Train Model Anomaly

Take the output from a DSP block and train an anomaly detection model using K-means. Updates are streamed over the websocket API.

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cURL

curl --request POST \
  --url https://studio.edgeimpulse.com/v1/api/{projectId}/jobs/train/anomaly/{learnId} \
  --header 'content-type: application/json' \
  --header 'x-jwt-token: REPLACE_KEY_VALUE' \
  --data '{"axes":[0,11,22],"clusterCount":32,"minimumConfidenceRating":0.3}'

Python

import http.client

conn = http.client.HTTPSConnection("studio.edgeimpulse.com")

payload = "{\"axes\":[0,11,22],\"clusterCount\":32,\"minimumConfidenceRating\":0.3}"

headers = {
    'content-type': "application/json",
    'x-jwt-token': "REPLACE_KEY_VALUE"
    }

conn.request("POST", "/v1/api/{projectId}/jobs/train/anomaly/{learnId}", payload, headers)

res = conn.getresponse()
data = res.read()

print(data.decode("utf-8"))

Node.js

const request = require('request');

const options = {
  method: 'POST',
  url: 'https://studio.edgeimpulse.com/v1/api/{projectId}/jobs/train/anomaly/{learnId}',
  headers: {'content-type': 'application/json', 'x-jwt-token': 'REPLACE_KEY_VALUE'},
  body: {axes: [0, 11, 22], clusterCount: 32, minimumConfidenceRating: 0.3},
  json: true
};

request(options, function (error, response, body) {
  if (error) throw new Error(error);

  console.log(body);
});