| Key | Value |
|---|---|
| MD5 | 7C0C19A3357E4BF8C6478692F36F2B69 |
| PackageArch | ppc64 |
| PackageDescription | Scikit-learn integrates machine learning algorithms in the tightly-knit scientific Python world, building upon numpy, scipy, and matplotlib. As a machine-learning module, it provides versatile tools for data mining and analysis in any field of science and engineering. It strives to be simple and efficient, accessible to everybody, and reusable in various contexts. |
| PackageMaintainer | Fedora Project |
| PackageName | python-scikit-learn |
| PackageRelease | 2.fc22 |
| PackageVersion | 0.16.0 |
| SHA-1 | 5D355EC39F7FA485A66589A10A3223FEFAD174D9 |
| SHA-256 | 9EE117D9E1AA84C756CB7A1BE823E042EDE2C44261D0AE2A6B6F9A77ED41AC7A |
| hashlookup:children-total | 944 |
| hashlookup:trust | 50 |
The searched file hash includes 944 children files known and seen by metalookup. A sample is included below:
| Key | Value |
|---|---|
| FileName | ./usr/lib64/python3.4/site-packages/sklearn/cluster/hierarchical.py |
| FileSize | 40213 |
| MD5 | 8445D44F4F9E4D4112DCC21A36D037F7 |
| SHA-1 | 000C3C854269C2922BE1C06595F3E880851D30D3 |
| SHA-256 | E9F414A812CF50C6FC8061E0C0D971798F41FA8326ABEF670BFE139617C27AB2 |
| SSDEEP | 768:b6CfhXuUcGwQogk2J3If3V4Spty5kccGwfoPr22J4HDGV4r2o2KKkPkGwkelh7pD:b64eUPzIf3V4Spty5kcP1rqHDGV4r2o2 |
| TLSH | T19B03B722660423715B8790924E7F91A7E34044DF9F5320793DAD92686F12B68F2FFBC9 |
| Key | Value |
|---|---|
| FileName | ./usr/lib/python3/dist-packages/sklearn/utils/sparsetools/_graph_validation.py |
| FileSize | 2407 |
| MD5 | 6CCA3A2DFA57FF6AF3CF3A27AE22F209 |
| SHA-1 | 01070C25205C477A297A7CCE48DA78871F64DD2C |
| SHA-256 | 298C9425EE8888DD03C6A32021051C1ACE1D8C45775B277F0095589690515DD8 |
| SSDEEP | 48:PLdf167rziXSwtpF8AyEv9iVfkZY2MiV8K2pq:DL6fep8AJYVfkZLFKtpq |
| TLSH | T1FE41FE25932D0564D16380E48C83A70E1AD8F6073F67242DF4EEBC682F3861C63257BD |
| Key | Value |
|---|---|
| FileName | ./usr/share/doc/python-sklearn-doc/stable/_downloads/plot_polynomial_interpolation.py |
| FileSize | 1895 |
| MD5 | A4CC2943F64D2730EF80B9504C583D19 |
| SHA-1 | 011BDEF5443BE65B5EC29C9D37FCEEC7206429FA |
| SHA-256 | 2B12D9E9919C21B4BFF58007AB9F645B717AE7749E79099AFBB8B253B5A3ABFC |
| SSDEEP | 48:3b/2fr4glFa11YCuArC18AlcCxaD+1sozVGsA9MGNr:z0lAO18gcCE+BgPr |
| TLSH | T15541B9092E55E82107364074B6F898616E19046EAE8305663DCDBE301B42B0F3D3BF47 |
| Key | Value |
|---|---|
| FileName | ./usr/lib64/python3.4/site-packages/sklearn/neighbors/nearest_centroid.py |
| FileSize | 7219 |
| MD5 | 3F05ED5457FCF69C422C4EB5F5D86CA4 |
| SHA-1 | 0164D287CCF459DEB314C9B84916163D69BEEE13 |
| SHA-256 | 0A2CAF710C753B10E6D10F95CF1D2E91CD430CCF9CF37EB0B95A300A138040D6 |
| SSDEEP | 192:zLmCsu7Ej7Mvse9Du2izWTOgxMW2MB7lxXB:/maELeZu2iSO+32MB71 |
| TLSH | T1D5E1B5166B061B3AC787C46396CD495BB746863B9364182E3CFD52642F0142CA3FFDD9 |
| Key | Value |
|---|---|
| FileName | ./usr/lib64/python2.7/site-packages/sklearn/svm/bounds.pyo |
| FileSize | 3161 |
| MD5 | E99D3ECF70F423EEB16F5A934DFBB361 |
| SHA-1 | 017CA997D4D9CB1C637532F8CC7AA1AA64391CCE |
| SHA-256 | 4A9D309926CCD42A9E5B672C32BF2AD3C75EB302C8CC10E3B4A1AFD78DBA3873 |
| SSDEEP | 48:GA7+Uo4mgAqVCSyTvz40DXVTUVBl4osB9/3bEOtcJmGMRGkV/bJK7fbR162HXUGt:v7J/mzqVWfLVIIT9/bvk4V/ts3HkQx |
| TLSH | T1D15174866EA58DAF8DA741F661744303DF5C90FB9252271135EE921D3FCA432A32F2C9 |
| Key | Value |
|---|---|
| FileName | ./usr/share/doc/python-sklearn-doc/stable/_downloads/plot_swissroll.py |
| FileSize | 1446 |
| MD5 | 6C764C92907310B7717595E840304798 |
| SHA-1 | 0193A74906128D26183FB66001B61CA5D447B865 |
| SHA-256 | 5D7791C51D76DD46308EE5B4B799509831C1EEDC2D767C39B78E7E98A39B066D |
| SSDEEP | 24:x2RAnm7PXQ2KQsFe3M/MDyC5NJYTC4aeujm5tSU3+LVJU3+PbYY1+BZjs:xEAn12KED3JLe0atSfJNYYqs |
| TLSH | T116313F1C2E07B27697A2F0E83E6417DDEB515A009F2044F8B83D68F45381B7CB82D51B |
| Key | Value |
|---|---|
| FileName | ./usr/lib64/python2.7/site-packages/sklearn/utils/stats.pyo |
| FileSize | 2059 |
| MD5 | 3EA8C859866B4B9E48239D219CF231A9 |
| SHA-1 | 019DD4A0C9FA10392DDB3E14D31622C678F4528F |
| SHA-256 | 707B8143F53A8D1561B39A9C41BAC3A9B238EF149175003C697DCA36A0268D38 |
| SSDEEP | 24:aruJ5xiRAnOQFMLH1F2i81giiuDXtVofJF97hFqnfIE2rC8EOvyyeqR1VA9nWAjQ:ari5x8PX9g79VozVXFE29qyUq2IU6R2M |
| TLSH | T14D416308BAD6067BD8F483BA61F18673CFD5D43791412B52305CA0AD2FA4E3AA17F254 |
| Key | Value |
|---|---|
| FileName | ./usr/lib64/python3.4/site-packages/sklearn/linear_model/tests/test_sgd.py |
| FileSize | 44051 |
| MD5 | 2A30219204D8A3FDB17710890DF5C022 |
| SHA-1 | 022C4A0D46C8BE6450F86A4F5A534285740DA850 |
| SHA-256 | 80967239521C8F49941B2FE639AAAF0FE5F6BEBFC82FDEBF338E49F746C83D9F |
| SSDEEP | 768:gqgGbCeWez6QENuvvCQw0pRHuvt1zvOHy/3M/APJYuzxja2ngbCe+DCihHF02woS:PueWe2zNunCQwIRHulV0j4Z0Ho4ECGFI |
| TLSH | T17213C86501731F275347443A88BB874F6A066E334D85186DB4BD860CAF8A179B3EFDB8 |
| Key | Value |
|---|---|
| FileName | ./usr/share/doc/python-sklearn-doc/stable/_downloads/plot_cv_diabetes.py |
| FileSize | 2527 |
| MD5 | E72038F327EB1E9BE42A89248FDADDEA |
| SHA-1 | 02B9832007B41F9CEDC0ADF47E8E74156A7D2F20 |
| SHA-256 | 5C3E8CB59B8C3592B195A2372E1213A62FD9B27EB7664E8C51CF116BE2953E78 |
| SSDEEP | 48:8RSBEU7vtHh44QdkCq1axlZFkmoKCQv73ZIpMm0MNLp:8KLQyCqk7kmzCQTXhMj |
| TLSH | T1C751740DF1436B711F4A10F4B58990A03BA2927A6D1B7935786DCB5C9786FF20B324BA |
| Key | Value |
|---|---|
| FileName | ./usr/share/doc/python-sklearn-doc/stable/_downloads/plot_outlier_detection.py |
| FileSize | 3891 |
| MD5 | 0599DF8A835FC2666792692E44AD35F5 |
| SHA-1 | 02FC1B7CB98D534744F594BBACF8A6B3542A80BF |
| SHA-256 | EEC712403F6EC854DE21503F362884880CBDFD6A8894E6BC2ABE25303FE6AEAE |
| SSDEEP | 48:tmhvpugCQ2qaWBCWMLwJFCtJTjMGK5ReTdbzuKZGUwGpf6a/wmJBaMVz:tkhbFernwJsJTjw0RSKUUw86CBaMVz |
| TLSH | T1E581A5A331412737175798F5C1AE848A23334037AE9A7D55353CC7740F1637D623BA66 |