Train model (Anomaly)

Train model (Anomaly)

post

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.

Authorizations

ApiKeyAuthenticationJWTAuthenticationJWTHttpHeaderAuthentication

Path parameters

projectIdintegerrequired

Project ID

learnIdintegerrequired

Learn Block ID, use the impulse functions to retrieve the ID

Body

axesinteger[]required

Which axes (indexes from DSP script) to include in the training set

Example: [0,11,22]

clusterCountintegeroptional

Number of clusters for K-means, or number of components for GMM

Example: 32

minimumConfidenceRatingnumberrequired

Minimum confidence rating required before tagging as anomaly

Example: 0.3

skipEmbeddingsAndMemorybooleanoptional

If set, skips creating embeddings and measuring memory (used in tests)

Responses

application/json

cURL

JavaScript

Python

HTTP

cURL

curl -L \
  --request POST \
  --url 'https://studio.edgeimpulse.com/v1/api/{projectId}/jobs/train/anomaly/{learnId}' \
  --header 'x-api-key: YOUR_API_KEY' \
  --header 'Content-Type: application/json' \
  --data '{
    "axes": [
      0,
      11,
      22
    ],
    "clusterCount": 32,
    "minimumConfidenceRating": 0.3,
    "skipEmbeddingsAndMemory": true
  }'

200

200

{
  "success": true,
  "error": "text",
  "id": 12873488112
}

OK