IncrementalClassificationNaiveBayes Predict

Classify observations using incremental naive Bayes classification model

Since R2025a

• 

Libraries:
Statistics and Machine Learning Toolbox / Incremental Learning / Classification / NaiveBayes

Description

The IncrementalClassificationNaiveBayes Predict block classifies observations using a trained naive Bayes classification model returned as the output of an IncrementalClassificationNaiveBayes Fit block.

Import an initial naive Bayes classification model object into the block by specifying the name of a workspace variable that contains the object. The input port mdl receives a bus signal that represents an incremental learning model fit to streaming data. The input port x receives a chunk of predictor data (observations), and the output port label returns predicted class labels for the chunk. The optional output port score returns predicted class scores or posterior probabilities. The optional output port cost returns the expected classification costs. The optional output port CanPredict returns the prediction status of the trained model.

Examples

Perform Incremental Learning Using IncrementalClassificationNaiveBayes Fit and Predict Blocks

This example shows how to use the IncrementalClassificationNaiveBayes Fit and IncrementalClassificationNaiveBayes Predict blocks for incremental learning and multiclass classification in Simulink®.

Load the human activity data set and randomly shuffle the data. For details on the data set, enter Description at the command line. Responses can be one of five classes: Sitting, Standing, Walking, Running, or Dancing.

load humanactivity
n = numel(actid);
rng(0,"twister") % For reproducibility
idx = randsample(n,n);
X = feat(idx,:);
Y = actid(idx);

Create an initial incremental classification naive Bayes model. The model is configured with 60 predictors, class names, and a metrics warmup period of 200 observations.

nbMdl = incrementalClassificationNaiveBayes(NumPredictors=60,ClassNames=[1,2,3,4,5], MetricsWarmupPeriod=200);

To demonstrate streaming, divide the training data into chunks of 50 observations. For each chunk, select a single observation as a test set to import into the IncrementalClassificationNaiveBayes Predict block.

numObsPerChunk = 50;
nchunk = floor(n/numObsPerChunk);
numPredictors = size(feat,2);
Xin = zeros(numObsPerChunk,numPredictors,nchunk);
Yin = zeros(numObsPerChunk,nchunk);
Xtest = zeros(1,numPredictors,nchunk);
for j = 1:nchunk
    ibegin = min(n,numObsPerChunk*(j-1) + 1);
    iend = min(n,numObsPerChunk*j);
    idx = ibegin:iend;   
    Xin(:,:,j) = X(idx,:);
    Yin(:,j) = Y(idx);
    Xtest(1,:,j) = X(idx(1),:);
 end

Convert the training and test set chunks into time series objects.

t = 0:size(Xin,3)-1;
Xtrain_ts = timeseries(Xin,t,InterpretSingleRowDataAs3D=true);
Ytrain_ts = timeseries(Yin',t,InterpretSingleRowDataAs3D=true);
Xtest_ts = timeseries(Xtest,t,InterpretSingleRowDataAs3D=true);

This example provides a Simulink model, slexIncClassNBPredictExample.slx, shown in the figure below. The model is configured to use incrementalClassificationNaiveBayes as the initial model for the fit block.

slName = "slexIncClassNBPredictExample";
open_system(slName);

Simulate the model and export the simulation outputs to the workspace. You can use the Simulation Data Inspector (Simulink) to view the logged data of an Outport block.

simOut = sim(slName,"StopTime",num2str(numel(t)-1));

% Extract labels
label_sig = simOut.yout.getElement(1);
label_sl = squeeze(label_sig.Values.Data);

% Extract scores values
scores_sig = simOut.yout.getElement(2);
scores_sl = squeeze(scores_sig.Values.Data);

% Extract cost values
cost_sig = simOut.yout.getElement(3);
cost_sl = squeeze(cost_sig.Values.Data);

At each iteration, the IncrementalClassificationNaiveBayes Fit block fits a chunk of observations (predictor data) and outputs the updated incremental learning model parameters as a bus signal. The IncrementalClassificationNaiveBayes Predict block calculates the predicted label for each test set observation.

To see how the model parameters and response values evolve during training, plot them on separate tiles.

figure
tiledlayout(3,1);
nexttile
plot(scores_sl(1,:),".")
ylabel("Score")
xlabel("Iteration")
xlim([0 nchunk])
nexttile
plot(label_sl,".")
ylabel("Label")
xlabel("Iteration")
xlim([0 nchunk])
nexttile
plot(cost_sl(1,:),".")
ylabel("Cost")
xlabel("Iteration")
xlim([0 nchunk])

Ports

Input

mdl — Incremental learning model

bus signal

Incremental learning model (incrementalClassificationNaiveBayes) fit to streaming data, specified as a bus signal (see Composite Signals (Simulink)).

x — Chunk of predictor data

numeric matrix

Chunk of predictor data, specified as a numeric matrix.

The block supports only numeric input predictor data. If your input data includes categorical data, you must prepare an encoded version of the categorical data. Use dummyvar to convert each categorical variable to a numeric matrix of dummy variables. Then, concatenate all dummy variable matrices and any other numeric predictors. For more details, see Dummy Variables.

Data Types: single | double | half | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | Boolean | fixed point

Output

label — Chunk of predicted class labels

column vector

Chunk of predicted class labels, returned as a column vector. The predicted class is the class that minimizes the expected classification cost. For more details, see the More About section of the predict object function.

Data Types: single | double | half | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | Boolean | fixed point | enumerated

score — Predicted class scores or posterior probabilities

matrix

Predicted class scores or posterior probabilities, returned as a n-by-numel(mdl.ClassNames) matrix, where n is the number of observations in x. The classification score score(j,k) represents the posterior probability that the observation j in x belongs to class k.

To check the order of the classes, use the ClassNames property of the naive Bayes model specified by Select initial trained machine learning model.

Dependencies

To enable this port, select the check box for Add output port for predicted class scores on the Main tab of the Block Parameters dialog box.

Data Types: single | double | half | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | Boolean | fixed point

cost — Expected misclassification costs

matrix

Expected misclassification costs, returned as a n-by-numel(mdl.ClassNames) matrix, where n is the number of observations in x. The classification cost Cost(j,k) represents the cost of the observation in row j of x being classified into class k.

To check the order of the classes, use the ClassNames property of the naive Bayes model specified by Select initial trained machine learning model.

Dependencies

To enable this port, select the check box for Add output port for expected classification cost on the Main tab of the Block Parameters dialog box.

Data Types: single | double | half | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | Boolean | fixed point

CanPredict — Model status

0 (false) | 1(true)

Model status for prediction, returned as numeric or logical 0 (false) or 1 (true).

Note

If you specify a model that is not trained, then the IncrementalClassificationNaiveBayes Predict cannot predict the response and the model status is 0 (false).

Dependencies

To enable this port, select the check box for Add output port for status of trained machine learning model on the Main tab of the Block Parameters dialog box.

Parameters

To edit block parameters interactively, use the Property Inspector. From the Simulink^® Toolstrip, on the Simulation tab, in the Prepare gallery, select Property Inspector.

Main

Select initial machine learning model — Initial incremental naive Bayes classification model

nbMdl (default) | incrementalClassificationNaiveBayes model object

Specify the name of a workspace variable that contains the configured incrementalClassificationNaiveBayes model object.

The following restrictions apply:

• The predictor data cannot include categorical predictors (logical, categorical, char, string, or cell). If you supply training data in a table, the predictors must be numeric (double or single). To include categorical predictors in a model, preprocess them by using dummyvar before fitting the model.

• The ScoreTransform property of the initial model cannot be "invlogit" or an anonymous function.

• The value of the DistributionNames name-value argument cannot be "mn" or "mvmn".

Programmatic Use

Block Parameter: InitialLearner

Type: character vector or string

Values: incrementalClassificationNaiveBayes object name

Default: "nbMdl"

Add output port for predicted class scores — Add output port for predicted class scores

off (default) | on

Select the check box to include the output port score for predicted class scores in the IncrementalClassificationNaiveBayes Predict block.

Programmatic Use

Block Parameter: ShowOutputScore

Type: character vector or string

Values: "off" | "on"

Default: "off"

Add output port for expected classification cost — Add optional output port for expected classification cost

off (default) | on

Select the check box to include the output port cost in the IncrementalClassificationNaiveBayes Predict block.

Programmatic Use

Block Parameter: ShowOutputCost

Type: character vector or string

Values: "off" | "on"

Default: "off"

Add output port for status of trained machine learning model — Add second output port for model status

off (default) | on

Select the check box to include the output port CanPredict in the IncrementalClassificationNaiveBayes Predict block. This check box does not appear if the workspace already contains an incremental Naive Bayes classification model named nbMdl capable of prediction when you created the IncrementalClassificationNaiveBayes Predict block. Alternatively, you can specify to include the output port CanPredict by selecting theIncrementalClassificationNaiveBayes Predict block in the Simulink workspace and entering set_param(gcb,ShowOutputCanPredict="on") at the MATLAB command line.

Programmatic Use

Block Parameter: ShowOutputCanPredict

Type: character vector or string

Values: "off" | "on"

Default: "off"

Sample time (–1 for inherited) — Option to specify sample time

–1 (default) | scalar

Specify the discrete interval between sample time hits or specify another type of sample time, such as continuous (0) or inherited (–1). For more options, see Types of Sample Time (Simulink).

By default, the IncrementalClassificationNaiveBayes Predict block inherits sample time based on the context of the block within the model.

Programmatic Use

Block Parameter: SystemSampleTime

Type: string scalar or character vector

Values: scalar

Default: "–1"

Data Types

Fixed-Point Operational Parameters

Integer rounding mode — Rounding mode for fixed-point operations

Floor (default) | Ceiling | Convergent | Nearest | Round | Simplest | Zero

Specify the rounding mode for fixed-point operations. For more information, see Rounding Modes (Fixed-Point Designer).

Block parameters always round to the nearest representable value. To control the rounding of a block parameter, enter an expression into the mask field using a MATLAB^® rounding function.

Programmatic Use

Block Parameter: RndMeth

Type: character vector

Values: "Ceiling" | "Convergent" | "Floor" | "Nearest" | "Round" | "Simplest" | "Zero"

Default: "Floor"

Saturate on integer overflow — Method of overflow action

off (default) | on

Specify whether overflows saturate or wrap.

Action	Rationale	Impact on Overflows	Example
Select this check box (on).	Your model has possible overflow, and you want explicit saturation protection in the generated code.	Overflows saturate to either the minimum or maximum value that the data type can represent.	The maximum value that the int8 (signed 8-bit integer) data type can represent is 127. Any block operation result greater than this maximum value causes overflow of the 8-bit integer. With the check box selected, the block output saturates at 127. Similarly, the block output saturates at a minimum output value of –128.
Clear this check box (off).	You want to optimize the efficiency of your generated code. You want to avoid overspecifying how a block handles out-of-range signals. For more information, see Troubleshoot Signal Range Errors (Simulink).	Overflows wrap to the appropriate value that the data type can represent.	The maximum value that the int8 (signed 8-bit integer) data type can represent is 127. Any block operation result greater than this maximum value causes overflow of the 8-bit integer. With the check box cleared, the software interprets the value causing the overflow as int8, which can produce an unintended result. For example, a block result of 130 (binary 1000 0010) expressed as int8 is –126.

Programmatic Use

Block Parameter: SaturateOnIntegerOverflow

Type: character vector

Values: "off" | "on"

Default: "off"

Lock output data type setting against changes by the fixed-point tools — Prevention of fixed-point tools from overriding data type

off (default) | on

Select this parameter to prevent the fixed-point tools from overriding the data type you specify for the block. For more information, see Use Lock Output Data Type Setting (Fixed-Point Designer).

Programmatic Use

Block Parameter: LockScale

Type: character vector

Values: "off" | "on"

Default: "off"

Data Type

Label data type — Data type of label output

Inherit: Inherit via back propagation | Inherit: auto | double | single | half | int8 | uint8 | int16 | uint16 | int32 | uint32 | int64 | uint64 | boolean | fixdt(1,16,0) | fixdt(1,16,2^0,0) | Enum: <class name> | <data type expression>

Specify the data type for the label output. The type can be inherited, specified as an enumerated data type, or expressed as a data type object such as Simulink.NumericType.

The supported data types depend on the labels used in the model specified by Select initial trained machine learning model.

• If the model uses numeric or logical labels, the supported data types are Inherit: Inherit via back propagation (default), double, single, half, int8, uint8, int16, uint16, int32, uint32, int64, uint64, boolean, fixed point, and a data type object.

• If the model uses nonnumeric labels, the supported data types are Inherit: auto (default), Enum: <class name>, and a data type object.

When you select an inherited option, the software behaves as follows:

• Inherit: Inherit via back propagation (default for numeric and logical labels) — Simulink automatically determines the Label data type of the block during data type propagation (see Data Type Propagation (Simulink)). In this case, the block uses the data type of a downstream block or signal object.

• Inherit: auto (default for nonnumeric labels) — The block uses an autodefined enumerated data type variable. For example, suppose the workspace variable name specified by Select initial trained machine learning model is myMdl, and the class labels are class 1 and class 2. Then, the corresponding label values are myMdl_enumLabels.class_1 and myMdl_enumLabels.class_2. The block converts the class labels to valid MATLAB identifiers by using the matlab.lang.makeValidName function.

For more information about data types, see Control Data Types of Signals (Simulink).

Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).

Programmatic Use

Block Parameter: LabelDataTypeStr

Type: character vector or string

Values: "Inherit: Inherit via back propagation" | "Inherit: auto" | "double" | "single" | "half" | "int8" | "uint8" | "int16" | "uint16" | "int32" | "uint32" | "int64" | "uint64" | "boolean" | "fixdt(1,16,0)" | "fixdt(1,16,2^0,0)" | "Enum: <class name>" | "<data type expression>"

Default: "Inherit: Inherit via back propagation" (for numeric and logical labels) | "Inherit: auto" (for nonnumeric labels)

Label data type Minimum — Minimum value of label output for range checking

[] (default) | scalar

Specify the lower value of the label output range that Simulink checks.

Simulink uses the minimum value to perform:

• Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).

• Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).

• Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).

The Label data type Minimum parameter does not saturate or clip the actual label output signal. To do so, use the Saturation (Simulink) block instead.

Dependencies

You can specify this parameter only if the model specified by Select initial trained machine learning model uses numeric labels.

Programmatic Use

Block Parameter: LabelOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Label data type Maximum — Maximum value of label output for range checking

[] (default) | scalar

Specify the upper value of the label output range that Simulink checks.

Simulink uses the maximum value to perform:

• Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).

• Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).

• Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).

The Label data type Maximum parameter does not saturate or clip the actual label output signal. To do so, use the Saturation (Simulink) block instead.

Dependencies

You can specify this parameter only if the model specified by Select initial trained machine learning model uses numeric labels.

Programmatic Use

Block Parameter: LabelOutMax

Type: character vector

Values: "[]" | scalar

Default: "[]"

Score data type — Data type of score output

Inherit: auto (default) | double | single | half | int8 | uint8 | int16 | uint16 | int32 | uint32 | int64 | uint64 | boolean | fixdt(1,16,0) | fixdt(1,16,2^0,0) | <data type expression>

Specify the data type for the score output. The type can be inherited, specified directly, or expressed as a data type object such as Simulink.NumericType.

When you select Inherit: auto, the block uses a rule that inherits a data type.

For more information about data types, see Control Data Types of Signals (Simulink).

Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).

Programmatic Use

Block Parameter: ScoreDataTypeStr

Type: character vector or string

Values: "Inherit: auto" | "double" | "single" | "half" | "int8" | "uint8" | "int16" | "uint16" | "int32" | "uint32" | "int64" | "uint64" | "boolean" | "fixdt(1,16,0)" | "fixdt(1,16,2^0,0)" | "<data type expression>"

Default: "Inherit: auto"

Score data type Minimum — Minimum value of score output for range checking

[] (default) | scalar

Specify the lower value of the score output range that Simulink checks.

Simulink uses the minimum value to perform:

• Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).

• Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).

• Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).

The Score data type Minimum parameter does not saturate or clip the actual score output. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: ScoreOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Score data type Maximum — Maximum value of score output for range checking

[] (default) | scalar

Specify the upper value of the score output range that Simulink checks.

Simulink uses the maximum value to perform:

• Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).

• Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).

• Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).

The Score data type Maximum parameter does not saturate or clip the actual score output. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: ScoreOutMax

Type: character vector

Values: "[]" | scalar

Default: "[]"

Raw score data type — Untransformed score data type

Inherit: auto (default) | double | single | half | int8 | uint8 | int16 | uint16 | int32 | uint32 | int64 | uint64 | boolean | fixdt(1,16,0) | fixdt(1,16,2^0,0) | <data type expression>

Specify the data type for the internal untransformed scores. The type can be inherited, specified directly, or expressed as a data type object such as Simulink.NumericType.

When you select Inherit: auto, the block uses a rule that inherits a data type.

For more information about data types, see Control Data Types of Signals (Simulink).

Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).

Dependencies

You can specify this parameter only if the model specified by Select initial trained machine learning model uses a score transformation other than "none" (default, same as "identity").

• If the model uses no score transformations ("none" or "identity"), then you can specify the score data type by using Score data type.

• If the model uses a score transformation other than "none" or "identity", then you can specify the data type of untransformed raw scores by using this parameter. To specify the data type of transformed scores, use Score data type.

You can change the score transformation option by specifying the ScoreTransform name-value argument during training, or by modifying the ScoreTransform property after training.

Programmatic Use

Block Parameter: RawScoreDataTypeStr

Type: character vector

Values: "Inherit: auto" | "double" | "single" | "half" | "int8" | "uint8" | "int16" | "uint16" | "int32" | "uint32" | "int64" | "uint64" | "boolean" | "fixdt(1,16,0)" | "fixdt(1,16,2^0,0)" | "<data type expression>"

Default: "Inherit: auto"

Raw score data type Minimum — Minimum untransformed score for range checking

[] (default) | scalar

Specify the lower value of the untransformed score range that Simulink checks.

Simulink uses the minimum value to perform:

• Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).

• Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).

• Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).

The Raw score data type Minimum parameter does not saturate or clip the actual untransformed score signal.

Programmatic Use

Block Parameter: RawScoreOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Raw score data type Maximum — Maximum untransformed score for range checking

[] (default) | scalar

Specify the upper value of the untransformed score range that Simulink checks.

Simulink uses the maximum value to perform:

• Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).

• Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).

• Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).

The Raw score data type Maximum parameter does not saturate or clip the actual untransformed score signal.

Programmatic Use

Block Parameter: RawScoreOutMax

Type: character vector

Values: "[]" | scalar

Default: "[]"

Estimated cost data type — Data type of cost output

Inherit: auto (default) | double | single | half | int8 | uint8 | int16 | uint16 | int32 | uint32 | int64 | uint64 | boolean | fixdt(1,16,0) | fixdt(1,16,2^0,0) | <data type expression>

Specify the data type for the cost output. The type can be inherited, specified directly, or expressed as a data type object such as Simulink.NumericType.

When you select Inherit: auto, the block uses a rule that inherits a data type.

For more information about data types, see Control Data Types of Signals (Simulink).

Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).

Programmatic Use

Block Parameter: CostDataTypeStr

Type: character vector or string

Values: "Inherit: auto" | "double" | "single" | "half" | "int8" | "uint8" | "int16" | "uint16" | "int32" | "uint32" | "int64" | "uint64" | "boolean" | "fixdt(1,16,0)" | "fixdt(1,16,2^0,0)" | "<data type expression>"

Default: "Inherit: auto"

Estimated cost data type Minimum — Minimum value of cost output for range checking

[] (default) | scalar

Specify the lower value of the cost output range that Simulink checks.

Simulink uses the minimum value to perform:

• Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).

• Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).

• Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).

The Estimated cost data type Minimum parameter does not saturate or clip the actual cost signal. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: CostOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Estimated cost data type Maximum — Maximum value of cost output for range checking

[] (default) | scalar

Specify the upper value of the cost output range that Simulink checks.

Simulink uses the maximum value to perform:

• Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).

• Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).

• Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).

The Estimated cost data type Maximum parameter does not saturate or clip the actual cost signal. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: CostOutMax

Type: character vector

Values: "[]" | scalar

Default: "[]"

Additional Data Types

Likelihood data type — Data type of likelihood output

Inherit: auto (default) | double | single | half | int8 | uint8 | int16 | uint16 | int32 | uint32 | int64 | uint64 | boolean | fixdt(1,16,0) | fixdt(1,16,2^0,0) | <data type expression>

Specify the data type for the likelihood output. The type can be inherited, specified directly, or expressed as a data type object such as Simulink.NumericType.

When you select Inherit: auto, the block uses a rule that inherits a data type.

For more information about data types, see Control Data Types of Signals (Simulink).

Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).

Programmatic Use

Block Parameter: LikelihoodDataTypeStr

Type: character vector

Values: "Inherit: auto" | "double" | "single" | "half" | "int8" | "uint8" | "int16" | "uint16" | "int32" | "uint32" | "int64" | "uint64" | "boolean" | "fixdt(1,16,0)" | "fixdt(1,16,2^0,0)" | "<data type expression>"

Default: "Inherit: auto"

Likelihood data type Minimum — Minimum value of likelihood output for range checking

[] (default) | scalar

Specify the lower value of the likelihood output range that Simulink checks.

Simulink uses the minimum value to perform:

• Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).

• Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).

• Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).

The Likelihood data type Minimum parameter does not saturate or clip the actual likelihood values. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: LikelihoodOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Likelihood data type Maximum — Maximum value of likelihood output for range checking

[] (default) | scalar

Specify the upper value of the likelihood output range that Simulink checks.

Simulink uses the maximum value to perform:

• Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).

• Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).

• Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).

The Likelihood data type Maximum parameter does not saturate or clip the actual likelihood values. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: LikelihoodOutMax

Type: character vector

Values: "[]" | scalar

Default: "[]"

Posterior data type — Data type of posterior probability output

Inherit: auto (default) | double | single | half | int8 | uint8 | int16 | uint16 | int32 | uint32 | int64 | uint64 | boolean | fixdt(1,16,0) | fixdt(1,16,2^0,0) | <data type expression>

Specify the data type for posterior probabilities. The type can be inherited, specified directly, or expressed as a data type object such as Simulink.NumericType.

When you select Inherit: auto, the block uses a rule that inherits a data type.

For more information about data types, see Control Data Types of Signals (Simulink).

Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).

Programmatic Use

Block Parameter: PosteriorDataTypeStr

Type: character vector

Values: "Inherit: auto" | "double" | "single" | "half" | "int8" | "uint8" | "int16" | "uint16" | "int32" | "uint32" | "int64" | "uint64" | "boolean" | "fixdt(1,16,0)" | "fixdt(1,16,2^0,0)" | "<data type expression>"

Default: "Inherit: auto"

Posterior data type Minimum — Minimum value of posterior probability output for range checking

[] (default) | scalar

Specify the lower value of the posterior probability output range that Simulink checks.

Simulink uses the minimum value to perform:

• Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).

• Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).

• Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).

The Posterior data type Minimum parameter does not saturate or clip the actual posterior probabilities. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: PosteriorOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Posterior data type Maximum — Maximum value of posterior probability output for range checking

[] (default) | scalar

Specify the upper value of the posterior probability output range that Simulink checks.

Simulink uses the maximum value to perform:

• Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).

• Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).

• Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).

The Posterior data type Maximum parameter does not saturate or clip the actual posterior probabilities. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: PosteriorOutMax

Type: character vector

Values: "[]" | scalar

Default: "[]"

Block Characteristics

Data Types	Boolean | double | enumerated | fixed point | half | integer | single
Direct Feedthrough	yes
Multidimensional Signals	no
Variable-Size Signals	no
Zero-Crossing Detection	no

More About

Data Type Synchronization

The data types of internal model parameters are synchronized to the data type of the enabled score output port. If the score output port is not enabled, the model parameter data types are synchronized to other internal data types.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.

Fixed-Point Conversion
Design and simulate fixed-point systems using Fixed-Point Designer™.

Version History

Introduced in R2025a