PCA VI
- Updated2023-02-21
- 4 minute(s) read
PCA VI
Owning Palette: Feature Manipulation VIs
Requires: Analytics and Machine Learning Toolkit
Trains a principal component analysis (PCA) model. You can use the PCA model to reduce the dimension of training data.
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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PCA settings specifies the method and value for this VI to calculate the number of principal components.
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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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PCA model info returns the information about the PCA model. Wire PCA model info to the reference input of a standard Property Node to get an AML PCA 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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number of components specifies the number of principal components. The default is 2. number of components must be reasonable to avoid too much memory usage for a single batch. For the first batch data, number of components must be less than or equal to the number of rows in the training data. Starting from the second batch data, number of components can be 1 or any number that fits in the computation capacity. |
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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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PCA model info returns the information about the PCA model. Wire PCA model info to the reference input of a standard Property Node to get an AML PCA 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 PCA VI:
- Feature Manipulation (Training) VI: labview\examples\AML\Feature Manipulation
- Feature Manipulation (Training) (Batch) VI: labview\examples\AML\Feature Manipulation
- Clustering (Set Parameters, Training) VI: labview\examples\AML\Clustering
- Clustering (Search Parameters, Training) VI: labview\examples\AML\Clustering
- Clustering (Deployment) VI: labview\examples\AML\Clustering