Normalize VI

Owning Palette: Feature Manipulation VIs

Requires: Analytics and Machine Learning Toolkit

Trains a normalization model. You can use the normalization model to normalize training data with Z-Score or Min-Max method.

You can perform batch training with this VI when you have a large training data set.

Examples

One Shot

	model in specifies the information about the entire workflow of the model.
	normalization settings specifies the settings of normalization. transformation method specifies the method of normalization. 0 ZScore (default)—Normalizes the data so that the data has a mean of 0 and a variance of 1. 1 MinMax—Normalizes the data so that the data is in the range that range settings defines. range settings specifies the range of normalization for the MinMax method. This input is valid only if transformation method is MinMax. max specifies the maximum value of the normalized data. The default is 1. min specifies the minimum value of the normalized data. The default is 0.
	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.
	normalization model info returns the information of the normalization model. Wire normalization model info to the reference input of a standard Property Node to get an AML Normalize Property Node.
	error out contains error information. This output provides standard error out functionality.

Batch

	reset specifies whether to reset the trained model programmatically at run time. The default is FALSE.
	model in specifies the information about the entire workflow of the model.
	normalization settings specifies the settings of normalization. transformation method specifies the method of normalization. 0 ZScore (default)—Normalizes the data so that the data has a mean of 0 and a variance of 1. 1 MinMax—Normalizes the data so that the data is in the range that range settings defines. range settings specifies the range of normalization for the MinMax method. This input is valid only if transformation method is MinMax. max specifies the maximum value of the normalized data. The default is 1. min specifies the minimum value of the normalized data. The default is 0.
	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.
	normalization model info returns the information of the normalization model. Wire normalization model info to the reference input of a standard Property Node to get an AML Normalize Property Node.
	error out contains error information. This output provides standard error out functionality.

Examples

Refer to the following VIs for examples of using the Normalize VI:

• Feature Manipulation (Training) VI: labview\examples\AML\Feature Manipulation
• Feature Manipulation (Training) (Batch) VI: labview\examples\AML\Feature Manipulation
• Anomaly Detection (Training) VI: labview\examples\AML\Anomaly Detection
• Anomaly Detection (Training) (Batch) VI: labview\examples\AML\Anomaly Detection
• Classification (Set Parameters, Training) VI: labview\examples\AML\Classification