ code, specified as a character vector.

The object function generateCode of ClassificationECOCCoderConfigurer 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

Verbosity level, specified as true (logical 1) or false (logical 0). The verbosity level controls the display of notification messages at the command line.

ValueDescription
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, 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 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.

This property is read-only.

codegen 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{:})
If you customize the code generation workflow, modify cgArgs accordingly before calling codegen.

If you modify other properties of configurer, the software updates the CodeGenerationArguments property accordingly.

Data Types: cell

List of the tunable input arguments of the entry-point function predict.m for code generation, specified as a cell array. The cell array contains another cell array that includes coder.PrimitiveType objects and coder.Constant objects.

If you modify the coder attributes of predict arguments, then the software updates the corresponding objects accordingly. If you specify the Tunability attribute as false, then the software removes the corresponding object from the PredictInputs list.

The cell array in PredictInputs is equivalent to configurer.CodeGenerationArguments{6} for the coder configurer configurer.

Data Types: cell

List of the tunable input arguments of the entry-point function update.m for code generation, specified as a cell array of a structure. The structure includes a coder.CellType object for BinaryLearners and coder.PrimitiveType objects for Cost and Prior.

If you modify the coder attributes of update arguments, then the software updates the corresponding objects accordingly. If you specify the Tunability attribute as false, then the software removes the corresponding 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

generateCodeGenerate C/C++ code using coder configurer
generateFilesGenerate MATLAB files for code generation using coder configurer
validatedUpdateInputsValidate and extract machine learning model parameters to update

Examples

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Train a machine learning model, and then generate code for the predict and update functions of the model by using a coder configurer.

Load Fisher's iris data set and train a multiclass ECOC model using SVM binary learners.

load fisheriris
X = meas;
Y = species;
Mdl = fitcecoc(X,Y);

Mdl is a ClassificationECOC object.

Create a coder configurer for the ClassificationECOC 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)
configurer = 
  ClassificationECOCCoderConfigurer with properties:

   Update Inputs:
    BinaryLearners: [1x1 ClassificationSVMCoderConfigurer]
             Prior: [1x1 LearnerCoderInput]
              Cost: [1x1 LearnerCoderInput]

   Predict Inputs:
                 X: [1x1 LearnerCoderInput]

   Code Generation Parameters:
        NumOutputs: 1
    OutputFileName: 'ClassificationECOCModel'


  Properties, Methods

configurer is a ClassificationECOCCoderConfigurer object, which is a coder configurer of a ClassificationECOC 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 (MATLAB).

Generate code for the predict and update functions of the ECOC classification model (Mdl) with default settings.

generateCode(configurer)
generateCode creates these files in output folder:
'initialize.m', 'predict.m', 'update.m', 'ClassificationECOCModel.mat'

The generateCode function completes these actions:

  • Generate 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.

  • Create a MEX function named ClassificationECOCModel for the two entry-point functions.

  • Create the code for the MEX function in the codegen\mex\ClassificationECOCModel 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,  13-Apr-2019 03:42:59
[varargout{1:nargout}] = initialize('predict',X,varargin{:});
end
type update.m
function update(varargin) %#codegen
% Autogenerated by MATLAB,  13-Apr-2019 03:42:59
initialize('update',varargin{:});
end
type initialize.m
function [varargout] = initialize(command,varargin) %#codegen
% Autogenerated by MATLAB, 13-Apr-2019 03:42:59
coder.inline('always');
persistent model;
if isempty(model)
    model = loadCompactModel('ClassificationECOCModel.mat');
end
switch(command)
    case 'update'
        % Update struct fields: BinaryLearners
        %                       Prior
        %                       Cost
        
        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

Train an error-correcting output codes (ECOC) model using SVM binary learners and create a coder configurer for the model. Use the properties of the coder configurer to specify coder attributes of the ECOC model parameters. Use the object function of the coder configurer to generate C code that predicts labels for new predictor data. Then retrain the model using different settings, and update parameters in the generated code without regenerating the code.

Train Model

Load Fisher's iris data set.

load fisheriris
X = meas;
Y = species;

Create an SVM binary learner template to use a Gaussian kernel function and to standardize predictor data.

t = templateSVM('KernelFunction','gaussian','Standardize',true);

Train a multiclass ECOC model using the template t.

Mdl = fitcecoc(X,Y,'Learners',t);

Mdl is a ClassificationECOC object.

Create Coder Configurer

Create a coder configurer for the ClassificationECOC 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. Also, set the number of outputs to 2 so that the generated code returns the first two outputs of the predict function, which are the predicted labels and negated average binary losses.

configurer = learnerCoderConfigurer(Mdl,X,'NumOutputs',2)
configurer = 
  ClassificationECOCCoderConfigurer with properties:

   Update Inputs:
    BinaryLearners: [1×1 ClassificationSVMCoderConfigurer]
             Prior: [1×1 LearnerCoderInput]
              Cost: [1×1 LearnerCoderInput]

   Predict Inputs:
                 X: [1×1 LearnerCoderInput]

   Code Generation Parameters:
        NumOutputs: 2
    OutputFileName: 'ClassificationECOCModel'


  Properties, Methods

configurer is a ClassificationECOCCoderConfigurer object, which is a coder configurer of a ClassificationECOC object. The display shows the tunable input arguments of predict and update: X, BinaryLearners, Prior, and Cost.

Specify Coder Attributes of Parameters

Specify the coder attributes of predict arguments (predictor data and the name-value pair arguments 'Decoding' and 'BinaryLoss') and update arguments (support vectors of the SVM learners) so that you can use these arguments as the input arguments of predict and update in the generated code.

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 4];
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 4 predictors, so the second value of the SizeVector attribute must be 4 and the second value of the VariableDimensions attribute must be false.

Next, modify the coder attributes of BinaryLoss and Decoding to use the 'BinaryLoss' and 'Decoding' name-value pair arguments in the generated code. Display the coder attributes of BinaryLoss.

configurer.BinaryLoss
ans = 
  EnumeratedInput with properties:

             Value: 'hinge'
    SelectedOption: 'Built-in'
    BuiltInOptions: {'hamming'  'linear'  'quadratic'  'exponential'  'binodeviance'  'hinge'  'logit'}
        IsConstant: 1
        Tunability: 0

To use a nondefault value in the generated code, you must specify the value before generating the code. Specify the Value attribute of BinaryLoss as 'exponential'.

configurer.BinaryLoss.Value = 'exponential';
configurer.BinaryLoss
ans = 
  EnumeratedInput with properties:

             Value: 'exponential'
    SelectedOption: 'Built-in'
    BuiltInOptions: {'hamming'  'linear'  'quadratic'  'exponential'  'binodeviance'  'hinge'  'logit'}
        IsConstant: 1
        Tunability: 1

If you modify attribute values when Tunability is false (logical 0), the software sets the Tunability to true (logical 1).

Display the coder attributes of Decoding.

configurer.Decoding
ans = 
  EnumeratedInput with properties:

             Value: 'lossweighted'
    SelectedOption: 'Built-in'
    BuiltInOptions: {'lossweighted'  'lossbased'}
        IsConstant: 1
        Tunability: 0

Specify the IsConstant attribute of Decoding as false so that you can use all available values in BuiltInOptions in the generated code.

configurer.Decoding.IsConstant = false;
configurer.Decoding
ans = 
  EnumeratedInput with properties:

             Value: [1×1 LearnerCoderInput]
    SelectedOption: 'NonConstant'
    BuiltInOptions: {'lossweighted'  'lossbased'}
        IsConstant: 0
        Tunability: 1

The software changes the Value attribute of Decoding to a LearnerCoderInput object so that you can use both 'lossweighted' and 'lossbased' as the value of 'Decoding'. Also, the software sets the SelectedOption to 'NonConstant' and the Tunability to true.

Finally, modify the coder attributes of SupportVectors in BinaryLearners. Display the coder attributes of SupportVectors.

configurer.BinaryLearners.SupportVectors
ans = 
  LearnerCoderInput with properties:

            SizeVector: [54 4]
    VariableDimensions: [1 0]
              DataType: 'double'
            Tunability: 1

The default value of VariableDimensions is [true false] because each learner has a different number of support vectors. If you retrain the ECOC model using new data or different settings, the number of support vectors in the SVM learners can vary. Therefore, increase the upper bound of the number of support vectors.

configurer.BinaryLearners.SupportVectors.SizeVector = [150 4];
SizeVector attribute for Alpha has been modified to satisfy configuration constraints.
SizeVector attribute for SupportVectorLabels has been modified to satisfy configuration constraints.

If you modify the coder attributes of SupportVectors, then the software modifies the coder attributes of Alpha and SupportVectorLabels 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.

Display the coder configurer.

configurer
configurer = 
  ClassificationECOCCoderConfigurer with properties:

   Update Inputs:
    BinaryLearners: [1×1 ClassificationSVMCoderConfigurer]
             Prior: [1×1 LearnerCoderInput]
              Cost: [1×1 LearnerCoderInput]

   Predict Inputs:
                 X: [1×1 LearnerCoderInput]
        BinaryLoss: [1×1 EnumeratedInput]
          Decoding: [1×1 EnumeratedInput]

   Code Generation Parameters:
        NumOutputs: 2
    OutputFileName: 'ClassificationECOCModel'


  Properties, Methods

The display now includes BinaryLoss and Decoding as well.

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 (MATLAB).

Generate code for the predict and update functions of the ECOC classification model (Mdl) with default settings.

generateCode(configurer)
generateCode creates these files in output folder:
'initialize.m', 'predict.m', 'update.m', 'ClassificationECOCModel.mat'

The generateCode function completes these actions:

  • Generate 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.

  • Create a MEX function named ClassificationECOCModel for the two entry-point functions.

  • Create the code for the MEX function in the codegen\mex\ClassificationECOCModel folder.

  • Copy 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 labels. 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. Because you specified 'Decoding' as a tunable input argument by changing the IsConstant attribute before generating the code, you also need to specify it in the call to the MEX function, even though 'lossweighted' is the default value of 'Decoding'.

[label,NegLoss] = predict(Mdl,X,'BinaryLoss','exponential');
[label_mex,NegLoss_mex] = ClassificationECOCModel('predict',X,'BinaryLoss','exponential','Decoding','lossweighted');

Compare label to label_mex by using isequal.

isequal(label,label_mex)
ans = logical
   1

isequal returns logical 1 (true) if all the inputs are equal. The comparison confirms that the predict function of Mdl and the predict function in the MEX function return the same labels.

NegLoss_mex might include round-off differences compared to NegLoss. In this case, compare NegLoss_mex to NegLoss, allowing a small tolerance.

find(abs(NegLoss-NegLoss_mex) > 1e-8)
ans =

  0×1 empty double column vector

The comparison confirms that NegLoss and NegLoss_mex are equal within the tolerance 1e–8.

Retrain Model and Update Parameters in Generated Code

Retrain the model using a different setting. Specify 'KernelScale' as 'auto' so that the software selects an appropriate scale factor using a heuristic procedure.

t_new = templateSVM('KernelFunction','gaussian','Standardize',true,'KernelScale','auto');
retrainedMdl = fitcecoc(X,Y,'Learners',t_new);

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);

Update parameters in the generated code.

ClassificationECOCModel('update',params)

Verify Generated Code

Compare the outputs from the predict function of retrainedMdl to the outputs from the predict function in the updated MEX function.

[label,NegLoss] = predict(retrainedMdl,X,'BinaryLoss','exponential','Decoding','lossbased');
[label_mex,NegLoss_mex] = ClassificationECOCModel('predict',X,'BinaryLoss','exponential','Decoding','lossbased');
isequal(label,label_mex)
ans = logical
   1

find(abs(NegLoss-NegLoss_mex) > 1e-8)
ans =

  0×1 empty double column vector

The comparison confirms that labels and labels_mex are equal, and NegLoss and NegLoss_mex are equal within the tolerance.

More About

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Introduced in R2019a