| Key | Value |
|---|---|
| MD5 | 1701BEF1750B6EE41CB7EC6460775D47 |
| 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 | python-scikit-learn |
| PackageRelease | 1.fc21 |
| PackageVersion | 0.15.2 |
| SHA-1 | D7495DFB6A5EA4DBA9ACDE791AC4BBC934FE939E |
| SHA-256 | C72DFDDC03F30E387E8240C4380C76148544409569AEFDE4F4A1CD20F607547A |
| 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/python2.7/site-packages/sklearn/metrics/cluster/bicluster/bicluster_metrics.pyo |
| FileSize | 3786 |
| MD5 | 97BD11157AD0390391029F4EC04A18B1 |
| SHA-1 | 0077B2B3FAA8F998755DDFBE56A3300AAA71CE52 |
| SHA-256 | C8AA28C014DC21351C5C0D32BB8091D1FBBB9EC96C032E3734AA4AE8DE193829 |
| SSDEEP | 96:dgv/Ag1zFqkgROg1CR1qEiRg1Eg1fg1KUF9J30pbsyKQaIh6g1BLtjg1t:dW/AWPeOrR/iRjKFe0/TAQ1Q |
| TLSH | T1EB71606166E50AA7CEE1F1369AF242A78325E4F3510077C5316CE4343B46AB8B23F389 |
| Key | Value |
|---|---|
| FileName | ./usr/lib/python2.7/site-packages/sklearn/decomposition/dict_learning.pyo |
| FileSize | 38109 |
| MD5 | F28F639E924D3CFD67D71563F817C46C |
| SHA-1 | 00D9258EC4FD9E3980B034977567FF16B7D70155 |
| SHA-256 | 07ACCD2E5A6FACA896BD044E59E4B5E47B5CA09E4B408EFE0FE7E353A15AF1A7 |
| SSDEEP | 768:2dba9QZari9yD/ISMnlybRo52NqQu0eXP7m092XY6sKz:9QZ9qw9nl4oUoQLuP3oAk |
| TLSH | T10F03C6903B84032746AB527566F845979AB2F4BBCA93270078ADA1B43FC3534C27F7D6 |
| 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/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/python2.7/site-packages/sklearn/externals/bundled/joblib/format_stack.pyc |
| FileSize | 10646 |
| MD5 | 0C694C8BB26C6CF451D84C03F866717B |
| SHA-1 | 019D4CD9D1FCEF83B5CA9C527B0A5F7D0673E71A |
| SHA-256 | E9F68188C657A1FE3C28816678E30939A14FF5861E2B8EBC28456427021CE756 |
| SSDEEP | 192:dSnURivwTWNbm0U3olHutaUwUZqumCAd8b1fhZ+oTmpqpDIZXvRGWzg/dNjQ1yZg:dXiv6WNU3olOAUwUZquO85D+oaJZfF8y |
| TLSH | T1A3228190E3E40A67C191953560B44307DEA4F5F66183A78277BCF4BA3BC6324C97BB88 |