| Key | Value |
|---|---|
| MD5 | 140CCA151C8861186C868ECF5E1A6382 |
| PackageArch | s390 |
| 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 | python3-scikit-learn |
| PackageRelease | 1.fc23 |
| PackageVersion | 0.16.1 |
| SHA-1 | 9DCFA61C2C9863B294E5A40AC33453EFBD41C77C |
| SHA-256 | 06AD5AFF3BBD76A70D7B0A91BC82A51BD25786F362C6544044B108F11A7AED80 |
| 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/python3.4/site-packages/sklearn/feature_extraction/__pycache__/__init__.cpython-34.pyo |
| FileSize | 615 |
| MD5 | 931BC5DFA33079ED7CD96E9A58D0A029 |
| SHA-1 | 005348B3BDBEA5DB0BC86FC23812C18D5888D579 |
| SHA-256 | F35E7651D886C831318F74C9EFBA3F8463EF2F4B7883F002AC6E169DE75B579C |
| SSDEEP | 12:oUligTk8zQayfrFi8T1UK1/yMZNbNUC/oDjeydHWUWoGEDFDe/u/f:bY6LSFhhU03ZNbNTotSoGCeq |
| TLSH | T11AF0D84E41BAE211E8EAF37A9112022829ACB8DAB62F83046F4CB0A63FE16850016800 |
| 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/lib/python3.4/site-packages/sklearn/neighbors/__pycache__/unsupervised.cpython-34.pyo |
| FileSize | 4595 |
| MD5 | C101D1869C89617A90625843DDD281D9 |
| SHA-1 | 0158040C05021961BE457CF32B12E4B7D495530D |
| SHA-256 | 33E0495B2D880332BB6F9145E69214153C61CD1DB5EB181D710945CB140D844E |
| SSDEEP | 96:tMr48UCjCqzGQmgeSQsfgzjkFIslGL9lG6Gkh/C+iZPLICaBWyrHNNpgpMHpRb9M:yrt9zkzlm6vi0t7gpMHpRbV+l9wDbEHV |
| TLSH | T1C791C6716F454366F6A2E2B7CCA89209CAF5DD13BE7128293CFC45642F4984463BE9CC |
| 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/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/lib/python3.4/site-packages/sklearn/__check_build/__pycache__/__init__.cpython-34.pyo |
| FileSize | 1701 |
| MD5 | 02C7AEACE39510D5A71A9C6BEFB83A9B |
| SHA-1 | 01E3B501C5E00FE85749253A4CC1BC28A573B192 |
| SHA-256 | A51ED13A8D25C3CE1157B9432FB7C68D01D44200656CB944BA3A6090D3FADA27 |
| SSDEEP | 24:Svv9+Go/D6oJFzqoj2DKDJJQZlA53AFH+cCB8etJrAC1JRwPC+:EVcD6oJFzqoKmDCli6HUB8sEC1JRqt |
| TLSH | T1C8313FAF97805232D010F37E603F235DE971C4BCF306A3225AAC662C2BF671A4A73581 |
| 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 |