| Key | Value |
|---|---|
| MD5 | 0B5B19D9ADFC87C2B935B888AECEA5A7 |
| PackageArch | s390x |
| 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 | 3.fc20 |
| PackageVersion | 0.14.1 |
| SHA-1 | FADF2C294EDD6D3D16374617A9DA1CD8A4DBA9F4 |
| SHA-256 | E15C9C9E57B5AA8607BE3DA4C6AA16C5F61E8CA7B5B1C3F78AB81819AEB039A5 |
| hashlookup:children-total | 801 |
| hashlookup:trust | 50 |
The searched file hash includes 801 children files known and seen by metalookup. A sample is included below:
| Key | Value |
|---|---|
| FileName | ./usr/lib64/python2.7/site-packages/sklearn/qda.pyo |
| FileSize | 8441 |
| MD5 | A29B9034F3A68424B866B6B5709BCF81 |
| SHA-1 | 00050BECB8ECA0F94D389DD05505D7E9F6F9E325 |
| SHA-256 | 00D847D62669C8A3F813B55960FCB66FF7FBA1532E58B2414DAB886226343881 |
| SSDEEP | 192:laLv1RYuHWj7yk9yLRfi08R/9CcqKx9ze9I9X7Q5p9U7lxI9G4fO69iifc949EDa:qrYqWLAFfV8xATKxJei1WO78kX6oauDa |
| TLSH | T1C002A645BFA10A6FD9A39176A0F84107DEB4D4B79280631134FFA5362F98279C23F789 |
| Key | Value |
|---|---|
| FileName | ./usr/share/doc/python-sklearn-doc/examples/linear_model/plot_ols_ridge_variance.py |
| FileSize | 2032 |
| MD5 | 44A08C8EAB78FA8522617D143280CE43 |
| SHA-1 | 002D35237E1045669AE711D219C5C7E2C828DF64 |
| SHA-256 | DF23A76B9DA4857633A934C3CDEAC1FB3F1C3C0BFA5266F2CF148F22CC3F7240 |
| SSDEEP | 48:4YOa+3VOPSAN5mAOxGwq0JcrsyPAhpa2b7TyJANfeK6AK/l:4YOTlvsmAOxGwvJdyP7ifmWeK6Ail |
| TLSH | T15841861B62861B73A337942DBDB9329C7351409F79427CA57BFC61085F8172C0EB94B5 |
| 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/examples/exercises/plot_iris_exercise.py |
| FileSize | 1577 |
| MD5 | 3CEE1240FBA2960897069B76B5637772 |
| SHA-1 | 0116272B06B5037C5FC6E48E289CAD5FC1E6CC61 |
| SHA-256 | BE809F9603D572D38F9B2B5C30FDDBC3865711F28480FD46C2EDFED3DB78BC83 |
| SSDEEP | 24:/AX9SV6wq4Vxknvg059WbkKX52BrpH2sCU5tkLtmGvItyG4bxpZNbH/D:IXcVVVqrKX5m8lMtggGPbxfNz/D |
| TLSH | T1F031201A904E337213C790BD82EB29846B5366234B44687A777DB7D1DF02764F239942 |
| Key | Value |
|---|---|
| FileName | ./usr/lib64/python2.7/site-packages/sklearn/tests/test_qda.pyc |
| FileSize | 2932 |
| MD5 | 3934CB4F3F1DB5A644ED1EACDFD6532D |
| SHA-1 | 011B80C58529B903BEAEF6ABAE138A3DF1513A43 |
| SHA-256 | 8104B5F327252E2D879932FCC32F0F24958EF3E33995AF02A70FC8746C38A6EE |
| SSDEEP | 48:MrXSsBrUHCHeGaeM/udRdfK0sWHyMvgXXYaQ+52PHsWJ2s2XbWV214RdCibYJ20w:+5xHeOndRdIWPvgXXbQnPHYs2XnyRdn3 |
| TLSH | T15551DE53A3EA8D1BC0A11138F4B9530BECB1F4BA6E85A7946AFED03939D8344D51B385 |
| Key | Value |
|---|---|
| FileName | ./usr/lib64/python2.7/site-packages/sklearn/manifold/spectral_embedding_.pyo |
| FileSize | 15977 |
| MD5 | C7A0E6A0BD0BEA4B4C15E9AB97D5FBCE |
| SHA-1 | 025A975C0C9E72A0C75A0C50E84289B679110F0F |
| SHA-256 | B6E6523E9237EF3696D2A0AC8DE49B7717FB4408F72C6C52705C15BBD9B5C500 |
| SSDEEP | 384:Fnhogg6t4gD1PoKGtZRy6KWV+nPH8snOH+j1dV9d888b:btg6t469QZs6d8nPH8snc+jk |
| TLSH | T13672C5247F4683AFC1A1A0B2A4F4158BCF79E4B7C883639135DED1791FD2664E22E385 |
| Key | Value |
|---|---|
| FileName | ./usr/share/doc/python-sklearn-doc/examples/plot_rfe_digits.py |
| FileSize | 852 |
| MD5 | 9103650C2F36397AB88DF835B34D38B5 |
| SHA-1 | 02C0A867F5D8E9C34607BCE93EF5ABF7C6F495A9 |
| SHA-256 | 9F282DADA6645094618C0A5BFA1913CC17F1384A2BBEC0B7B7783AB89CBE9928 |
| SSDEEP | 12:ilgJr4Y/8OREMeyAMyfbyvmvA9NZfXALqSkAVL/KO5Oe4N6sYD:iqh4E8HyLyTyOEfXALCAVIGh |
| TLSH | T141011E5D5220B7771DB758B582F5809319F20D3A2341622015A8CA658B82BB6FFF7A43 |
| Key | Value |
|---|---|
| FileName | ./usr/share/doc/python-sklearn-doc/examples/plot_kernel_approximation.py |
| FileSize | 7974 |
| MD5 | 92E29404D8F074E46F35C40F20766AB8 |
| SHA-1 | 02FB35C9AF06F6F6872DFFCAAC0A51570D42B6A2 |
| SHA-256 | BF5D973C48EC576C568FF9ECFB74EA75395EBF29B4D1C19AB82D210B6A4DDCF4 |
| SSDEEP | 96:0X75FrUiJKIiRjQBdLdgooMgFYZygEqTgR/KYIOcITTmpsm51iuPztYi:+75FQiti6KYZkR/KYIOcITq1iuhYi |
| TLSH | T17CF1E80B20E30B3223B7207C23DC21C7BFA49056E9975A3DF99D8654379AF21E276649 |
| Key | Value |
|---|---|
| FileName | ./usr/share/doc/python3-scikit-learn/examples/ensemble/plot_adaboost_multiclass.py |
| FileSize | 3585 |
| MD5 | 34017803563CB9F9B635C002523C75AB |
| SHA-1 | 0322042E7BF79C438AD29D097C0E77A689587F33 |
| SHA-256 | 50E1E9107975866013E5FFB4FA9033867E9BB126CC32959A3E9CC4E3E4204164 |
| SSDEEP | 96:ir9H8frWUXgINfBhroMYbPXwW/K/oKR6m257TwDLj5dlTn2093:DC3iXR6mp1N |
| TLSH | T1F07193258A666A3187B96CFECCAC526D3360144C9D22D009B5FD8F300F0BF19ECBA2D4 |
| Key | Value |
|---|---|
| FileName | ./usr/share/pyshared/sklearn/cluster/spectral.py |
| FileSize | 19089 |
| MD5 | 7F39F2175913371EB02C1FC7AD23163A |
| SHA-1 | 03C4601BEAD359C5B92ED2D0FA173AB399D8C9FE |
| SHA-256 | E4FAABA262F99730C34B8078D83DACC8BF18776F391D8A9657004B8524E70BE8 |
| SSDEEP | 384:M+X/Q0+fEqCC56so+IRrREezf61red3/YV7i/aD6VHnNW5ajWlFYV7wJC1S/a703:M+X/Q0kEqCC56soTPloret/WW/aD6741 |
| TLSH | T17582D739394262379C87E09289FE20A68364058F8F537455B99DC6281F13E7873BEFD5 |