HyperLogLog
Approximate distinct counting algorithm
HyperLogLog is an algorithm for the count-distinct problem, approximating the number of distinct elements in a multiset. 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.
Nº Q17092118 ★★
Incomum · Saberes
HyperLogLog
Approximate distinct counting algorithm
HyperLogLog is an algorithm for the count-distinct problem, approximating the number of distinct elements in a multiset. 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.
Último preço
—
Preço mínimo
—
Mediana 7 d
—
Vendas 30 d
0
Faixa 30 d
—
Em circulação
0
Cotação
mediana
mín – máx
vendas
Sem vendas no período
Ver tabela
| Data | mediana | Mín | Máx | vendas |
|---|
Histórico de vendas
- Última venda
- —
- Média 30 d
- —
- Mínima 30 d
- —
- Máxima 30 d
- —
- Vendas 7 d
- 0
- Vendas 30 d
- 0
Ainda sem vendas.
Vendas anônimas: sem comprador nem vendedor. Os números contam só vendas entre jogadores.
Na Wikipédia
Texto em inglês Ainda não há artigo no seu idioma: trecho em inglês.
HyperLogLog is an algorithm for the count-distinct problem, approximating the number of distinct elements in a multiset. 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, such as the HyperLogLog algorithm, use significantly less memory than this, but can only approximate the cardinality. The HyperLogLog algorithm is able to estimate cardinalities of > 109 with a typical accuracy (standard error) of 2%, using 1.5 kB of memory. HyperLogLog is an extension of the earlier LogLog algorithm, itself deriving from the 1984 Flajolet–Martin algorithm.
Texto: Wikipédia em inglês, CC BY-SA 4.0. ·
Cartas próximas
Teorema de Cantor
Nº Q474881 ★★
Learning with errors
Problem in machine learning that is conjectured to be hard to solve. Introduced by Oded Regev in 2005, it is a generalization of the parity learning problem
Nº Q6510239 ★
Logaritmo comum
Função matemática
Nº Q966582 ★★★
Amostragem por hipercubo latino
Nº Q6496514 ★★
Limited-memory BFGS
Optimization algorithm
Nº Q6549489 ★★
Log-structured merge-tree
Data structure
Nº Q6666764 ★★