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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.
Before 2.5, generators were lazy iterators; data was passed unidirectionally out of the generator. From Python 2.5 on, it is possible to pass data back into a generator function; and from version 3.3, it can be passed through multiple stack levels. [102]
The result has been called "procedural oatmeal", a term coined by writer Kate Compton, in that while it is possible to mathematically generate thousands of bowls of oatmeal with procedural generation, they will be perceived to be the same by the user, and lack the notion of perceived uniqueness that a procedural system should aim for. [3]
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.
The main approach HKDF follows is the "extract-then-expand" paradigm, where the KDF logically consists of two modules: the first stage takes the input keying material and "extracts" from it a fixed-length pseudorandom key, and then the second stage "expands" this key into several additional pseudorandom keys (the output of the KDF).
In computing, inline expansion, or inlining, is a manual or compiler optimization that replaces a function call site with the body of the called function. Inline expansion is similar to macro expansion, but occurs during compilation, without changing the source code (the text), while macro expansion occurs prior to compilation, and results in different text that is then processed by the compiler.
It can be shown that if is a pseudo-random number generator for the uniform distribution on (,) and if is the CDF of some given probability distribution , then is a pseudo-random number generator for , where : (,) is the percentile of , i.e. ():= {: ()}. Intuitively, an arbitrary distribution can be simulated from a simulation of the standard ...
The name is derived from the term "panel data", an econometrics term for data sets that include observations over multiple time periods for the same individuals, [3] as well as a play on the phrase "Python data analysis". [4]: 5 Wes McKinney started building what would become Pandas at AQR Capital while he was a researcher there from 2007 to ...