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ClassificationDiscriminant Predict

Classify observations using discriminant analysis model

Since R2024a

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  • ClassificationDiscriminant Predict Block Icon

Libraries:
Statistics and Machine Learning Toolbox / Classification

Description

The ClassificationDiscriminant Predict block classifies observations using a discriminant analysis classification object (ClassificationDiscriminant) for multiclass classification.

Import a trained classification object into the block by specifying the name of a workspace variable that contains the object. The input port x receives an observation (predictor data), and the output port label returns a predicted class label for the observation. The optional output port score returns the predicted class scores or posterior probabilities. The optional output port cost returns the expected classification costs.

Examples

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This example uses:

Open Live Script

This example shows how to use the ClassificationDiscriminant Predict block for label prediction in Simulink®. The block accepts an observation (predictor data) and returns the predicted class label, class score, and expected classification cost for the observation using the trained discriminant analysis classification model. To complete this example, you can use the provided Simulink model, or create a new model.

Train Classification Model

Load the humanactivity data set. This data set contains 24,075 observations of five physical human activities: Sitting, Standing, Walking, Running, and Dancing. Each observation has 60 features extracted from acceleration data measured by smartphone accelerometer sensors.

load humanactivity

Create the predictor X as a numeric matrix that contains 60 features for 24,075 observations. Create the class labels Y as a numeric vector that contains the activity IDs in integers: 1, 2, 3, 4, and 5 representing Sitting, Standing, Walking, Running, and Dancing, respectively.

X = feat;
Y = actid;

Randomly partition observations into a training set and a test set with stratification using the class information in Y. Use approximately 80% of the observations to train a discriminant analysis model, and 20% of the observations to test the performance of the trained model on new data. 

rng(0,"twister") % For reproducibility of the partition
cv = cvpartition(Y,"Holdout",0.20);

Extract the training and test indices.

trainingInds = training(cv);
testInds = test(cv);

Specify the training and test data sets.

XTrain = X(trainingInds,:);
YTrain = Y(trainingInds);
XTest = X(testInds,:);
YTest = Y(testInds);

Train a discriminant analysis classification model by passing the training data XTrain and YTrain to the fitcdiscr function. 

daMdl = fitcdiscr(XTrain,YTrain);

daMdl is a trained ClassificationDiscriminant model. You can use dot notation to access the properties of daMdl. For example, you can enter daMdl.ModelParameters to get more information about the trained model parameters.

Open Provided Simulink Model

This example provides the Simulink model slexClassificationDAPredictExample.slx, which includes the ClassificationDiscriminant Predict block. You can open the Simulink model or create a new model as described in the next section.

Open the Simulink model slexClassificationDAPredictExample.slx.

open_system("slexClassificationDAPredictExample")

When you open the Simulink model, the software runs the code in the PreLoadFcn callback function before loading the model. The PreLoadFcn callback function of slexClassificationDAPredictExample includes code to check if your workspace contains the daMdl variable for the trained model. If the workspace does not contain the variable, PreLoadFcn loads the sample data, trains the discriminant analysis classification model, and creates an input signal for the Simulink model. To view the callback function, in the Setup section on the Modeling tab, click Model Settings and select Model Properties. Then, on the Callbacks tab, select the PreLoadFcn callback function in the Model callbacks pane.

Create Simulink Model

To create a new Simulink model, open the Blank Model template and add the ClassificationDiscriminant Predict block from the Classification section of the Statistics and Machine Learning Toolbox™ library.

Double-click the ClassificationDiscriminant Predict block to open the Block Parameters dialog box. Import a trained ClassificationDiscriminant model into the block by specifying the name of a workspace variable that contains the object. The default variable name is daMdl, which is the object you created at the command line.

Select the check box for Add output port for predicted class scores to add the second output port score, and select the check box for Add output port for expected classification cost to add the third output port cost. Click OK.

Click the Refresh button to refresh the settings of the trained model in the dialog box. The Trained Machine Learning Model section of the dialog box displays the options used to train the model daMdl.

Add one Inport block and three Outport blocks, and connect them to the ClassificationDiscriminant Predict block.

The ClassificationDiscriminant Predict block expects an observation containing 60 predictor values, because the model was trained using a data set with 60 predictor variables. Double-click the Inport block, and set Port dimensions to 60 on the Signal Attributes tab. To specify that the output signals have the same length as the input signal, set Sample time to 1 on the Execution tab of the Inport dialog box. Click OK.

At the command line, create an input signal in the form of a structure array for the Simulink model. The structure array must contain these fields:

  • time — The points in time at which the observations enter the model. The orientation must correspond to the observations in the predictor data. In this example, time must be a column vector.

  • signals — A 1-by-1 structure array describing the input data and containing the fields values and dimensions, where values is a matrix of predictor data, and dimensions is the number of predictor variables.

Create an appropriate structure array for future predictions. 

modelInput.time = (0:length(YTest)-1)';
modelInput.signals(1).values = XTest;
modelInput.signals(1).dimensions = size(XTest,2);

Import the signal data from the workspace:

  • Open the Configuration Parameters dialog box in Simulink. In the Setup section of the Modeling tab, click the top half of the Model Settings button.

  • In the Data Import/Export pane, select the Input check box and enter modelInput in the adjacent text box.

  • In the Solver pane, under Simulation time, set Stop time to modelInput.time(end). Under Solver selection, set Type to Fixed-step, and set Solver to discrete (no continuous states). These settings enable the model to run the simulation for each query point in modelInput. Click OK.

For more details, see Load Signal Data for Simulation (Simulink).

Save the model as slexClassificationDAPredictExample.slx in Simulink.

Simulate Model

Simulate the Simulink model and export the simulation outputs to the workspace. When the Inport block detects an observation, it places the observation into the ClassificationDiscriminant Predict block. You can use the Simulation Data Inspector (Simulink) to view the logged data of an Outport block.

simOut = sim("slexClassificationDAPredictExample");

Determine the simulated classification labels.

outputs = simOut.yout;
sim_label = outputs.get("label").Values.Data;

Create a confusion matrix chart from the true labels (YTest) and the labels predicted by the Simulink model (sim_label).

confusionchart(string(YTest),string(sim_label))

Figure contains an object of type ConfusionMatrixChart.

Large values on the diagonal indicate accurate predictions for the corresponding class.

Ports

Input

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Predictor data, specified as a row or column vector of one observation.

The variables in x must have the same order as the predictor variables that trained the model specified by Select trained machine learning model.

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

Output

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Predicted class label, returned as a scalar. 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

Predicted class scores or posterior probabilities, returned as a row vector of size 1-by-k, where k is the number of classes in the discriminant analysis model. The classification score Score(i) represents the posterior probability that the observation in x belongs to class i.

To check the order of the classes, use the ClassNames property of the discriminant analysis model specified by Select 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

Expected classification costs, returned as a row vector of size 1-by-k, where k is the number of classes in the discriminant analysis model. The classification cost Cost(i) represents the cost of classifying the observation in x to class i.

To check the order of the classes, use the ClassNames property of the discriminant analysis model specified by Select 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

Parameters

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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

Specify the name of a workspace variable that contains a ClassificationDiscriminant object.

When you train the model by using fitcdiscr, 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 value of the ScoreTransform name-value argument cannot be "invlogit" or an anonymous function.

Programmatic Use

Block Parameter: TrainedLearner
Type: workspace variable
Values: ClassificationDiscriminant object
Default: "daMdl"

Select the check box to include the output port score in the ClassificationDiscriminant Predict block.

Programmatic Use

Block Parameter: ShowOutputScore
Type: character vector
Values: "off" | "on"
Default: "off"

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

Programmatic Use

Block Parameter: ShowOutputCost
Type: character vector
Values: "off" | "on"
Default: "off"

Data Types

Fixed-Point Operational Parameters

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"

Specify whether overflows saturate or wrap.

ActionRationaleImpact on OverflowsExample

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"

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

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 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 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
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)

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

Simulink uses the minimum value to perform:

Note

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 trained machine learning model uses numeric labels.

Programmatic Use

Block Parameter: LabelOutMin
Type: character vector
Values: "[]" | scalar
Default: "[]"

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

Simulink uses the maximum value to perform:

Note

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 trained machine learning model uses numeric labels.

Programmatic Use

Block Parameter: LabelOutMax
Type: character vector
Values: "[]" | scalar
Default: "[]"

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
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"

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

Simulink uses the minimum value to perform:

Note

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: "[]"

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

Simulink uses the maximum value to perform:

Note

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: "[]"

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 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"

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

Simulink uses the minimum value to perform:

Note

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: "[]"

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

Simulink uses the maximum value to perform:

Note

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: "[]"

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
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"

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

Simulink uses the minimum value to perform:

Note

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: "[]"

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

Simulink uses the maximum value to perform:

Note

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

Specify the data type of the Mahalanobis distance metric. 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.

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).

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

Note

The Mahalanobis distance data type parameter specifies the data type of the distance metric used internally by the block. For more information, see mahal.

Programmatic Use

Block Parameter: DistanceDataTypeStr
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"

Specify the lower value of the distance range that Simulink checks.

Simulink uses the minimum value to perform:

Note

The Mahalanobis distance data type Minimum parameter does not saturate or clip the actual distance signal.

Programmatic Use

Block Parameter: DistanceOutMin
Type: character vector
Values: "[]" | scalar
Default: "[]"

Specify the upper value of the distance range that Simulink checks.

Simulink uses the maximum value to perform:

Note

The Mahalanobis distance data type Maximum parameter does not saturate or clip the actual distance signal.

Programmatic Use

Block Parameter: DistanceOutMax
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

Alternative Functionality

You can use a MATLAB Function (Simulink) block with the predict object function of a discriminant analysis classification object (ClassificationDiscriminant). For an example, see Predict Class Labels Using MATLAB Function Block.

When deciding whether to use the ClassificationDiscriminant Predict block in the Statistics and Machine Learning Toolbox™ library or a MATLAB Function block with the predict function, consider the following:

  • If you use the Statistics and Machine Learning Toolbox library block, you can use the Fixed-Point Tool (Fixed-Point Designer) to convert a floating-point model to fixed point.

  • Support for variable-size arrays must be enabled for a MATLAB Function block with the predict function.

  • If you use a MATLAB Function block, you can use MATLAB functions for preprocessing or post-processing before or after predictions in the same MATLAB Function block.

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 R2024a

See Also

Blocks

Functions

Objects

Topics