Search results
Results from the WOW.Com Content Network
Common aggregate functions include: Average (i.e., arithmetic mean) Count; Maximum; Median; Minimum; Mode; Range; Sum; Others include: Nanmean (mean ignoring NaN values, also known as "nil" or "null") Stddev; Formally, an aggregate function takes as input a set, a multiset (bag), or a list from some input domain I and outputs an element of an ...
Folds can be regarded as consistently replacing the structural components of a data structure with functions and values. Lists, for example, are built up in many functional languages from two primitives: any list is either an empty list, commonly called nil ([]), or is constructed by prefixing an element in front of another list, creating what is called a cons node ( Cons(X1,Cons(X2,Cons ...
In computer science, a generator is a routine that can be used to control the iteration behaviour of a loop. All generators are also iterators. [1] A generator is very similar to a function that returns an array, in that a generator has parameters, can be called, and generates a sequence of values.
Default generator in R and the Python language starting from version 2.3. Xorshift: 2003 G. Marsaglia [26] It is a very fast sub-type of LFSR generators. Marsaglia also suggested as an improvement the xorwow generator, in which the output of a xorshift generator is added with a Weyl sequence.
A maximum length sequence (MLS) is a type of pseudorandom binary sequence.. They are bit sequences generated using maximal linear-feedback shift registers and are so called because they are periodic and reproduce every binary sequence (except the zero vector) that can be represented by the shift registers (i.e., for length-m registers they produce a sequence of length 2 m − 1).
To perform such a simulation, it is sufficient to construct pseudorandom generators against the family F of all circuits of size s(n) whose inputs have length n and output a single bit, where s(n) is an arbitrary polynomial, the seed length of the pseudorandom generator is O(log n) and its bias is ⅓.
Since dimensional models only gain from aggregates on large data sets, it should be considered at what size of the data sets one should start using aggregates One can also ask oneself if a data warehouse always handles data sets that are too large for direct queries, or if it sometimes is a good idea to omit the aggregate tables when starting a ...
Below, there is view of each step of the mapping process for a list of integers X = [0, 5, 8, 3, 2, 1] mapping into a new list X' according to the function () = + : . View of processing steps when applying map function on a list