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In bitwise tries, keys are treated as bit-sequence of some binary representation and each node with its child-branches represents the value of a sub-sequence of this bit-sequence to form a binary tree (the sub-sequence contains only one bit) or n-ary tree (the sub-sequence contains multiple bits).
A notable variant is the bitwise trie, which uses individual bits from fixed-length binary data (such as integers or memory addresses) as keys. History, etymology, and pronunciation [ edit ]
An x-fast trie containing the integers 1 (001 2), 4 (100 2) and 5 (101 2). Blue edges indicate descendant pointers. An x-fast trie is a bitwise trie: a binary tree where each subtree stores values whose binary representations start with a common prefix. Each internal node is labeled with the common prefix of the values in its subtree and ...
A bitwise operation operates on one or more bit patterns or binary numerals at the level of their individual bits.It is a fast, primitive action directly supported by the central processing unit (CPU), and is used to manipulate values for comparisons and calculations.
Bitwise AND of 4-bit integers. A bitwise AND is a binary operation that takes two equal-length binary representations and performs the logical AND operation on each pair of the corresponding bits. Thus, if both bits in the compared position are 1, the bit in the resulting binary representation is 1 (1 × 1 = 1); otherwise, the result is 0 (1 × ...
In computer science, a hash tree (or hash trie) is a persistent data structure that can be used to implement sets and maps, intended to replace hash tables in purely functional programming. In its basic form, a hash tree stores the hashes of its keys, regarded as strings of bits, in a trie, with the actual keys and (optional) values stored at ...
A PATRICIA trie is a special variant of the radix 2 (binary) trie, in which rather than explicitly store every bit of every key, the nodes store only the position of the first bit which differentiates two sub-trees. During traversal the algorithm examines the indexed bit of the search key and chooses the left or right sub-tree as appropriate.
The block bootstrap is used when the data, or the errors in a model, are correlated. In this case, a simple case or residual resampling will fail, as it is not able to replicate the correlation in the data. The block bootstrap tries to replicate the correlation by resampling inside blocks of data (see Blocking (statistics)). The block bootstrap ...