Search results
Results from the WOW.Com Content Network
Rendezvous or highest random weight (HRW) hashing [1] [2] is an algorithm that allows clients to achieve distributed agreement on a set of options out of a possible set of options. A typical application is when clients need to agree on which sites (or proxies) objects are assigned to.
[1] [2] The algorithm assumes that we have no prior knowledge about the accuracy of the algorithms in the pool, but there are sufficient reasons to believe that one or more will perform well. Assume that the problem is a binary decision problem. To construct the compound algorithm, a positive weight is given to each of the algorithms in the pool.
The weighted majority algorithm corrects above trivial algorithm by keeping a weight of experts instead of fixing the cost at either 1 or 0. [1] This would make fewer mistakes compared to halving algorithm. Initialization: Fix an /. For each expert, associate the weight ≔1.
The Go programming language has built-in types complex64 (each component is 32-bit float) and complex128 (each component is 64-bit float). Imaginary number literals can be specified by appending an "i". The Perl core module Math::Complex provides support for complex numbers. Python provides the built-in complex type. Imaginary number literals ...
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.
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.
The two-step floating catchment area (2SFCA) method is a method for combining a number of related types of information into a single, immediately meaningful, index that allows comparisons to be made across different locations. Its importance lies in the improvement over considering the individual sources of information separately, where none on ...
A minifloat in 1 byte (8 bit) with 1 sign bit, 4 exponent bits and 3 significand bits (in short, a 1.4.3 minifloat) is demonstrated here. The exponent bias is defined as 7 to center the values around 1 to match other IEEE 754 floats [3] [4] so (for most values) the actual multiplier for exponent x is 2 x−7. All IEEE 754 principles should be ...