{"action":"opened","issue":{"url":"https://api.github.com/repos/tdhock/coseg/issues/3","repository_url":"https://api.github.com/repos/tdhock/coseg","labels_url":"https://api.github.com/repos/tdhock/coseg/issues/3/labels{/name}","comments_url":"https://api.github.com/repos/tdhock/coseg/issues/3/comments","events_url":"https://api.github.com/repos/tdhock/coseg/issues/3/events","html_url":"https://github.com/tdhock/coseg/issues/3","id":165358511,"number":3,"title":"Regularized isotonic regression solver","user":{"login":"tdhock","id":932850,"avatar_url":"https://avatars.githubusercontent.com/u/932850?v=3","gravatar_id":"","url":"https://api.github.com/users/tdhock","html_url":"https://github.com/tdhock","followers_url":"https://api.github.com/users/tdhock/followers","following_url":"https://api.github.com/users/tdhock/following{/other_user}","gists_url":"https://api.github.com/users/tdhock/gists{/gist_id}","starred_url":"https://api.github.com/users/tdhock/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/tdhock/subscriptions","organizations_url":"https://api.github.com/users/tdhock/orgs","repos_url":"https://api.github.com/users/tdhock/repos","events_url":"https://api.github.com/users/tdhock/events{/privacy}","received_events_url":"https://api.github.com/users/tdhock/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2016-07-13T16:01:33Z","updated_at":"2016-07-13T16:01:33Z","closed_at":null,"body":"I got the linear time constrained optimal segmentation C++ code working, for the Poisson Loss and the PeakSeg constraint (changes are up, down, up, down, ...).\r\n\r\nIt should be relatively straightforward to adapt the existing code for the isotonic regression constraint (changes are up, up, up, ...). For the Poisson Loss (non-negative count data) I could write the isotonic regression code pretty much immediately. For other losses it will be more complicated. @guillemr said that he will work on the Normal/square loss in the coming weeks.\r\n\r\n@jpvert with what kind of data did you want to use the regularized isotonic regression model? (normal, poisson, ...?) Also, for your code, what parameterization would be better?\r\n- The FPOP/Optimal Partitioning algorithm takes as input a penalty parameter lambda, and returns one model that minimizes the penalized cost. Probably better if you know what lambda penalty parameter that you want to use.\r\n- The PDPA/Segment Neighborhood algorithm takes as input the maximum number of segments S, and returns optimal models with 1, ..., S segments. Probably better if you don't know what penalty or number of segments is best, and you want to try several different models.\r\n"}}
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