Predict Class Labels Using ClassificationDiscriminant Predict Block

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:

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:

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))

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