Train model (Anomaly)
Train model (Anomaly)
Take the output from a DSP block and train an anomaly detection model using K-means or GMM. Updates are streamed over the websocket API.
POST
Train model (Anomaly)
Authorizations
Path Parameters
Project ID
Learn Block ID, use the impulse functions to retrieve the ID
Body
application/json
Which axes (indexes from DSP script) to include in the training set
Example:
[0, 11, 22]DEPRECATED, use "thresholds" instead. Minimum confidence rating required before tagging as anomaly
Example:
0.3
Number of clusters for K-means, or number of components for GMM
Example:
32
If set, skips creating embeddings and measuring memory (used in tests)
List of configurable thresholds for this block.