| Key | Value |
|---|---|
| MD5 | CBCDE018121FFA504E917445B28EF091 |
| 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.fc23 |
| PackageVersion | 0.16.1 |
| SHA-1 | 548FC6323226467D21EA89AECAF89425630A7AD5 |
| SHA-256 | 5E55CC130D3045F7511575EBB93B6B09A4DF4E4A4C127079F33E7CEEDEA82F78 |
| 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/lib64/python2.7/site-packages/sklearn/covariance/__init__.pyo |
| FileSize | 1316 |
| MD5 | CAAF225C33CA407B468A1518CA364740 |
| SHA-1 | 010D0F1AF27E8D825E81E2351B041BD35DF16492 |
| SHA-256 | CC6B6C38CCD6E388296DC0FA0E9FE76CBD6CC89664B4BFADB0A509558E3B94C6 |
| SSDEEP | 24:O+3Ir0VRBl+NxyX803sKGLYpVVrvaRizDiTS1op89LckM2uA6O:O+U0VQDvnAjZWyXM2cO |
| TLSH | T19B21E005E77A9BA7A82C0A31F4CCC1974A5836F6CB171653251C567B27C7913C3EA3E4 |
| 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/feature_extraction/tests/__init__.pyo |
| FileSize | 166 |
| MD5 | F0FB17A719BBAE12D128AABBD942824A |
| SHA-1 | 0174F7E78152BC3A4981A9D8A9F9DC993F349284 |
| SHA-256 | CF757124D766979B3BBAD373C140AF0D3F5022D3C7CBEA0123E59BF5D1AC4083 |
| SSDEEP | 3:uXl0leh/Tj3tNltNltWOKTNIIMmoWrz4AzcVAJCE/6BRzaiitn:Olceh/T4OAOxmDrkAlTcRaF |
| TLSH | T1EDC08CC0F27353A2D8BA087AA100021E6A88887368027690790D404F1E980BD0A2E480 |
| 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/datasets/base.pyo |
| FileSize | 18047 |
| MD5 | 0E5E4AC3F9455023CA432FA94288ADD9 |
| SHA-1 | 01997C1676F6CA16D5BFD7ED9C30552D3F7078DB |
| SHA-256 | FB3B2384316C6AC564E53677E8E1C9E533255B99C4302355B56B2CC9AA0D4FED |
| SSDEEP | 384:DkgZaG76RHXjFFDOMvX2NMWmkhe6ib3/6YqfkkR+C8N0z78888C:D9HyFr+DinrbBT |
| TLSH | T1348261853BC087BBC6A291B1A4FC4153CA24F5AB6241535438ECE1B42FD5B25E2BF7C9 |
| 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/lib64/python2.7/site-packages/sklearn/utils/bench.pyo |
| FileSize | 617 |
| MD5 | 26EE37E7E7B86F27DD5833BD3A845FF5 |
| SHA-1 | 027773EE8DE6414DCC40983AD749848D91B26436 |
| SHA-256 | F12C9946926FC11A8E3E6DB7ADC49473DD2DDD4606449C7BFD2F21651A1F1215 |
| SSDEEP | 12:Olce6XvpVgX+yhvluKCoVC293IbvJh/YEZkKzIw40HK89+SkfxcTEllg54w8fxcC:OQ/p2+Cuno43bvPge60h9PA2T5BI217w |
| TLSH | T1F8F0A28CA2211FA6D6BB15B5B126312627F5913B3306375368B8C5DB1F9C3A9412F51C |