Train Anomaly Detection Model VI

Owning Palette: Anomaly Detection VIs

Requires: Analytics and Machine Learning Toolkit

Trains an anomaly detection model.

Examples

	model in specifies the information about the entire workflow of the model.
	anomaly detection model info in specifies the initialized anomaly detection model for training. You can acquire an initialized anomaly detection model from the following VIs: Initialize Anomaly Detection Model (GMM-CV) VI Initialize Anomaly Detection Model (One-class SVM) VI Initialize Anomaly Detection Model (PCA T2Q) VI Initialize Anomaly Detection Model (SOM-MQE) VI
	error in describes error conditions that occur before this node runs. This input provides standard error in functionality.
	model out returns the information about the entire workflow of the model. Wire model out to the reference input of a standard Property Node to get an AML Analytics Property Node.
	anomaly detection model info out returns the trained anomaly detection model. Wire anomaly detection model info out to the reference input of a standard Property Node to get the properties of the trained anomaly detection model. The following table displays the VI you wire to anomaly detection model info in and the corresponding Property Node you get from anomaly detection model info out. VI Name Property Node Initialize Anomaly Detection Model (GMM-CV) VI AML GMM-CV Initialize Anomaly Detection Model (One-Class SVM) VI AML One-Class SVM Initialize Anomaly Detection Model (PCA T2Q) VI AML PCA T2Q Initialize Anomaly Detection Model (SOM-MQE) VI AML SOM-MQE
	error out contains error information. This output provides standard error out functionality.

Examples

Refer to the following VIs for examples of using the Train Anomaly Detection Model VI:

• Anomaly Detection (Training) VI: labview\examples\AML\Anomaly Detection
• Anomaly Detection (Training) (Batch) VI: labview\examples\AML\Anomaly Detection