{"action":"created","issue":{"url":"https://api.github.com/repos/group-24/Palpitate/issues/7","labels_url":"https://api.github.com/repos/group-24/Palpitate/issues/7/labels{/name}","comments_url":"https://api.github.com/repos/group-24/Palpitate/issues/7/comments","events_url":"https://api.github.com/repos/group-24/Palpitate/issues/7/events","html_url":"https://github.com/group-24/Palpitate/issues/7","id":113634005,"number":7,"title":"Statistical Machine Learning Model: WEKA","user":{"login":"coopie","id":6509132,"avatar_url":"https://avatars.githubusercontent.com/u/6509132?v=3","gravatar_id":"","url":"https://api.github.com/users/coopie","html_url":"https://github.com/coopie","followers_url":"https://api.github.com/users/coopie/followers","following_url":"https://api.github.com/users/coopie/following{/other_user}","gists_url":"https://api.github.com/users/coopie/gists{/gist_id}","starred_url":"https://api.github.com/users/coopie/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/coopie/subscriptions","organizations_url":"https://api.github.com/users/coopie/orgs","repos_url":"https://api.github.com/users/coopie/repos","events_url":"https://api.github.com/users/coopie/events{/privacy}","received_events_url":"https://api.github.com/users/coopie/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":{"login":"markezorvaldez","id":15170160,"avatar_url":"https://avatars.githubusercontent.com/u/15170160?v=3","gravatar_id":"","url":"https://api.github.com/users/markezorvaldez","html_url":"https://github.com/markezorvaldez","followers_url":"https://api.github.com/users/markezorvaldez/followers","following_url":"https://api.github.com/users/markezorvaldez/following{/other_user}","gists_url":"https://api.github.com/users/markezorvaldez/gists{/gist_id}","starred_url":"https://api.github.com/users/markezorvaldez/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/markezorvaldez/subscriptions","organizations_url":"https://api.github.com/users/markezorvaldez/orgs","repos_url":"https://api.github.com/users/markezorvaldez/repos","events_url":"https://api.github.com/users/markezorvaldez/events{/privacy}","received_events_url":"https://api.github.com/users/markezorvaldez/received_events","type":"User","site_admin":false},"milestone":{"url":"https://api.github.com/repos/group-24/Palpitate/milestones/2","html_url":"https://github.com/group-24/Palpitate/milestones/Sprint%202","labels_url":"https://api.github.com/repos/group-24/Palpitate/milestones/2/labels","id":1379859,"number":2,"title":"Sprint 2","description":"This sprint will be to create a working prototype of the whole test system. We will be able to:\r\n\r\n* Preprocess the data taken from the database. This program will take input of the features we want to extract using openSMILE, and return either a DB or directory which contains the information we need for training. Parts of this program will be used in the future when we are analysing audio and video in realtime. Eduardo has sugested looking at when people are pronouncing vowels, and so some form of \"snipping\" the video will be needed.\r\n\r\n* Use the data in some way to train the model we have in WEKA. The program will be able to train given an instance of the data that we have (video, audio features) and train against the average heartrate in the test.\r\n\r\n* Agree upon, and create a prototype software, which will feature in the demo\r\n\r\n* Make a demo video with blackjack and hookers.\r\n ","creator":{"login":"coopie","id":6509132,"avatar_url":"https://avatars.githubusercontent.com/u/6509132?v=3","gravatar_id":"","url":"https://api.github.com/users/coopie","html_url":"https://github.com/coopie","followers_url":"https://api.github.com/users/coopie/followers","following_url":"https://api.github.com/users/coopie/following{/other_user}","gists_url":"https://api.github.com/users/coopie/gists{/gist_id}","starred_url":"https://api.github.com/users/coopie/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/coopie/subscriptions","organizations_url":"https://api.github.com/users/coopie/orgs","repos_url":"https://api.github.com/users/coopie/repos","events_url":"https://api.github.com/users/coopie/events{/privacy}","received_events_url":"https://api.github.com/users/coopie/received_events","type":"User","site_admin":false},"open_issues":6,"closed_issues":1,"state":"open","created_at":"2015-10-27T16:59:56Z","updated_at":"2015-11-03T00:26:40Z","due_on":"2015-11-06T00:00:00Z","closed_at":null},"comments":1,"created_at":"2015-10-27T17:09:53Z","updated_at":"2015-11-03T12:50:01Z","closed_at":null,"body":"Using WEKA, we must have a comprehensive machine learning model. \r\n\r\n<insert some more info here>"},"comment":{"url":"https://api.github.com/repos/group-24/Palpitate/issues/comments/153341854","html_url":"https://github.com/group-24/Palpitate/issues/7#issuecomment-153341854","issue_url":"https://api.github.com/repos/group-24/Palpitate/issues/7","id":153341854,"user":{"login":"kren1","id":310855,"avatar_url":"https://avatars.githubusercontent.com/u/310855?v=3","gravatar_id":"","url":"https://api.github.com/users/kren1","html_url":"https://github.com/kren1","followers_url":"https://api.github.com/users/kren1/followers","following_url":"https://api.github.com/users/kren1/following{/other_user}","gists_url":"https://api.github.com/users/kren1/gists{/gist_id}","starred_url":"https://api.github.com/users/kren1/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/kren1/subscriptions","organizations_url":"https://api.github.com/users/kren1/orgs","repos_url":"https://api.github.com/users/kren1/repos","events_url":"https://api.github.com/users/kren1/events{/privacy}","received_events_url":"https://api.github.com/users/kren1/received_events","type":"User","site_admin":false},"created_at":"2015-11-03T12:50:01Z","updated_at":"2015-11-03T12:50:01Z","body":"Implement something like [class] (https://github.com/group-24/Palpitate/blob/master/src/main/java/ml/models/regression/UserIndependentModel.java) . There is nothing there at the momement. But I imagined the interface be like train(...) where you do the actual training, then load() and save() so we can save the models to disk (Weka should have loads of methods to do that). Then we also need the actual asses(features) function, which is how we use the model.\r\n\r\nThe most important thing so the report how good the model is.\r\n\r\nThere should also be a user dependant model."}}