{"action":"created","issue":{"url":"https://api.github.com/repos/NeuroDataDesign/py-U-Rerf/issues/3","repository_url":"https://api.github.com/repos/NeuroDataDesign/py-U-Rerf","labels_url":"https://api.github.com/repos/NeuroDataDesign/py-U-Rerf/issues/3/labels{/name}","comments_url":"https://api.github.com/repos/NeuroDataDesign/py-U-Rerf/issues/3/comments","events_url":"https://api.github.com/repos/NeuroDataDesign/py-U-Rerf/issues/3/events","html_url":"https://github.com/NeuroDataDesign/py-U-Rerf/issues/3","id":361086218,"node_id":"MDU6SXNzdWUzNjEwODYyMTg=","number":3,"title":"Use graph output from ndgm on Healthy Brain Network MRI data to perform feature learning with Agglomerative Clustering and Gaussian Mixture Models.","user":{"login":"GaneshArvapalli","id":16581543,"node_id":"MDQ6VXNlcjE2NTgxNTQz","avatar_url":"https://avatars3.githubusercontent.com/u/16581543?v=4","gravatar_id":"","url":"https://api.github.com/users/GaneshArvapalli","html_url":"https://github.com/GaneshArvapalli","followers_url":"https://api.github.com/users/GaneshArvapalli/followers","following_url":"https://api.github.com/users/GaneshArvapalli/following{/other_user}","gists_url":"https://api.github.com/users/GaneshArvapalli/gists{/gist_id}","starred_url":"https://api.github.com/users/GaneshArvapalli/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/GaneshArvapalli/subscriptions","organizations_url":"https://api.github.com/users/GaneshArvapalli/orgs","repos_url":"https://api.github.com/users/GaneshArvapalli/repos","events_url":"https://api.github.com/users/GaneshArvapalli/events{/privacy}","received_events_url":"https://api.github.com/users/GaneshArvapalli/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":{"login":"GaneshArvapalli","id":16581543,"node_id":"MDQ6VXNlcjE2NTgxNTQz","avatar_url":"https://avatars3.githubusercontent.com/u/16581543?v=4","gravatar_id":"","url":"https://api.github.com/users/GaneshArvapalli","html_url":"https://github.com/GaneshArvapalli","followers_url":"https://api.github.com/users/GaneshArvapalli/followers","following_url":"https://api.github.com/users/GaneshArvapalli/following{/other_user}","gists_url":"https://api.github.com/users/GaneshArvapalli/gists{/gist_id}","starred_url":"https://api.github.com/users/GaneshArvapalli/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/GaneshArvapalli/subscriptions","organizations_url":"https://api.github.com/users/GaneshArvapalli/orgs","repos_url":"https://api.github.com/users/GaneshArvapalli/repos","events_url":"https://api.github.com/users/GaneshArvapalli/events{/privacy}","received_events_url":"https://api.github.com/users/GaneshArvapalli/received_events","type":"User","site_admin":false},"assignees":[{"login":"GaneshArvapalli","id":16581543,"node_id":"MDQ6VXNlcjE2NTgxNTQz","avatar_url":"https://avatars3.githubusercontent.com/u/16581543?v=4","gravatar_id":"","url":"https://api.github.com/users/GaneshArvapalli","html_url":"https://github.com/GaneshArvapalli","followers_url":"https://api.github.com/users/GaneshArvapalli/followers","following_url":"https://api.github.com/users/GaneshArvapalli/following{/other_user}","gists_url":"https://api.github.com/users/GaneshArvapalli/gists{/gist_id}","starred_url":"https://api.github.com/users/GaneshArvapalli/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/GaneshArvapalli/subscriptions","organizations_url":"https://api.github.com/users/GaneshArvapalli/orgs","repos_url":"https://api.github.com/users/GaneshArvapalli/repos","events_url":"https://api.github.com/users/GaneshArvapalli/events{/privacy}","received_events_url":"https://api.github.com/users/GaneshArvapalli/received_events","type":"User","site_admin":false}],"milestone":null,"comments":0,"created_at":"2018-09-17T23:56:47Z","updated_at":"2018-09-21T01:13:18Z","closed_at":null,"author_association":"NONE","body":"Description: \r\n\r\nUse graph output from ndgm on Healthy Brain Network MRI data to perform feature learning with various deep learning techniques.\r\n\r\n**DoD**:\r\n- Implementation of GMM and Agglomerative Clustering to reduce graph into representative features for clustering\r\n- Usage of at least one other sklearn clustering algorithm to determine representative features (t.b.d. based on team choices)"},"comment":{"url":"https://api.github.com/repos/NeuroDataDesign/py-U-Rerf/issues/comments/423382504","html_url":"https://github.com/NeuroDataDesign/py-U-Rerf/issues/3#issuecomment-423382504","issue_url":"https://api.github.com/repos/NeuroDataDesign/py-U-Rerf/issues/3","id":423382504,"node_id":"MDEyOklzc3VlQ29tbWVudDQyMzM4MjUwNA==","user":{"login":"GaneshArvapalli","id":16581543,"node_id":"MDQ6VXNlcjE2NTgxNTQz","avatar_url":"https://avatars3.githubusercontent.com/u/16581543?v=4","gravatar_id":"","url":"https://api.github.com/users/GaneshArvapalli","html_url":"https://github.com/GaneshArvapalli","followers_url":"https://api.github.com/users/GaneshArvapalli/followers","following_url":"https://api.github.com/users/GaneshArvapalli/following{/other_user}","gists_url":"https://api.github.com/users/GaneshArvapalli/gists{/gist_id}","starred_url":"https://api.github.com/users/GaneshArvapalli/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/GaneshArvapalli/subscriptions","organizations_url":"https://api.github.com/users/GaneshArvapalli/orgs","repos_url":"https://api.github.com/users/GaneshArvapalli/repos","events_url":"https://api.github.com/users/GaneshArvapalli/events{/privacy}","received_events_url":"https://api.github.com/users/GaneshArvapalli/received_events","type":"User","site_admin":false},"created_at":"2018-09-21T01:13:18Z","updated_at":"2018-09-21T01:13:18Z","author_association":"NONE","body":"Pseudocode:\r\n\r\nGMM (sklearn GaussianMixture):\r\n```\r\nimport edgelists from 'http://neurodatadesign.s3-website-us-east-1.amazonaws.com/' as E\r\n\r\nnum_samples = size(E)\r\nnum_features = 10    // T.B.D. based on number of graph statistics calculated\r\nX = new matrix(num_samples, num_features)\r\n\r\n// Generating data\r\ncount = 0\r\nfor i in E:\r\n    g = make_graph(i)\r\n    stats = graph_statistics(g)\r\n    X[count, :] = stats\r\n    count++\r\n\r\n// Clustering\r\nnum_components = 5 // T.B.D. based on performance\r\ngmm = mixture.GaussianMixture(n_components=num_components)\r\ngmm.fit(X)\r\n\r\n// Plotting\r\nreduced = pca(n_components=2).fit_transform(X)\r\n\r\nplt.plot(reduced[:, 0], reduced[:, 1])\r\nplt.show()\r\n```\r\n\r\nAgglomerative Clustering:\r\n```\r\nimport edgelists from 'http://neurodatadesign.s3-website-us-east-1.amazonaws.com/' as E\r\n\r\nnum_samples = size(E)\r\nnum_features = 10    // T.B.D. based on number of graph statistics calculated\r\nX = new matrix(num_samples, num_features)\r\n\r\n// Generating data\r\ncount = 0\r\nfor i in E:\r\n    g = make_graph(i)\r\n    stats = graph_statistics(g)\r\n    X[count, :] = stats\r\n    count++\r\n\r\n// Clustering\r\nnum_components = 5 // T.B.D. based on performance\r\nagg = cluster.AgglomerativeClustering(n_components=num_components)\r\ngmm.fit(X)\r\n\r\n// Plotting\r\nreduced = pca(n_components=2).fit_transform(X)\r\n\r\nplt.plot(reduced[:, 0], reduced[:, 1])\r\nplt.show()\r\n```"}}
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