The bias-correction constant, denoted alpha in the algorithm
Number of bytes to take per hash, denoted b in the algorithm (b = log2(m))
The number of registers, denoted m in the algorithm
The registers used to store data
Compute the accuracy of the cardinality estimation produced by this HyperLogLog
The accuracy of the cardinality estimation
Estimate the cardinality of the multiset
The estimated cardinality of the multiset
Check if another HyperLogLog is equal to this one
The HyperLogLog to compare to this one
True if they are equal, false otherwise
Load an Object from a provided JSON object
the JSON object to load
Return the Object loaded from the provided JSON object
Perform the union with another HyperLogLog multiset
Multiset ot merge with
The union of the two multisets
Return a next random seeded int32 integer
Save the current structure as a JSON
Update The multiset with a new element
Element to add
Constructor
The number of registers to use
Get the number of registers used by the HyperLogLog
Get a function used to draw random number
A factory function used to draw random integer
Get the seed used in this structure
Set the seed for this structure
the new seed that will be used in this structure
Generated using TypeDoc
HyperLogLog is an algorithm for the count-distinct problem, approximating the number of distinct elements in a multiset.
HyperLogLog: the analysis of a near-optimal cardinality estimation algorithm http://algo.inria.fr/flajolet/Publications/FlFuGaMe07.pdf
Thomas Minier