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The summaries (arrays) output by the algorithm are mergeable, in the sense that combining summaries of two streams s and r by adding their arrays keywise and then decrementing each counter in the resulting array until only k keys remain results in a summary of the same (or better) quality as compared to running the Misra-Gries algorithm over ...
The bag-of-words model (BoW) is a model of text which uses an unordered collection (a "bag") of words. It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity .
Word count is commonly used by translators to determine the price of a translation job. Word counts may also be used to calculate measures of readability and to measure typing and reading speeds (usually in words per minute). When converting character counts to words, a measure of 5 or 6 characters to a word is generally used for English. [1]
Word2vec is a group of related models that are used to produce word embeddings.These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words.
This wiki template is to ease the use of text counting within Word Association Game. {{Wikipedia:Department of Fun/Word Count}} produces the following text: Word count is / as of word: . The parameters must be set, otherwise it produces a dull text.
As a collection algorithm, reference counting tracks, for each object, a count of the number of references to it held by other objects. If an object's reference count reaches zero, the object has become inaccessible, and can be destroyed. When an object is destroyed, any objects referenced by that object also have their reference counts decreased.
Python's tuple assignment, fully available in its foreach loop, also makes it trivial to iterate on (key, value) pairs in dictionaries: for key , value in some_dict . items (): # Direct iteration on a dict iterates on its keys # Do stuff
The HyperLogLog has three main operations: add to add a new element to the set, count to obtain the cardinality of the set and merge to obtain the union of two sets. Some derived operations can be computed using the inclusion–exclusion principle like the cardinality of the intersection or the cardinality of the difference between two HyperLogLogs combining the merge and count operations.