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The Flajolet–Martin algorithm is an algorithm for approximating the number of distinct elements in a stream with a single pass and space-consumption logarithmic in the maximal number of possible distinct elements in the stream (the count-distinct problem).
This is a list of countries by number of islands, with figures given for the numbers of islands within their territories. In some cases, this figure is approximate and may vary slightly between sources depending on which islands are counted. The criteria for inclusion appear to differ considerably between the countries so they are not necessarily directly comparable. Different languages use ...
Such a partition is called a partition with distinct parts. If we count the partitions of 8 with distinct parts, we also obtain 6: 8; 7 + 1; 6 + 2; 5 + 3; 5 + 2 + 1; 4 + 3 + 1; This is a general property. For each positive number, the number of partitions with odd parts equals the number of partitions with distinct parts, denoted by q(n).
The floor function must be used to convert a floating number to an integer ( and possibly casting of datatypes too ). The function nextSort is a sorting function used to sort each bucket. Conventionally, insertion sort is used, but other algorithms could be used as well, such as selection sort or merge sort .
In computer science, the count-distinct problem [1] (also known in applied mathematics as the cardinality estimation problem) is the problem of finding the number of distinct elements in a data stream with repeated elements. This is a well-known problem with numerous applications.
HyperLogLog is an algorithm for the count-distinct problem, approximating the number of distinct elements in a multiset. [1] Calculating the exact cardinality of the distinct elements of a multiset requires an amount of memory proportional to the cardinality, which is impractical for very large data sets. Probabilistic cardinality estimators ...
Disability amid disaster: People with disabilities are disproportionately impacted by natural disasters
A notable problem occurs when information gain is applied to attributes that can take on a large number of distinct values. For example, suppose that one is building a decision tree for some data describing the customers of a business.