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Owning Palette: Anomaly Detection VIs
Requires: Analytics and Machine Learning Toolkit
Initializes the hyperparameters of the Gaussian mixture model (GMM) algorithm.
Use the Train Anomaly Detection Model VI to train the GMM baseline model with training data. Use the Deploy Anomaly Detection Model VI to deploy the GMM baseline model and calculate the confidence value (CV) of the data for deployment. The Deploy Anomaly Detection Model VI calculates the CV by computing the overlap between the GMM baseline model and the GMM model. The Deploy Anomaly Detection Model VI uses health index to return the CV.
You can initialize a GMM model for batch training with this VI when you have a large training data set. To improve the machine learning model quality and the predictive performance, NI recommends that you shuffle the data set before training so each batch has similar distribution.
Use the pull-down menu to select an instance of this VI.
initial parameters specifies the initial parameters to train the GMM baseline model.
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hyperparameters specifies the hyperparameters to train the GMM baseline model.
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error in describes error conditions that occur before this node runs. This input provides standard error in functionality. | |||||||||||
untrained GMM baseline model returns the initialized GMM baseline model for training. | |||||||||||
error out contains error information. This output provides standard error out functionality. |
initial parameters specifies the initial parameters to train the GMM baseline model.
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hyperparameters specifies the hyperparameters to train the GMM baseline model.
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error in describes error conditions that occur before this node runs. This input provides standard error in functionality. | |||||||||||
untrained GMM baseline model returns the initialized GMM baseline model for training. | |||||||||||
error out contains error information. This output provides standard error out functionality. |
The following equation defines the GMM model:
where | |
K is the number of Gaussian mixture components. Each Gaussian mixture component represents a Gaussian distribution. | |
μ_{i} and Σ_{i} are the mean value and the covariance value of the ith Gaussian mixture component | |
α_{i} is the weight of the ith Gaussian mixture component |
Refer to the following VIs for examples of using the Initialize Anomaly Detection Model (GMM-CV) VI:
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