ClassificationTreeCoderConfigurer
Coder configurer of binary decision tree model for multiclass classification
Description
A ClassificationTreeCoderConfigurer
object is a coder configurer
of a binary decision tree model for multiclass classification (ClassificationTree
or CompactClassificationTree
).
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 the tree model parameters by using object properties.
Generate C/C++ code for the
predict
andupdate
functions of the classification tree 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 tree 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 classification tree model, see
the Code Generation sections of CompactClassificationTree
, predict
, and update
.
Creation
After training a classification tree model by using fitctree
, create a coder configurer for the model by using learnerCoderConfigurer
. Use the properties of a coder configurer to specify the
coder attributes of the 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 the predictor data to pass to the generated C/C++ code for the
predict
function of the classification
tree 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) | 2 | 3 | 4
Number of output arguments to return from the generated C/C++ code for the
predict
function of the classification
tree model, specified as 1, 2, 3, or 4.
The output arguments of predict
are label
(predicted class labels),
score
(posterior probabilities),
node
(node numbers for predicted
classes), and cnum
(class numbers of predicted
labels), in that order. 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 a classification tree 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
.
Children
— Coder attributes of child nodes for each node
LearnerCoderInput
object
Coder attributes of the child nodes for each node in the tree (Children
of a
classification tree 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[nd 2]
, wherend
is the number of nodes 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
— This value must betrue
.
If you modify the first dimension of SizeVector
to be
newnd
, then the software modifies the first dimension of the
SizeVector
attribute to be newnd
for the
properties ClassProbability
, CutPoint
, and
CutPredictorIndex
. Similarly, if you modify the first dimension
of VariableDimensions
to be 1
, then the software
modifies the first dimension of the VariableDimensions
attribute to
be 1
for these properties.
ClassProbability
— Coder attributes of class probabilities for each node
LearnerCoderInput
object
Coder attributes of the class probabilities for each node in the tree (ClassProbability
of a classification tree 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[nd c]
, wherend
is the number of nodes inMdl
andc
is the number of classes.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
— This value must betrue
.
If you modify the first dimension of SizeVector
to be
newnd
, then the software modifies the first dimension of the
SizeVector
attribute to be newnd
for the
properties Children
, CutPoint
, and
CutPredictorIndex
. Similarly, if you modify the first dimension
of VariableDimensions
to be 1
, then the software
modifies the first dimension of the VariableDimensions
attribute to
be 1
for these properties.
Cost
— Coder attributes of misclassification cost
LearnerCoderInput
object
Coder attributes of the misclassification cost (Cost
of a classification tree
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]
, wherec
is the number of classes.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
.
CutPoint
— Coder attributes of cut point for each node
LearnerCoderInput
object
Coder attributes of the cut point for each node in the tree (CutPoint
of a
classification tree 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[nd 1]
, wherend
is the number of nodes 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
— This value must betrue
.
If you modify the first dimension of SizeVector
to be
newnd
, then the software modifies the first dimension of the
SizeVector
attribute to be newnd
for the
properties Children
, ClassProbability
, and
CutPredictorIndex
. Similarly, if you modify the first dimension
of VariableDimensions
to be 1
, then the software
modifies the first dimension of the VariableDimensions
attribute to
be 1
for these properties.
CutPredictorIndex
— Coder attributes of cut predictor index for each node
LearnerCoderInput
object
Coder attributes of the cut predictor index for each node in the tree (CutPredictorIndex
of a classification tree 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[nd 1]
, wherend
is the number of nodes 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
— This value must betrue
.
If you modify the first dimension of SizeVector
to be
newnd
, then the software modifies the first dimension of the
SizeVector
attribute to be newnd
for the
properties Children
, ClassProbability
, and
CutPoint
. Similarly, if you modify the first dimension of
VariableDimensions
to be 1
, then the software
modifies the first dimension of the VariableDimensions
attribute to
be 1
for these properties.
Prior
— Coder attributes of prior probabilities
LearnerCoderInput
object
Coder attributes of the prior probabilities (Prior
of a classification
tree 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]
, wherec
is the number of classes.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
.
Other Configurer Options
OutputFileName
— File name of generated C/C++ code
'ClassificationTreeModel'
(default) | character vector
File name of the generated C/C++ code, specified as a character vector.
The object function generateCode
of
ClassificationTreeCoderConfigurer
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 fisheriris
data set, which contains flower data, and train a decision tree model.
load fisheriris
X = meas;
Y = species;
Mdl = fitctree(X,Y);
Mdl
is a ClassificationTree
object.
Create a coder configurer for the ClassificationTree
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 = ClassificationTreeCoderConfigurer with properties: Update Inputs: Children: [1x1 LearnerCoderInput] ClassProbability: [1x1 LearnerCoderInput] CutPoint: [1x1 LearnerCoderInput] CutPredictorIndex: [1x1 LearnerCoderInput] Prior: [1x1 LearnerCoderInput] Cost: [1x1 LearnerCoderInput] Predict Inputs: X: [1x1 LearnerCoderInput] Code Generation Parameters: NumOutputs: 1 OutputFileName: 'ClassificationTreeModel'
configurer
is a ClassificationTreeCoderConfigurer
object, which is a coder configurer of a ClassificationTree
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 classification tree model (Mdl
) with default settings.
generateCode(configurer)
generateCode creates these files in output folder: 'initialize.m', 'predict.m', 'update.m', 'ClassificationTreeModel.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
ClassificationTreeModel
for the two entry-point functions.Create the code for the MEX function in the
codegen\mex\ClassificationTreeModel
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 17:19:03 [varargout{1:nargout}] = initialize('predict',X,varargin{:}); end
type update.m
function update(varargin) %#codegen % Autogenerated by MATLAB, 20-Jul-2024 17:19:03 initialize('update',varargin{:}); end
type initialize.m
function [varargout] = initialize(command,varargin) %#codegen % Autogenerated by MATLAB, 20-Jul-2024 17:19:03 coder.inline('always') persistent model if isempty(model) model = loadLearnerForCoder('ClassificationTreeModel.mat'); end switch(command) case 'update' % Update struct fields: Children % ClassProbability % CutPoint % CutPredictorIndex % 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
Update Parameters of Classification Tree Model in Generated Code
Train a decision tree for multiclass classification 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 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 the entire data set, and update parameters in the generated code without regenerating the code.
Train Model
Load the fisheriris
data set, which contains flower data. This data set has four predictors: the sepal length, sepal width, petal length, and petal width of the flowers. The response variable contains the flower species names: setosa, versicolor, and virginica. Train a classification tree model using half of the observations.
load fisheriris X = meas; Y = species; rng('default') % For reproducibility n = length(Y); c = cvpartition(Y,'HoldOut',0.5); idxTrain = training(c,1); XTrain = X(idxTrain,:); YTrain = Y(idxTrain); Mdl = fitctree(XTrain,YTrain);
Mdl
is a ClassificationTree
object.
Create Coder Configurer
Create a coder configurer for the ClassificationTree
model by using learnerCoderConfigurer
. Specify the predictor data. The learnerCoderConfigurer
function uses the input XTrain
to configure the coder attributes of the predict
function input. Also, set the number of outputs to 4 so that the generated code returns predicted labels, scores, node numbers, and class numbers.
configurer = learnerCoderConfigurer(Mdl,XTrain,'NumOutputs',4);
configurer
is a ClassificationTreeCoderConfigurer
object, which is a coder configurer of a ClassificationTree
object.
Specify Coder Attributes of Parameters
Specify the coder attributes of the classification tree model parameters so that you can update the parameters in the generated code after retraining the model.
First, specify the coder attributes of the X
property of configurer
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
ans = 1x2 logical array
1 0
The size of the first dimension is the number of observations. Setting the value of the SizeVector
attribute to Inf
causes the software to change the value of the VariableDimensions
attribute to 1
. In other words, the upper bound of the size is Inf
and the size is variable, meaning that the predictor data 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. Because the predictor data contains 4 predictors, the value of the SizeVector
attribute must be 4
and the value of the VariableDimensions
attribute must be 0
.
If you retrain the tree model using new data or different settings, the number of nodes in the tree can vary. Therefore, specify the first dimension of the SizeVector
attribute of one of these properties so that you can update the number of nodes in the generated code: Children
, ClassProbability
, CutPoint
, or CutPredictorIndex
. The software then modifies the other properties automatically.
For example, set the first value of the SizeVector
attribute of the CutPoint
property to Inf
. The software modifies the SizeVector
and VariableDimensions
attributes of Children
, ClassProbability
, and CutPredictorIndex
to match the new upper bound on the number of nodes in the tree. Additionally, the first value of the VariableDimensions
attribute of CutPoint
changes to 1
.
configurer.CutPoint.SizeVector = [Inf 1];
SizeVector attribute for Children has been modified to satisfy configuration constraints. SizeVector attribute for CutPredictorIndex has been modified to satisfy configuration constraints. VariableDimensions attribute for Children has been modified to satisfy configuration constraints. VariableDimensions attribute for CutPredictorIndex has been modified to satisfy configuration constraints. SizeVector attribute for ClassProbability has been modified to satisfy configuration constraints. VariableDimensions attribute for ClassProbability has been modified to satisfy configuration constraints.
configurer.CutPoint.VariableDimensions
ans = 1x2 logical array
1 0
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.
Generate code for the predict
and update
functions of the classification tree model (Mdl
).
generateCode(configurer)
generateCode creates these files in output folder: 'initialize.m', 'predict.m', 'update.m', 'ClassificationTreeModel.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
ClassificationTreeModel
for the two entry-point functions.Create the code for the MEX function in the
codegen\mex\ClassificationTreeModel
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 output arguments. 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.
[label,score,node,cnum] = predict(Mdl,XTrain);
[label_mex,score_mex,node_mex,cnum_mex] = ClassificationTreeModel('predict',XTrain);
Compare label
and label_mex
by using isequal
. Similarly, compare node
to node_mex
and cnum
to cnum_mex
.
isequal(label,label_mex)
ans = logical
1
isequal(node,node_mex)
ans = logical
1
isequal(cnum,cnum_mex)
ans = logical
1
isequal
returns logical 1 (true
) if all the input arguments are equal. The comparison confirms that the predict
function of Mdl
and the predict
function in the MEX function return the same labels, node numbers, and class numbers.
Compare score
and score_mex
.
max(abs(score-score_mex),[],'all')
ans = 0
In general, score_mex
might include round-off differences compared to score
. In this case, the comparison confirms that score
and score_mex
are equal.
Retrain Model and Update Parameters in Generated Code
Retrain the model using the entire data set.
retrainedMdl = fitctree(X,Y);
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.
ClassificationTreeModel('update',params)
Verify Generated Code
Compare the output arguments from the predict
function of retrainedMdl
and the predict
function in the updated MEX function.
[label,score,node,cnum] = predict(retrainedMdl,X);
[label_mex,score_mex,node_mex,cnum_mex] = ClassificationTreeModel('predict',X);
isequal(label,label_mex)
ans = logical
1
isequal(node,node_mex)
ans = logical
1
isequal(cnum,cnum_mex)
ans = logical
1
max(abs(score-score_mex),[],'all')
ans = 0
The comparison confirms that the labels, node numbers, class numbers, and scores are equal.
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
CutPoint
property of the coder configurer
configurer
:
configurer.CutPoint.SizeVector = [20 1]; configurer.CutPoint.VariableDimensions = [1 0];
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 R2019b
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