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Linear congruential generator - how to choose seeds and statistical tests

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问题 I need to do a linear congruential generator that will successfully pass the selected statistical tests. My question is: how to choose numbers for the generator properly and which statistical tests should I choose? I thought about: Chi-Square Frequency Test for Uniformity Collect 10,000 numbers per generation method Sub-divide[0.1) into 10 equal subdivisions Kolmogorov-Smirnov Test for uniformity Since K-S Test works better with a smaller set of numbers, you may use the first 100 out fo the

pseudo random distribution which guarantees all possible permutations of value sequence - C++

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问题 Random question. I am attempting to create a program which would generate a pseudo-random distribution. I am trying to find the right pseudo-random algorithm for my needs. These are my concerns: 1) I need one input to generate the same output every time it is used. 2) It needs to be random enough that a person who looks at the output from input 1 sees no connection between that and the output from input 2 (etc.), but there is no need for it to be cryptographically secure or truly random. 3

Making a customizable LCG that travels backward and forward

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问题 How would i go about making an LCG (type of pseudo random number generator) travel in both directions? I know that travelling forward is (a*x+c)%m but how would i be able to reverse it? I am using this so i can store the seed at the position of the player in a map and be able to generate things around it by propogating backward and forward in the LCG (like some sort of randomized number line). 回答1: All LCGs cycle. In an LCG which achieves maximal cycle length there is a unique predecessor and

why 48 bit seed in util Random class?

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问题 Why this class uses 48 bit seed in its linear congruence formula? I would have expected 32 or 64... I know it takes higher order bits when asked for 32 bit values. But why only 16 more additional bits? Was it a "random" choice? 回答1: You need more bits of state than bits of output, because the nature of an LCG is such that the low-order bits of state are not very random at all. So if you want 32-bit outputs, you need more than 32 bits of state. Why use 48 rather than 64? Because 48 is enough,

why 48 bit seed in util Random class?

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Why this class uses 48 bit seed in its linear congruence formula? I would have expected 32 or 64... I know it takes higher order bits when asked for 32 bit values. But why only 16 more additional bits? Was it a "random" choice? You need more bits of state than bits of output, because the nature of an LCG is such that the low-order bits of state are not very random at all. So if you want 32-bit outputs, you need more than 32 bits of state. Why use 48 rather than 64? Because 48 is enough, and you're designing this decades ago , so there are good reasons to want to avoid using any more resources

How to generate a predictable shuffling of a sequence without generating the whole sequence in advance?

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问题 The following python code describes exactly what I want to achieve for a sequence of arbitrary size (population): import random fixed_seed = 1 #generate the same sequence every time with a fixed seed population = 1000 sample_count = 5 #demonstration number num_retries = 3 #just enough to show the repeatable behaviour for trynum in xrange(num_retries): #generate the fresh/ordered sequence (0->population)... seq = range(population) #seed the random number generator the same way every time...

Why is the use of rand() considered bad?

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问题 I heard some guys telling that the use of rand() is bad EVEN AFTER USING srand() to get a seed. Why is that so? I want to know how the stuff happens... And sorry for another question.. but what is an alternative to this then? 回答1: There are two parts to this story. First, rand is a pseudorandom number generator. This means it depends on a seed. For a given seed it will always give the same sequence (assuming the same implementation). This makes it not suitable for certain applications where