Set Feature Manipulation Model VI

Owning Palette: Feature Manipulation VIs

Requires: Analytics and Machine Learning Toolkit

Sets properties for a trained feature manipulation model before deployment.

Normalize

	model in specifies the information about the entire workflow of the model.
	normalization model specifies the properties of the trained normalization model. normalization settings specifies the settings of normalization. transformation method specifies the method of normalization. 0 ZScore—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. min specifies the minimum value of the normalized data. mean values specifies the mean values of all features in the training data for the Z-Score method. This input is valid only if transformation method is ZScore. standard deviation values specifies the standard deviation values of all features in the training data for the ZScore method. This input is valid only if transformation method is ZScore. min values specifies the minimum values of all features in the training data for the Min-Max method. This input is valid only if transformation method is MinMax. max values specifies the maximum values of all features in the training data for the Min-Max method. This input is valid only if transformation method is MinMax.
	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.
	error out contains error information. This output provides standard error out functionality.

Fisher

	model in specifies the information about the entire workflow of the model.
	selected feature index specifies the indexes of selected features.
	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.
	error out contains error information. This output provides standard error out functionality.

PCA

	model in specifies the information about the entire workflow of the model.
	PCA Model specifies the properties of the trained PCA model. mean values specifies the mean values of all features in the training data. eigenvectors specifies the eigenvectors to calculate the principal components.
	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.
	error out contains error information. This output provides standard error out functionality.

KPCA

	model in specifies the information about the entire workflow of the model.
	KPCA Model specifies the properties of the trained KPCA model. kernel settings specifies the settings to configure the kernel function. This VI supports the following kernel functions: Kernel function Definition Description Polynomial function x and y are the input sample vectors; gamma, coef0, and degree are the algorithm coefficients. Radial basis function x and y are the input sample vectors. Sigmoid function x and y are the input sample vectors; gamma and coef0 are the algorithm coefficients. type specifies the type of the kernel function. 0 Polynomial—Polynomial function 1 RBF—Radial basis function 2 Sigmoid—Sigmoid function degree specifies the degree coefficient of the kernel function. gamma specifies the gamma coefficient of the kernel function. coef0 specifies the coef0 coefficient of the kernel function. variance specifies the variance of the principal components. source data specifies the training data for the KPCA model. eigenvectors specifies the eigenvectors to calculate the principal components.
	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.
	error out contains error information. This output provides standard error out functionality.