{"action":"created","issue":{"url":"https://api.github.com/repos/nilmtk/nilmtk/issues/463","labels_url":"https://api.github.com/repos/nilmtk/nilmtk/issues/463/labels{/name}","comments_url":"https://api.github.com/repos/nilmtk/nilmtk/issues/463/comments","events_url":"https://api.github.com/repos/nilmtk/nilmtk/issues/463/events","html_url":"https://github.com/nilmtk/nilmtk/issues/463","id":122282345,"number":463,"title":"Learning appliance models from multiple homes","user":{"login":"nipunreddevil","id":60985,"avatar_url":"https://avatars.githubusercontent.com/u/60985?v=3","gravatar_id":"","url":"https://api.github.com/users/nipunreddevil","html_url":"https://github.com/nipunreddevil","followers_url":"https://api.github.com/users/nipunreddevil/followers","following_url":"https://api.github.com/users/nipunreddevil/following{/other_user}","gists_url":"https://api.github.com/users/nipunreddevil/gists{/gist_id}","starred_url":"https://api.github.com/users/nipunreddevil/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/nipunreddevil/subscriptions","organizations_url":"https://api.github.com/users/nipunreddevil/orgs","repos_url":"https://api.github.com/users/nipunreddevil/repos","events_url":"https://api.github.com/users/nipunreddevil/events{/privacy}","received_events_url":"https://api.github.com/users/nipunreddevil/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":6,"created_at":"2015-12-15T14:20:14Z","updated_at":"2015-12-15T15:34:54Z","closed_at":null,"body":"Our current supervised algorithm implementations- CO and FHMM learn the model from the same home on which we intend to disaggregate. This isn't the most realistic scenario. \r\n\r\nI'll start with Oli's excellent post on [\"What even is supervised/unsupervised disaggregation?\"](http://blog.oliverparson.co.uk/2015/05/what-even-is-supervisedunsupervised.html) as a reference point. What we want to do is to choose a train set and learn the \"general\" appliance model across these homes.\r\n\r\nLet's us assume that we know the number and type of appliances in the test homes.\r\n\r\nWe can use the following procedure:\r\n\r\n1. Divide data set into training and test based on homes. Say, out of 50 homes, we train on 25 homes and test on remaining 25.\r\n2. Say in 20 out of 25 homes we have a fridge. We now feed these 20 traces to a HMM to learn the fridge model. Similarly, we do so for all other appliances.\r\n3. For testing- for each home in test set, we create a FHMM corresponding to the HMMs for the appliances present in the home.\r\n4. For appliances having a single instance, the evaluation should be straightforward. For appliances with multiple instances, we'll need to do a matching with ground truth.\r\n\r\nHow does this sound @JackKelly, @oliparson  ?\r\n\r\nThis approach seems to be different from the Disaggregator interface that we've specified thus far! Any thoughts?\r\n"},"comment":{"url":"https://api.github.com/repos/nilmtk/nilmtk/issues/comments/164800127","html_url":"https://github.com/nilmtk/nilmtk/issues/463#issuecomment-164800127","issue_url":"https://api.github.com/repos/nilmtk/nilmtk/issues/463","id":164800127,"user":{"login":"oliparson","id":810167,"avatar_url":"https://avatars.githubusercontent.com/u/810167?v=3","gravatar_id":"","url":"https://api.github.com/users/oliparson","html_url":"https://github.com/oliparson","followers_url":"https://api.github.com/users/oliparson/followers","following_url":"https://api.github.com/users/oliparson/following{/other_user}","gists_url":"https://api.github.com/users/oliparson/gists{/gist_id}","starred_url":"https://api.github.com/users/oliparson/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/oliparson/subscriptions","organizations_url":"https://api.github.com/users/oliparson/orgs","repos_url":"https://api.github.com/users/oliparson/repos","events_url":"https://api.github.com/users/oliparson/events{/privacy}","received_events_url":"https://api.github.com/users/oliparson/received_events","type":"User","site_admin":false},"created_at":"2015-12-15T15:34:54Z","updated_at":"2015-12-15T15:34:54Z","body":"Related: #195 "}}