kandi X-RAY | jRand Summary
kandi X-RAY | jRand Summary
Java library for random data generation. JRand is heavily inspired by ChanceJS and tries to bring together a lot of that functionality to Java. In JRand there are Generator's which are reusable components to generate all sorts of things. You can get access to any generator you wish via the JRand facade class.
Top functions reviewed by kandi - BETA
- Generate a new card
- Generates a number for the card number
- Generate the syllable
- Generate a string
- Generate lorem
- Creates a paragraph
- Returns the sentence
- Generate number string
- Generate the custom format string
- Returns the area code
- Generates a list of fork methods for the given source class
- Creates a copy - constructor with the given list of variables
- Creates a fork method for this generator
- Random elements in the given list
- Retrieves uniform samples from the population without replacement
- Retrieve n elements from a list
- Generate a list of objects
- Generate a random string
- Joins a list of strings with a separator
- Creates a Country object from a string
- Generate multiple objects
- Returns the gender
- Generates a sentence
- Returns a string representation of this class
- Generate multiple unique elements
jRand Key Features
jRand Examples and Code Snippets
Trending Discussions on jRand
I have build a program that uses Differential Evolution to optimize atom positions in regard to their pair-wise potential and now want to parallelize it with OpenMP to which I am quite new. The Differential Evolution uses an overall do-while loop in which a convergence query is used as the exit condition.
- I know that I can not simply
!$OMP PARALLEL DOthe do-while loop
- I can not predict at what point the loop is terminated
following iterations would also meet the condition. The following is my unparallelized code:...
ANSWERAnswered 2018-Apr-29 at 19:59
The example you provided was not complete: it did not compile. I decided to construct a small (complete) program that does something which I think is similar. I hope it helps!
The program starts up a parallell session, in which new populations are found. Whenever a better population is found than the best so far, the best population is updated. The iteration stops when the global calculation spends too many iterations between consecutive improvements.
In this program, every next population is constructed completely from scratch. In your program, there is a more advanced generation of the next population. favoring 'better' populations over 'worse' ones.
In a naive parallallization, each thread will follow its own path through the search space, and it will not 'learn' from what the other threads have found out. To exchange search information between threads, you need to design a method and then program it. To do so seemed outside of the scope of this question.
Here comes the program:
No vulnerabilities reported
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