Key | Value |
---|---|
MD5 | A1737D10EAC4A74D850B7689AD880760 |
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 | 2.fc22 |
PackageVersion | 0.16.0 |
SHA-1 | 36FDC3A429E4156C1F2A20CF6D3B8735B1A5E6F9 |
SHA-256 | 8CEE5A3D32C6F0571163A26BACF635D6266B1BBBABD7FEA73B8718AA595A7B30 |
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/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/gaussian_process/tests/__pycache__/__init__.cpython-34.pyo |
FileSize | 158 |
MD5 | E6A828F1F2FCE71D5BEE24C8F18EB271 |
SHA-1 | 014C777B78A770AFA358515A27C1C81CB970614E |
SHA-256 | AC49F08C4B8F716330954A1042B48F43910B2D8F84CAB475F0F83DB7A6B08ADE |
SSDEEP | 3:Wt2l+leh/kreWWeuKT9YMKDVWrz4AzwWwO1Rzy6BRkcTitn:W8aeh/1M9YLMrkAkWFbWcD6 |
TLSH | T1C7C08C60821B82A2E4EDFD356010420224C4CC70A24ACB636A0851052E483480C22802 |
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/decomposition/__pycache__/nmf.cpython-34.pyo |
FileSize | 17206 |
MD5 | 7FCCF3DDE17A39511D9DCA1F64C64B9C |
SHA-1 | 01CEF5D7D7E283C1DE989927AAE13804482B7332 |
SHA-256 | 13ED573228040AC20E9FDE3613185FD097AC68195F166288EC112EF3E8435141 |
SSDEEP | 384:54knxtMaq8CkQzPjLIAVbxDIG+P4YSYE9uxY088iQ:54knxthq3jLIA5xUG+gYSJ0kQ |
TLSH | T18672A4116FC1466BF192F172A0B91603DBB1E14BAA4317513AEFE5383FD6354862F389 |
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/lib/python3.4/site-packages/sklearn/neighbors/dist_metrics.cpython-34m.so |
FileSize | 204184 |
MD5 | 4D712CFF535DAC961B21774472083959 |
SHA-1 | 0241DEF75488C6C29D9FD7AC8BF849740A1CF95C |
SHA-256 | 5AB73C0EC7481FD8078A4A07E597C5DD61E690B8652366B9C0551131F9ADBE59 |
SSDEEP | 3072:cKF7BFUKNmXQEm6cZ9s6gYxZkh8Oyk2cb+qh50mRXUqX/7o:RF7BnabcZAY68OybcbLh50mRkI/c |
TLSH | T17014F745BA388B25CD951777A87ECA0FAF69D413001B414FDB48D6BF7D9BAC88206F18 |
Key | Value |
---|---|
FileName | ./usr/lib/python3.4/site-packages/sklearn/feature_extraction/__pycache__/dict_vectorizer.cpython-34.pyo |
FileSize | 10510 |
MD5 | 0DA8EA31321D2C78F68D8A8FBE447E41 |
SHA-1 | 025F0793F97CB0551A26270560A41E6D1D8BA2B4 |
SHA-256 | AC15367ACA33153F3389DC81434DFC6660B22F971DEF93D31B684FA539E5A219 |
SSDEEP | 192:1ln2mEc95Ts99kd6Ze7bI5TuPN9j0O61gy9N9zbfG7cT6hqijMbFHu92iG9wq+NR:18e5TphI5aP0O3ypb+7rxo1u9Fq+Nipq |
TLSH | T1EC22B58E7EA0491FE6A3F5F681F90301DF20D2DB9545275A3A9CE07D3FC6629017E24A |