Result for 010D0F1AF27E8D825E81E2351B041BD35DF16492

Query result

Key Value
FileName./usr/lib64/python2.7/site-packages/sklearn/covariance/__init__.pyo
FileSize1316
MD5CAAF225C33CA407B468A1518CA364740
SHA-1010D0F1AF27E8D825E81E2351B041BD35DF16492
SHA-256CC6B6C38CCD6E388296DC0FA0E9FE76CBD6CC89664B4BFADB0A509558E3B94C6
SSDEEP24:O+3Ir0VRBl+NxyX803sKGLYpVVrvaRizDiTS1op89LckM2uA6O:O+U0VQDvnAjZWyXM2cO
TLSHT19B21E005E77A9BA7A82C0A31F4CCC1974A5836F6CB171653251C567B27C7913C3EA3E4
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD5CBCDE018121FFA504E917445B28EF091
PackageArchppc64
PackageDescriptionScikit-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.
PackageMaintainerFedora Project
PackageNamepython-scikit-learn
PackageRelease1.fc23
PackageVersion0.16.1
SHA-1548FC6323226467D21EA89AECAF89425630A7AD5
SHA-2565E55CC130D3045F7511575EBB93B6B09A4DF4E4A4C127079F33E7CEEDEA82F78