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In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space-efficient way of vectorizing features, i.e. turning arbitrary features into indices in a vector or matrix. [1] [2] It works by applying a hash function to the features and using their hash values as indices directly (after ...
A hash variable is typically marked by a % sigil, to visually distinguish it from scalar, array, and other data types, and to define its behaviour towards iteration. A hash literal is a key-value list, with the preferred form using Perl's => token, which makes the key-value association clearer:
The basic idea behind a hash table is that accessing an element of an array via its index is a simple, constant-time operation. Therefore, the average overhead of an operation for a hash table is only the computation of the key's hash, combined with accessing the corresponding bucket within the array.
Due to their usefulness, they were later included in several other implementations of the C++ Standard Library (e.g., the GNU Compiler Collection's (GCC) libstdc++ [2] and the Visual C++ (MSVC) standard library). The hash_* class templates were proposed into C++ Technical Report 1 (C++ TR1) and were accepted under names unordered_*. [3]
The following list contains syntax examples of how a range of element of an array can be accessed. In the following table: first – the index of the first element in the slice; last – the index of the last element in the slice; end – one more than the index of last element in the slice; len – the length of the slice (= end - first)
hash MD4: 128 bits hash MD5: 128 bits Merkle–Damgård construction: MD6: up to 512 bits Merkle tree NLFSR (it is also a keyed hash function) RadioGatún: arbitrary ideal mangling function RIPEMD: 128 bits hash RIPEMD-128: 128 bits hash RIPEMD-160: 160 bits hash RIPEMD-256: 256 bits hash RIPEMD-320: 320 bits hash SHA-1: 160 bits Merkle ...
Linear probing is a component of open addressing schemes for using a hash table to solve the dictionary problem.In the dictionary problem, a data structure should maintain a collection of key–value pairs subject to operations that insert or delete pairs from the collection or that search for the value associated with a given key.
The FNV-1 hash algorithm is as follows: [9] [10] algorithm fnv-1 is hash := FNV_offset_basis for each byte_of_data to be hashed do hash := hash × FNV_prime hash := hash XOR byte_of_data return hash. In the above pseudocode, all variables are unsigned integers. All variables, except for byte_of_data, have the same number of bits as the FNV hash.