{"action":"created","issue":{"url":"https://api.github.com/repos/haifengl/smile/issues/381","repository_url":"https://api.github.com/repos/haifengl/smile","labels_url":"https://api.github.com/repos/haifengl/smile/issues/381/labels{/name}","comments_url":"https://api.github.com/repos/haifengl/smile/issues/381/comments","events_url":"https://api.github.com/repos/haifengl/smile/issues/381/events","html_url":"https://github.com/haifengl/smile/issues/381","id":380217469,"node_id":"MDU6SXNzdWUzODAyMTc0Njk=","number":381,"title":"[Issue] Non-typical missing value imputation","user":{"login":"inejc","id":3806837,"node_id":"MDQ6VXNlcjM4MDY4Mzc=","avatar_url":"https://avatars0.githubusercontent.com/u/3806837?v=4","gravatar_id":"","url":"https://api.github.com/users/inejc","html_url":"https://github.com/inejc","followers_url":"https://api.github.com/users/inejc/followers","following_url":"https://api.github.com/users/inejc/following{/other_user}","gists_url":"https://api.github.com/users/inejc/gists{/gist_id}","starred_url":"https://api.github.com/users/inejc/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/inejc/subscriptions","organizations_url":"https://api.github.com/users/inejc/orgs","repos_url":"https://api.github.com/users/inejc/repos","events_url":"https://api.github.com/users/inejc/events{/privacy}","received_events_url":"https://api.github.com/users/inejc/received_events","type":"User","site_admin":false},"labels":[{"id":149807623,"node_id":"MDU6TGFiZWwxNDk4MDc2MjM=","url":"https://api.github.com/repos/haifengl/smile/labels/question","name":"question","color":"cc317c","default":true}],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":2,"created_at":"2018-11-13T13:08:07Z","updated_at":"2018-11-14T03:43:12Z","closed_at":null,"author_association":"NONE","body":"I see that `AverageImputation` estimates row-wise means which are then used to fill the missing values. I believe this is non-typical (e.g. scikit-learn's [implementation](https://github.com/scikit-learn/scikit-learn/blob/bac89c2/sklearn/impute.py#L93) computes column-wise statistics instead). Are there any specific reasons for that implementation?"},"comment":{"url":"https://api.github.com/repos/haifengl/smile/issues/comments/438526974","html_url":"https://github.com/haifengl/smile/issues/381#issuecomment-438526974","issue_url":"https://api.github.com/repos/haifengl/smile/issues/381","id":438526974,"node_id":"MDEyOklzc3VlQ29tbWVudDQzODUyNjk3NA==","user":{"login":"haifengl","id":5502052,"node_id":"MDQ6VXNlcjU1MDIwNTI=","avatar_url":"https://avatars3.githubusercontent.com/u/5502052?v=4","gravatar_id":"","url":"https://api.github.com/users/haifengl","html_url":"https://github.com/haifengl","followers_url":"https://api.github.com/users/haifengl/followers","following_url":"https://api.github.com/users/haifengl/following{/other_user}","gists_url":"https://api.github.com/users/haifengl/gists{/gist_id}","starred_url":"https://api.github.com/users/haifengl/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/haifengl/subscriptions","organizations_url":"https://api.github.com/users/haifengl/orgs","repos_url":"https://api.github.com/users/haifengl/repos","events_url":"https://api.github.com/users/haifengl/events{/privacy}","received_events_url":"https://api.github.com/users/haifengl/received_events","type":"User","site_admin":false},"created_at":"2018-11-14T03:43:12Z","updated_at":"2018-11-14T03:43:12Z","author_association":"OWNER","body":"I mean that each class has its own distribution (e.g. different means in case of Gaussian distribution). Calculating the mean of the mixture of all classes doesn't make much sense. All other algorithms in smile calculates the mean in a small neighborhood of the sample. The difference is how to find the neighbors."}}
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