{"id":17030564,"url":"https://github.com/vsoch/fvplay","last_synced_at":"2025-03-22T20:28:05.809Z","repository":{"id":2623828,"uuid":"3608799","full_name":"vsoch/fvplay","owner":"vsoch","description":"MATLAB scripts is intended for visualizing and preparing raw data  for use with a supervised classifier","archived":false,"fork":false,"pushed_at":"2012-03-10T18:10:02.000Z","size":100,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-01-28T00:29:53.121Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Matlab","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/vsoch.png","metadata":{"files":{"readme":"README","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2012-03-03T05:25:06.000Z","updated_at":"2018-12-20T16:33:29.000Z","dependencies_parsed_at":"2022-08-29T12:50:28.035Z","dependency_job_id":null,"html_url":"https://github.com/vsoch/fvplay","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vsoch%2Ffvplay","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vsoch%2Ffvplay/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vsoch%2Ffvplay/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vsoch%2Ffvplay/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/vsoch","download_url":"https://codeload.github.com/vsoch/fvplay/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245017882,"owners_count":20547889,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-14T08:07:32.328Z","updated_at":"2025-03-22T20:28:05.785Z","avatar_url":"https://github.com/vsoch.png","language":"Matlab","funding_links":[],"categories":[],"sub_categories":[],"readme":"fvplay: Feature Vector Play\n\nThis set of MATLAB scripts is intended for visualizing and preparing raw data \nfor use with an SVM classifier.  As is commonly known, the features that are used \nin the classifier are more important than the method itself, thus these tools \nprovide an easy way to visualize and interpret data for best feature creation.  \nData is expected to be an n x m matrix of doubles with two classes.\nPackage includes:\n\t\t\tfvprep\n\t\t\tfvplay\n\t\t\tfvplot\n\n\n%fvprep: Prepares data objects for use with rest of package. %%%%%%%%%%\n\nUSAGE: fvprep(rawdata,labels)\nINPUT: rawdata: an n x m matrix of doubles, with n objects / subjects \n\t\teach with m features\n       labels: a single column vector of size n x 1 with limited to values of\n\t-1 and 1 to distinguish the two classes\nOUTPUT: fv.features_vectorsraw data\n\tfv.index1 - indices in inputdata for label 1 (1)\n\tfv.index2 - indices in inputdata for label 2 (-1)\n        fv.m      - all means of features (columns)\n        fv.u1, fv.s1, fv.v1: svd output for group 1\n\tfv.u2, fv.s2, fv.v2: svd output for group 2\n\n\n% fvplay takes as input an fv data object prepared by fvprep, and %%%\n allows the user to select a transform to plot (to explore the data)\n fvplot returns the data, and also displays plot in figures 1 and 2\n\n USAGE: fvplot(fvobject,type)\n INPUT: fvobject: data object prepared by fvprep\n\t type: options include:\n\t\t\tquad: quadratic transformation\n\t\t\texp: exponential transformation\n\t\t\tsig: sigmoid transformation\n\t\t\tlog: logarithmic transformation\n OUTPUT: data: transformed n x m data matrix with n subjects (rows)\n\t\tand m features (columns)\n\n\n% fvplot takes as input the output of fvplay (an n x m matrix with n %% objects / subjects (rows) and m features (columns) and plots a mean\n subtracted histogram and heat map.\n\n USAGE: fvplot(fvobject,type)\n INPUT: transform: transformed n x m data matrix, with n objects and m features\n         fv: fv data object\n OUTPUT: graphical... plots!\n\n\n% fvcorr takes as input a data matrix and associated labels (in format -1 and 1)\n and calculates the cross correlation matrix.\n USAGE: fvcorr(data, labels)\n INPUT: \n\t data: a n x M matrix with n subjects, m columns of features\n    labels: a n X 1 column of labels, -1 and 1\n OUTPUT: ccdata: cross correlation matrix\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvsoch%2Ffvplay","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvsoch%2Ffvplay","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvsoch%2Ffvplay/lists"}