Description: 利用ANSI X9.17标准模拟了一个伪随机数发生器，内核加密算法调用3DES算法，能够按照标准要求产生64位伪随机数和64位种子值。-Use of ANSI X9.17 standard simulation of a pseudo-random number generator, called the kernel encryption algorithm 3DES algorithm, in accordance with the standard requirements to produce 64-bit pseudo-random number and 64-bit seed value. Platform: |
Size: 192512 |
Author:wildkaede |
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Description: binary search and Sets the seed of the random number generator,computes a random integer in a range Platform: |
Size: 1024 |
Author:puneet |
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Description: 采用MD4哈希函数的随机数生成函数，已在ARM环境下测试过。可应用于如无线数据发送时的随机避让。-Random number generator utilizing MD4 hash function of environmental noise captured as the seed and a linear congruence generator for the random numbers. Platform: |
Size: 2048 |
Author:王芊芊 |
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Description: Hardware random number generators attempt to extract
randomness directly from complex physical systems. In this way they create random outputs without requiring any seed inputs. In this paper we describe how to use Physical Random Functions (or Physical Unclonable Functions, PUFs) to create a candidate hardware random number generator. Platform: |
Size: 49152 |
Author:Hassan Abdelaziz |
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Description: 3、 编写一个函数。该函数接受下列参数：一个int数组的名称，数组大小和一个代表选取次数的值。然后函数从数组中随机选择指定数量的元素并打印他们。每个元素最多选择一次（模拟抽奖或挑选陪审成员）。另外，请使用time()或其它类似的函数生成种子。以便srand()函数初始化随机数生成器rand()。编写一个简单的程序测试该函数。-The preparation of a function. The function accepts the following parameters: the name of an int array, array size and select the number of a representative value. Function from the array and then randomly select a specified number of elements and print them. Select up to each element of a (simulated draw, or the selection of jury members). In addition, use time () or other similar function to generate the seed. To srand () function to initialize the random number generator rand (). The preparation of a simple procedure to test the function.
Platform: |
Size: 1024 |
Author:qfnu08zzr |
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Description: this a basic random HTML Generator, take a look at printRandomHTML() for usage
the random page is per seed number so each seed number will give the same random page
For Any suggestions, Bug fixes and other : Serans1 AT gmail.com-this is a basic random HTML Generator, take a look at printRandomHTML() for usage
the random page is per seed number so each seed number will give the same random page
For Any suggestions, Bug fixes and other : Serans1 AT gmail.com Platform: |
Size: 3072 |
Author:serans1 |
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Description: 快速排序使用随机数产生种子程序源码中的快速排序使用随机数来产生种种子提高了算法的效率同时在概率算法中也有非常好的实用性 可直接使用。
-The quicksort use the random number generator seed quicksort program source using a random number to generate every child to improve the efficiency of the algorithm also has the very good practicality can be used directly in probabilistic algorithms. Platform: |
Size: 59392 |
Author:xiruan |
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Description: （1）这是一个伪随机数发生器，周期为2 ^ 19937 - 1,可以用于加密
文件说明如下：
mtrand.h包含文件，包含类的声明
mtrand.cpp的库文件包含非内联成员
mttest.cpp小程序来测试随机数发生器
mttest.out输出文件，用于比较
（2）使用
初始化：两种方式，seed(s) 或seed(array, length).
随机数生成：MTRand_int32产生的32位无符号整数。
MTRand产生均匀的双精度浮点数，在halfopen时间间隔[0，1）（32位分辨率）。
MTRand_open产生均匀的双精度浮点数，在开区间（0，1）（32位分辨率）。
MTRand_closed产生均匀的双精度浮点数，在闭区间[0，1]（32位分辨率）。
MTRand53产生均匀的双精度浮点数，在半开区间[0，1）（53位分辨率）。
-(1) This is a pseudo-random number generator, a period of 2 ^ 19937- 1, can be used to encrypt the file description is as follows: mtrand.h include file contains the class declaration mtrand.cpp library file contains non-inline member mttest cpp small program to test the random number generator mttest.out output file for initialization (2): two ways, SEED (s) or seed (array, length) random number generator: MTRand_int32 produce the 32 unsigned integer. MTRand produce uniform double-precision floating-point number, in halfopen time interval [0, 1) (32-bit resolution). MTRand_open produce uniform double-precision floating-point numbers, the open interval (0,1) (32-bit resolution). MTRand_closed produce uniform double-precision floating-point numbers, the closed interval [0, 1] (32-bit resolution). MTRand53 produce uniform double-precision floating-point number, in the half-open interval [0,1) (53-bit resolution). Platform: |
Size: 18432 |
Author:park |
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Description: 遗传算法求tsp问题及其matlab代码实现
主程序中有一rand( seed ,n) n 为某一整数, 这是设置随机数发生器的种子，这样实际上规定了随机数发生的方式，因此后面的随机数其实都是定下来的，这样所有人就可以看到同一结果, 可注释掉此行，那么每次的结果就不一定再相同了. -Genetic algorithm to solve the TSP problem and the matlab code to achieve
A Rand main program ( seed , n) n as an integer, which is the setting of the random number generator seed, this actually provides a random number generating means, so the random number behind actually are determined, so that everyone can see the same results, can comment out this line, then each time the results do not necessarily the same.
Platform: |
Size: 919552 |
Author:bigrao |
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Description: A pseudorandom number generator (PRNG), also known as a deterministic random bit generator (DRBG),[1] is an algorithm for generating a sequence of numbers whose properties approximate the properties of sequences of random numbers. The PRNG-generated sequence is not truly random, because it is completely determined by a relatively small set of initial values, called the PRNG s seed (which may include truly random values). Although sequences that are closer to truly random can be generated using hardware random number generators, pseudorandom number generators are important in practice for their speed in number generation and their reproducibility.[2]
PRNGs are central in applications such as simulations (e.g. for the Monte Carlo method), electronic games (e.g. for procedural generation), and cryptography. Cryptographic applications require the output not to be predictable earlier outputs, and more elaborate algorithms, which do not inherit the linearity of simpler PRNGs, are needed.-A pseudorandom number generator (PRNG), also known as a deterministic random bit generator (DRBG),[1] is an algorithm for generating a sequence of numbers whose properties approximate the properties of sequences of random numbers. The PRNG-generated sequence is not truly random, because it is completely determined by a relatively small set of initial values, called the PRNG s seed (which may include truly random values). Although sequences that are closer to truly random can be generated using hardware random number generators, pseudorandom number generators are important in practice for their speed in number generation and their reproducibility.[2]
PRNGs are central in applications such as simulations (e.g. for the Monte Carlo method), electronic games (e.g. for procedural generation), and cryptography. Cryptographic applications require the output not to be predictable earlier outputs, and more elaborate algorithms, which do not inherit the linearity of simpler PRNGs, are needed. Platform: |
Size: 9216 |
Author:rajendra |
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Search - random number generator seed