Normalize VI
- Updated2023-02-21
- 4 minute(s) read
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.
One Shot
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model in specifies the information about the entire workflow of the model. | ||||||||||||
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normalization settings specifies the settings of normalization.
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error in describes error conditions that occur before this node runs. This input provides standard error in functionality. | ||||||||||||
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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. | ||||||||||||
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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. | ||||||||||||
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error out contains error information. This output provides standard error out functionality. |
Batch
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reset specifies whether to reset the trained model programmatically at run time. The default is FALSE. | ||||||||||||
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model in specifies the information about the entire workflow of the model. | ||||||||||||
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normalization settings specifies the settings of normalization.
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error in describes error conditions that occur before this node runs. This input provides standard error in functionality. | ||||||||||||
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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. | ||||||||||||
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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. | ||||||||||||
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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