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Other examples of real-world systems that use Rendezvous Hashing include the Github load balancer, [10] the Apache Ignite distributed database, [11] the Tahoe-LAFS file store, [12] the CoBlitz large-file distribution service, [13] Apache Druid, [14] IBM's Cloud Object Store, [15] the Arvados Data Management System, [16] Apache Kafka, [17] and ...
The difference between the full capitalization, float-adjusted, and equal weight versions is in how the index components are weighted. The full cap index uses the total shares outstanding for each company. The float-adjusted index uses shares adjusted for free float. The equal-weighted index assigns each security in the index the same weight.
The aim is to find non-negative weights such that for all examples, the sign of the weighted combination of the features matches its labels. That is, require that for all . Without loss of generality, assume the total weight is 1 so that they form a distribution.
Fundamentally based indexes or fundamental indexes, also called fundamentally weighted indexes, are indexes in which stocks are weighted according to factors related to their fundamentals such as earnings, dividends and assets, commonly used when performing corporate valuations. This fundamental weight may be calculated statically, or it may be ...
Hasse diagram of some classes of quantitative automata, ordered by expressiveness. [1]: Fig.1 In theoretical computer science and formal language theory, a weighted automaton or weighted finite-state machine is a generalization of a finite-state machine in which the edges have weights, for example real numbers or integers.
Moving least squares is a method of reconstructing continuous functions from a set of unorganized point samples via the calculation of a weighted least squares measure biased towards the region around the point at which the reconstructed value is requested.
Bipartite network projection is an extensively used method for compressing information about bipartite networks. [1] Since the one-mode projection is always less informative than the original bipartite graph, an appropriate method for weighting network connections is often required.
This algorithm can easily be adapted to compute the variance of a finite population: simply divide by n instead of n − 1 on the last line.. Because SumSq and (Sum×Sum)/n can be very similar numbers, cancellation can lead to the precision of the result to be much less than the inherent precision of the floating-point arithmetic used to perform the computation.