| Key | Value |
|---|---|
| MD5 | F893B4A16569BEF48801B5B05537D3A3 |
| 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 | 1.fc21 |
| PackageVersion | 0.15.2 |
| SHA-1 | 8C4384E799329BC997F3CC8E321C245567459960 |
| SHA-256 | 4DA8081B9463672716205548A3E0F9A00C124DF7A39A139A9F885DB9BD0DA9B7 |
| hashlookup:children-total | 906 |
| hashlookup:trust | 50 |
The searched file hash includes 906 children files known and seen by metalookup. A sample is included below:
| Key | Value |
|---|---|
| FileName | ./usr/lib64/python3.4/site-packages/sklearn/ensemble/tests/test_forest.py |
| FileSize | 17601 |
| MD5 | 856C9EB5C6357A8E978BB27ED478B14D |
| SHA-1 | 0001980ED073FFA12C4D97EE33F9FC4D4A9FF043 |
| SHA-256 | 95CDF4DE2328FC18906E92054FE52629B8B6B99CEF8992750C9EA14D9533FA72 |
| SSDEEP | 384:RmH3A2etKtuw8ixVT17yl8iMX5nITJojVpKv+66wLoVE/wpL+:RmH3A/tKtuw9xVT17yl8NX5nITJojVpu |
| TLSH | T18482D703F8960D595B53297E24DE510827956B1B860818753EFFD0086F9462CB3FBBBE |
| Key | Value |
|---|---|
| FileName | ./usr/lib64/python3.4/site-packages/sklearn/linear_model/ransac.py |
| FileSize | 13952 |
| MD5 | 67B38A5B19534C15626BEDDA27CE70D0 |
| SHA-1 | 000C0BD44626C1E94A98B9CB8615101BC35C180F |
| SHA-256 | 526176561F560882ECAE0B67F451191EBFC36F7E9228B327F61452F553983492 |
| SSDEEP | 192:1axKzOGnKFnGWjPfIAeMl0Bgox2WGZHO6qWKNRKES6dIhBNRERZCoNbk7A:oK68KRTfIAXkaHRKS6aBsRZhb7 |
| TLSH | T17F52940568203B374A87B5B068DE010BC77918A79686A4757CFCC3AD1F6297873ADBD8 |
| 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/share/doc/python3-scikit-learn/examples/cluster/plot_cluster_comparison.py |
| FileSize | 4865 |
| MD5 | 919283D95801BDB1582E6768ADC62A65 |
| SHA-1 | 0145D31CB950A8CC679300AF4CF93EC48DE5D612 |
| SHA-256 | 64C861D3DC5FE9F11F44F2AA5A86FF15BEB60B26B7974895663F37DC729039C3 |
| SSDEEP | 96:hLrD8Hd/MIsALpqtjAFejIHXSNIuGytASwTgSNexmDDz4bW:h4nVBgZ/6tLQW |
| TLSH | T176A1857167126117EF93B09A4EB751E837946057075028AAB52CC3254F0BB3CB3F2B9B |
| Key | Value |
|---|---|
| FileName | ./usr/share/doc/python3-scikit-learn/examples/exercises/plot_cv_digits.py |
| FileSize | 1207 |
| MD5 | C21A69A2BC54F263E69035C048095865 |
| SHA-1 | 016D65381370139D98DCC375AACCF083CD195B82 |
| SHA-256 | 657225AA5357703DBAC9E250E5690997774CA4C566BC32D257203E93FCAB5E17 |
| SSDEEP | 24:akV7BmSxOgUWqNqag5YEA5BRklGiVQ+zAsyPs1J:akaSVUNJEMBRkbuWyOJ |
| TLSH | T1B621DC0CBAA6B2780B9284B4FC44507137E393106708683E78ABDC6D5646F372B61CB2 |
| Key | Value |
|---|---|
| FileName | ./usr/lib64/python2.7/site-packages/sklearn/manifold/locally_linear.pyo |
| FileSize | 21863 |
| MD5 | 91757DCF694FAA02771CCB2B96A6D4B1 |
| SHA-1 | 0170BFA66B1E1597E7334F62899FE84D7AE71C34 |
| SHA-256 | A4909F64E71BC50AC47A4D89A4E762E5109A13845048454E17FACA79EDC6B71E |
| SSDEEP | 384:iUlMUhLDXzSPM/iVcAIWAflCq2/hTG7g8s2+8KeASs2ueOm+AK7AlRkfsk2Jk:iUlJtDXG0/i6JpflCP/hS7gg+8JK2uo+ |
| TLSH | T1E5A2E8256F86665A9861E232B8F00217DFB4F677A5822B4275EDD1383FC1365D36E3C0 |
| 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/metrics/cluster/tests/test_supervised.pyo |
| FileSize | 8394 |
| MD5 | 97EF8A4E06A78F2B2C5A89C0A41D7785 |
| SHA-1 | 01D17B7A13B8E99CB6643D3178BB595F86FFE122 |
| SHA-256 | 24B2746DD30802721EEBC4931772DAC6D0100892133AF1EE879CCA8988E1E029 |
| SSDEEP | 192:zj/NNftgM8m49hMecYu8iF7E+2uKdyKS13DQ8888888SwG:zBFy9aelu8yEduXKWE8888888SwG |
| TLSH | T19702EE91A3E24F8BC4B411B9F9F04323ED95F9767E107741196ED03C2BC876AA52A3C6 |
| Key | Value |
|---|---|
| FileName | ./usr/lib64/python2.7/site-packages/sklearn/utils/sparsetools/setup.pyo |
| FileSize | 1001 |
| MD5 | 2590FC9826A8A9045E42054899E1A886 |
| SHA-1 | 020FD14B145F2891357EA78603DE455AE3DF0CB9 |
| SHA-256 | 518D7F2A0B86634A546305A3D47AD52E97AE8F9EBA3994678FE12F6CA3A1B237 |
| SSDEEP | 24:4L1ttm6UklmA3orTm6ZiM69lrtM23KkmiMvz2Y7k:4L1gkSZBar62aFB72gk |
| TLSH | T1C011E181D3FD4F97C4B20678906881634EE8F97B9E819B846664FC593CCC7B6832714D |