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The current version, completed April 3, 2011, is MurmurHash3, [12] [13] which yields a 32-bit or 128-bit hash value. When using 128-bits, the x86 and x64 versions do not produce the same values, as the algorithms are optimized for their respective platforms. MurmurHash3 was released alongside SMHasher, a hash function test suite.
Name Length Type Pearson hashing: 8 bits (or more) XOR/table Paul Hsieh's SuperFastHash [1]: 32 bits Buzhash: variable XOR/table Fowler–Noll–Vo hash function
Widely used in many programs, e.g. it is used in Excel 2003 and later versions for the Excel function RAND [8] and it was the default generator in the language Python up to version 2.2. [9] Rule 30: 1983 S. Wolfram [10] Based on cellular automata. Inversive congruential generator (ICG) 1986 J. Eichenauer and J. Lehn [11] Blum Blum Shub: 1986
SipHash computes a 64-bit message authentication code from a variable-length message and 128-bit secret key. It was designed to be efficient even for short inputs, with performance comparable to non-cryptographic hash functions, such as CityHash; [4]: 496 [2] this can be used to prevent denial-of-service attacks against hash tables ("hash flooding"), [5] or to authenticate network packets.
From 2005 to December 2012, Van Rossum worked at Google, where he spent half of his time developing the Python language. At Google, he developed Mondrian, a web-based code review system written in Python and used within the company. He named the software after the Dutch painter Piet Mondrian. [20]
Python 2.6 was released to coincide with Python 3.0, and included some features from that release, as well as a "warnings" mode that highlighted the use of features that were removed in Python 3.0. [ 28 ] [ 10 ] Similarly, Python 2.7 coincided with and included features from Python 3.1, [ 29 ] which was released on June 26, 2009.
In a typical document classification task, the input to the machine learning algorithm (both during learning and classification) is free text. From this, a bag of words (BOW) representation is constructed: the individual tokens are extracted and counted, and each distinct token in the training set defines a feature (independent variable) of each of the documents in both the training and test sets.
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