Deploy Anomaly Detection Model VI

Owning Palette: Anomaly Detection VIs

Requires: Analytics and Machine Learning Toolkit

Deploys a trained anomaly detection model and returns the health index of input data.

Example

	model in specifies the information about the entire workflow of the model.
	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.
	health index returns anomaly values for the input data. For the GMM baseline model, health index contains only one element for the confidence value, which is in the range [0,1]. The closer the confidence value is to 1, the closer the input data is to normal condition. The closer the confidence value is to 0, the closer the input data is to abnormal condition. For the one-class SVM model, each element in health index is the health index value for each instance. Health index values greater than 0 represent the normal condition. Health index values less than 0 represent the abnormal condition. For the PCA baseline model, health index returns both the T2 value and the Q value in the following pattern: T2, Q, T2, Q, …. The greater the health index value is, the closer the input data is to abnormal condition. For the SOM baseline model, each element of health index is the MQE value for each instance. The greater the MQE value is, the closer the input data is to abnormal condition.
	error out contains error information. This output provides standard error out functionality.

Example

Refer to the Anomaly Detection (Deployment) VI in the labview\examples\AML\Anomaly Detection directory for an example of using the Deploy Anomaly Detection Model VI.