| Key | Value |
|---|---|
| MD5 | 678BA962932B8126200FCF9163BA8CEF |
| PackageArch | ppc64le |
| 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 | D898EC3E51BF1FC42EAEC2303474C0A4557E2958 |
| SHA-256 | 347AB53E4642DAC3B9DBF32283F639BBA41843F5239F8B7B715DAEE4C66634FB |
| 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/lib64/python2.7/site-packages/sklearn/datasets/base.pyo |
| FileSize | 18467 |
| MD5 | 1483B9181BF51DD3611DC89708C2D527 |
| SHA-1 | 00350D33517743EE8AFB7E43321D46D101FF28C2 |
| SHA-256 | 0EA431B9193DB9F8F09CEF5E7D52F59F99A101AD81501907FC3A45F82752138F |
| SSDEEP | 384:d/gZ93yYHGjFFDOMvXl1NMWmkhP6yb3/qYpPkppBlwMU9y48888X:doSFrt1Dx3Cfs |
| TLSH | T1CE8293853B8483BBC6A251B1A4FC4153CA24F5AB6241535038ECE1B42FE5B35E2BF7C9 |
| Key | Value |
|---|---|
| FileName | ./usr/lib64/python2.7/site-packages/sklearn/neural_network/rbm.pyo |
| FileSize | 12546 |
| MD5 | 4E5636BA3586CCEFCEF09ED1828F3EDC |
| SHA-1 | 004525F8CD5AF5755297A428F13BCB22EBEEC93D |
| SHA-256 | E46F4BFB4C87EE461644D6BB9AA719EB450A46A7D6AAA7D0D14E19834D7A2304 |
| SSDEEP | 384:wAmbxqntVkg3VKh2rVfCNpkW3lbGkss88m:3UqntVkusqVfCN+Io |
| TLSH | T1F2428485FEA25B6BC1A281B230F85143CEBAB07B95C1235138DEA5742FC5534E22E7C8 |
| Key | Value |
|---|---|
| FileName | ./usr/lib64/python2.7/site-packages/sklearn/linear_model/tests/test_sparse_coordinate_descent.pyo |
| FileSize | 9614 |
| MD5 | 57A7997EDB488E8CD2CC4BA62007B536 |
| SHA-1 | 008C4B15F251CD12CEE004512B10F7144E1784C0 |
| SHA-256 | AFF9A0CCB76F886AB34C8AC26CB9FAEDC1E9D7E7BB89D78A93DECB1C63FD41BA |
| SSDEEP | 192:2Rf75fVT4Mf9m9NswowsFqfh4/S3afsqYLWof8xyv3XbFflkZWfTyqfp7fROf4Wt:I71VTDA9NswowTW/3JSWY8xSJ1e2prR4 |
| TLSH | T1BE12BD41A3DA8E8BC1741034A9F40307ADA4F1BABE456B4215FCE87D7ED43A5C46F38A |
| 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/metrics/cluster/tests/__init__.pyo |
| FileSize | 163 |
| MD5 | 6669165828DE9D91DA6835FDC67CF447 |
| SHA-1 | 01107C26239F183D6FDDD6CEDB327281B56C69F2 |
| SHA-256 | 6E888887415832E615BD603DAFA6C00E8ADB2E520ABC0512D6CF12C10F18CF34 |
| SSDEEP | 3:Rkmleh/Tj3tNltNltWDKTNIIMmoWrz4AzIXJNe6BRzaiitn:9eh/T48OxmDrkAEXJUcRaF |
| TLSH | T138C08C90A2330296EE345D38AA00422E0AC9CC73A4057A90681C010E184A0AD0A2D6C5 |
| Key | Value |
|---|---|
| FileName | ./usr/lib64/python2.7/site-packages/sklearn/datasets/tests/test_20news.pyo |
| FileSize | 2237 |
| MD5 | ABCEE15FE23EFF44F473C45296446586 |
| SHA-1 | 01177FF5BB2566F596AA6968AAD04BFE88917408 |
| SHA-256 | 4FD6339BEFC6EF5979077BFC643298447BE99B7B8BC8C6822BACC9DA74A2FB4B |
| SSDEEP | 48:fPiDVMi+Y8NGOMm4a9B62dri5Ktg4U62dORXkh62d7s:fkj+PkOMmdw3ZAX0ts |
| TLSH | T1D641DC8263E687CBC4B05574763163139EB1E0FAA64463D203F8F43A3AEC363E44934A |
| 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 |