Seeding Multiple Random Number Generators - c#

I have recently been discussing the initialisation of multiple random number generators of the same type in the comments of another post and in that discussion we asked the following questions:
1) Is it a good idea to create multiple instances of the same random number generator with different seeds and use these random number generators in different parts of the program?
2) In particular, can the technique of creating random number generators using the .Net Random class, seeded as below, and using each RNG in different program contexts cause problems:
int size = 64; // The number of RNGs to use
int seed; // Get seed using some normal technique
Random[] r = new Random[size];
for (int i = 0; i < size; i++)
{
r[i] = new Random(seed + i);
}
3) What would you recommend instead if multiple streams of random numbers are required?
4) How would you recommend generating random numbers when thread safety is required?

1) Is it a good idea to create multiple instances of the same random number generator with different seeds and use these random number generators in different parts of the program?
No. The above scheme is in general not recommended.
In his book, The Art of Computer Programming, Volume 2: Seminumerical Algorithms. Addison-Wesley, Reading, MA, third edition, 1997, Dr. Knuth states that
It is not easy to invent a foolproof source of random numbers.
In this case, I point out that taking subsequences from a random sequence may be less random than the original sequence of random numbers:
Random Numbers
PCG
Notice that Micosoft's Random implementation is based on a subractive lagged-fibonacci generator:
Reference Source - System.Random
This kind of random number generator is known for an inbuilt three-point correlation, after all, we're generating the next random number:
These kinds of Random Number Generators also depend heavily on the initialisation of their initial 55 number state. Poor initialisation may lead to poor random numbers. In the above case, similar states, may result in correlated random numbers from each of the different random number generators. Microsoft even recommends against this in their MSDN post about System.Random: MSDN The System.Random class and thread safety:
Instead of instantiating individual Random objects, we recommend that you create a single Random instance to generate all the random numbers needed by your app.
We shall look at an example where a particular initialisation creates strong correlation between the different random number generators and look for alternatives.
2) I have implemented a program that attempts to initialise 64 instances of Random as described above so that we observe any visible flaws. I chose a particular initialisation as a proof of concept:
int size = 64; // The number of random numbers generators
int length = 20; // The number of random numbers from each generator
int steps = 18; // Move 18 steps forward in the beginning to show a particular phenomenon
Random[] r = new Random[size];
for (int i = 0; i < size; i++)
{
r[i] = new Random(i + 1);
// move RNG forward 18 steps
for (int j = 0; j < steps; j++)
{
r[i].Next(3);
}
}
for (int i = 0; i < size; i++)
{
for (int j = 0; j < length; j++)
{
Console.Write(r[i].Next(3) + ", "); // Generate a random number, 0 represents a small number, 1 a medium number and 2 a large number
}
Console.WriteLine();
}
This program generates the output shown here, each row represents the output from another RNG:
Notice that the highlighted columns: at particular places the RNGs seem to synchronise and produce output that does not look independent from each other.
I also wish to add another note, that creating a single list of random numbers and taking one random number from the list of each row also produces poor looking random numbers (the RNG being used here is known to have fail some statistical after all!).
3) The type of RNG used depends on your context. Some may be happy with the above output. In other cases, the RNG used may be unusable (Monte Carlo Simulation and Cryptography are two scenarios where System.Random should never be used, even for one stream of random numbers).
If you need to extract multiple subsequences of Random Numbers, find an RNG that has been designed for that purpose:
PCG
4) Finally, what if I want to use System.Random in multiple threads?
Microsoft MSDN has the answer in the same link I referred to above:
MSDN The System.Random class and thread safety

Not sure what "multiple streams of random numbers" means. In random numbers there is no relationship between any two random numbers, there is no order, each is a standalone instance.
If you use a cryptographic PRNG no seeding is necessary. Consider the .net RNGCryptoServiceProvider Class.

Related

Not jumping into code while not debugging [duplicate]

This question already has answers here:
Random number generator only generating one random number
(15 answers)
Closed 3 years ago.
for (int i = 1; i < 10; i++)
{
number = randomNumber();
if (!straightLine.Contains(number))
{
straightLine.Add(number);
}
}
public static int randomNumber()
{
Random random = new Random();
return random.Next(1, 100);
}
when I debug it works fine but when I run the program it gets 1 random number and that's it. so the problem is that the random Number method is only called once (when I don't debug then it calls it every time)
what can I do?
germi's answer is slightly misleading as it implies that any repeated instantiation of a new Random will produce the same sequence of values. This isn't quite correct, because your debug code works as you expect.
The documentation for Random says that the Random number generator is seeded from the system clock if you dont pass a seed value. https://learn.microsoft.com/en-us/dotnet/api/system.random.-ctor?view=netframework-4.8
The reason it works in debug is that debugging code is very slow (you're literally taking hundreds of milliseconds to step through the code a line at a time) and the clock has time to change in between runs of the loop.
When your code is run at full speed it runs so quickly that the system clock simply won't have changed to a new milliseconds value, so your repeated making of a new Random will result in it being seeded with the same value from the clock
If you were to insert some delay in the code such as Thread.Sleep(1000) in the loop then it would work. If you were to run the loop a million times it would take long enough to work through that the clock would eventually change - a million iterations might take long enough for a small handful of values to come out of the Random
The recommendation for a solution is sound though; make one new Random somewhere outside of the loop and then repeatedly call it. You could also seed the Random with something that is unique each time (like the value of i), though you should bear in mind that providing a particular seed will guarantee that the same random number comes out of it when you call Next. In some contexts this is useful, because you might want a situation where you can provide a certain value and then see the same sequence of random numbers emerge. The main thing to be mindful of is that by default the Random starts it's sequence based on the clock; two different computers with different time settings can theoretically produce the same random numbers if their clocks are reading the same at the moment the Random is created.
Repeatedly instantiating a new Random instance will lead to the same number being generated, since the seed will be the same.
The solution is to have one instance of Random in the class that generates the values:
var random = new Random();
for (int i = 1; i < 10; i++)
{
number = random.Next(1,100);
if (!straightLine.Contains(number))
{
straightLine.Add(number);
}
}
Note that this behavior only exists in .NET Framework, .NET Core will produce different values even across multiple Random instances created in quick succession.

Random.Next() Digits always gives the same output every runtime

I know there are lots of questions about random in here but they didn't help me at all, they are about generating same numbers in a single runtime, and mine isn't
So here is my code.
Random rnd = new Random(0);
for (int c = 0; c < arraySize; c++)
data[c] = rnd.Next();
if i run it for the first time.
the element of rnd.Next() would be 1559595546
next output would be 1755192844
and so on,
if i close the program and re run it,
the same set of numbers would be generated
data[0] = 1559595546
data[1] = 1755192844
and so on,
Why is that so? isnt it should generate different set of numbers every time i close the program and run it?
please help me understand this.
No, you're providing a fixed seed of 0. Therefore the output is always the same. Take a read of the documentation for the constructor overload that you are using:
http://msdn.microsoft.com/en-us/library/ctssatww%28v=vs.110%29.aspx
paying particular attention to the following statement:
Providing an identical seed value to different Random objects causes each instance to produce identical sequences of random numbers.
Perhaps you should:
var rnd = new Random(); //no seed
Thsi is because you have provide Seed 0 like
Random = new Random(0); //0 is Seed
If you remove it then it will generate different random numbers as you required.

Die Roller with user selecting number of sides

I'm new to programming and I'm trying to build a die roller game in C#.
The program asks the user for the number of sides and then rolls a dice with a random number.
I have the following pseudocode:
ask user for the number of sides.
roll the die with a random number with max range being the number of sides.
tell the user the number rolled
My question is how do I roll the dice with generating a random number in range specified by the user?
The System.Random class is commonly used to generate casual random numbers.
It has an overload of a method called Next which generates a random integer that is greater than or equal to 0 and strictly less than the passed integer argument.
Thus if the user chooses an n-sided die, and you have an instance of Random r,
r.Next(n) + 1
will generate a random integer between 1 and n inclusive.
It is good practice to create a single instance of Random and reuse it, because if you create several instances close together they will all generate the same numbers.
Random random = new Random();
int randomNumber = random.Next(1, userInput);
One thing for you to know though is the random function isn't exactly random, its random but in the same random order everytime.. I'll let you look on your favourite search engine for seeding ;)
To get a Random integer between 0 and 100 with 100 not included:
Random random = new Random();
int randomNumber = random.Next(0, 100);
System.Random, specifically System.Random.Next(Int32, Int32) should get you started.
Random.Next(Int32, Int32)
Returns a random number within a specified range.
public int RollDice(int iNoSides)
{
var rand = new Random();
return rand.Next(iNoSides) + 1
}
A simple google of random number c# would of got you more or less this exact code.
EDIT
You have the link to the MSDN documentation from other answers and comments, I know your new to programming but the best way to learn is to try as hard as you can to get something. If you get to a state where your mind can't function any more, posting your attempt with your question on Stack will make people generally a lot happier that they are genuinely helping somebody that is stuck
EDIT 2
Following #Rawling's comment, I realised that the overload of the method states that Next(n) returns a Non-negative number less than the specified maximum.

How to seed GUID generation?

What would be the easiest way to code a function in .NET to generate a GUID based on a seed so that I can have greater confidence about its uniqueness?
string GenerateSeededGuid(int seed) { /* code here */ }
Ideally, the seed would come from CryptGenRandom which describes its random number generation as follows:
The data produced by this function is cryptographically random. It is
far more random than the data generated by the typical random number
generator such as the one shipped with your C compiler.
This function is often used to generate random initialization vectors
and salt values.
Software random number generators work in fundamentally the same way.
They start with a random number, known as the seed, and then use an
algorithm to generate a pseudo-random sequence of bits based on it.
The most difficult part of this process is to get a seed that is truly
random. This is usually based on user input latency, or the jitter
from one or more hardware components.
With Microsoft CSPs, CryptGenRandom uses the same random number
generator used by other security components. This allows numerous
processes to contribute to a system-wide seed. CryptoAPI stores an
intermediate random seed with every user. To form the seed for the
random number generator, a calling application supplies bits it might
have—for instance, mouse or keyboard timing input—that are then
combined with both the stored seed and various system data and user
data such as the process ID and thread ID, the system clock, the
system time, the system counter, memory status, free disk clusters,
the hashed user environment block. This result is used to seed the
pseudorandom number generator (PRNG). [...] If an application has access to a good random source, it can
fill the pbBuffer buffer with some random data before calling
CryptGenRandom. The CSP then uses this data to further randomize its
internal seed. It is acceptable to omit the step of initializing the
pbBuffer buffer before calling CryptGenRandom.
tldr; use Guid.NewGuid instead of trying to invent another "more random" approach. (The only reason I can think of to create a UUIDvX from a seed is when a predictable, resettable, sequence is desired. However, a GUID might also not be the best approach2.)
By very definition of being a finite range - 128bits minus 6 versioning bits, so 122 bits of uniqueness for v4 - there are only so many (albeit supremely huge number! astronomically big!) "unique" identifiers.
Due to the Pigeonhole Principle there are only so many Pigeonholes. If Pigeons keep reproducing eventually there will not be enough Holes for each Pigeon. Due to the Birthday Paradox, assuming complete randomness, two Pigeons will try to fight for the same Pigeonholes before they are all filled up. Because there is no Master Pigeonhole List1 this cannot be prevented. Also, not all animals are Pigeons3.
While there are no guarantees as to which GUID generator will be used, .NET uses the underlying OS call, which is a GUIDv4 (aka Random UUID) generator since Windows 2k. As far as I know - or care, really - this is as good a random as it gets for such a purpose. It has been well vetted for over a decade and has not been replaced.
From Wikipedia:
.. only after generating 1 billion UUIDs every second for the next 100 years, the probability of creating just one duplicate would be about 50%. The probability of one duplicate would be about 50% if every person on earth owns 600 million UUIDs.
1 While there are still a finite set of Pigeonholes, UUIDv1 (aka MAC UUID) - assuming unique time-space - is guaranteed to generate deterministically unique numbers (with some "relatively small" theoretical maximum number of UUIDs generated per second on a given machine). Different broods of Pigeons living in different parallel dimensions - awesome!
2 Twitter uses Snowflakes in parallel dimensions in its own distributed Unique-ID scheme.
3 Rabbits like to live in Burrows, not Pigeonholes. The use of a GUID also acts as an implicit parallel partition. It is only when a duplicate GUID is used for the same purpose that collision-related problems can arise. Just think of how many duplicate auto-increment database primary keys there are!
All you really need to do in your GenerateSeededGuid method is to create a 128-bit random number and the convert it to a Guid. Something like:
public Guid GenerateSeededGuid(int seed)
{
var r = new Random(seed);
var guid = new byte[16];
r.NextBytes(guid);
return new Guid(guid);
}
This is a bit old, but no need for a random generator. But yes this is usefull for testing purpose, but not for general uses
public static Guid GenerateSeededGuid<T>(T value)
{
byte[] bytes = new byte[16];
BitConverter.GetBytes(value.GetHashCode()).CopyTo(bytes, 0);
return new Guid(bytes);
}
public static Guid SeededGuid(int seed, Random random = null)
{
random ??= new Random(seed);
return Guid.Parse(string.Format("{0:X4}{1:X4}-{2:X4}-{3:X4}-{4:X4}-{5:X4}{6:X4}{7:X4}",
random.Next(0, 0xffff), random.Next(0, 0xffff),
random.Next(0, 0xffff),
random.Next(0, 0xffff) | 0x4000,
random.Next(0, 0x3fff) | 0x8000,
random.Next(0, 0xffff), random.Next(0, 0xffff), random.Next(0, 0xffff)));
}
//Example 1
SeededGuid("Test".GetHashCode());
SeededGuid("Test".GetHashCode());
//Example 2
var random = new Random("Test".GetHashCode());
SeededGuid("Test".GetHashCode(), random);
SeededGuid("Test".GetHashCode(), random);
This method is based on php v4 uui https://www.php.net/manual/en/function.uniqid.php#94959

smart way to generate unique random number

i want to generate a sequence of unique random numbers in the range of 00000001 to 99999999.
So the first one might be 00001010, the second 40002928 etc.
The easy way is to generate a random number and store it in the database, and every next time do it again and check in the database if the number already exists and if so, generate a new one, check it again, etc.
But that doesn't look right, i could be regenerating a number maybe 100 times if the number of generated items gets large.
Is there a smarter way?
EDIT
as allways i forgot to say WHY i wanted this, and it will probably make things clearer and maybe get an alternative, and it is:
we want to generate an ordernumber for a booking, so we could just use 000001, 000002 etc. But we don't want to give the competitors a clue of how much orders are created (because it's not a high volume market, and we don't want them to know if we are on order 30 after 2 months or at order 100. So we want to have an order number which is random (yet unique)
You can use either an Linear Congruential Generator (LCG) or Linear Feedback Shift Register (LFSR). Google or wikipedia for more info.
Both can, with the right parameters, operate on a 'full-cycle' (or 'full period') basis so that they will generate a 'psuedo-random number' only once in a single period, and generate all numbers within the range. Both are 'weak' generators, so no good for cyptography, but perhaps 'good enough' for apparent randomness. You may have to constrain the period to work within your 'decimal' maximum as having 'binary' periods is necessary.
Update: I should add that it is not necessary to pre-calculate or pre-store previous values in any way, you only need to keep the previous seed-value (single int) and calculate 'on-demand' the next number in the sequence. Of course you can save a chain of pre-calculated numbers to your DB if desired, but it isn't necessary.
How about creating a set all of possible numbers and simply randomising the order? You could then just pick the next number from the tail.
Each number appears only once in the set, and when you want a new one it has already been generated, so the overhead is tiny at the point at which you want one. You could do this in memory or the database of your choice. You'll just need a sensible locking strategy for pulling the next available number.
You could build a table with all the possible numbers in it, give the record a 'used' field.
Select all records that have not been 'used'
Pick a random number (r) between 1 and record count
Take record number r
Get your 'random value' from the record
Set the 'used' flag and update the db.
That should be more efficient than picking random numbers, querying the database and repeat until not found as that's just begging for an eternity for the last few values.
Use Pseudo-random Number Generators.
For example - Linear Congruential Random Number Generator
(if increment and n are coprime, then code will generate all numbers from 0 to n-1):
int seed = 1, increment = 3;
int n = 10;
int x = seed;
for(int i = 0; i < n; i++)
{
x = (x + increment) % n;
Console.WriteLine(x);
}
Output:
4
7
0
3
6
9
2
5
8
1
Basic Random Number Generators
Mersenne Twister
Using this algorithm might be suitable, though it's memory consuming:
http://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle
Put the numbers in the array from 1 to 99999999 and do the shuffle.
For the extremely limited size of your numbers no you cannot expect uniqueness for any type of random generation.
You are generating a 32bit integer, whereas to reach uniqueness you need a much larger number in terms around 128bit which is the size GUIDs use which are guaranteed to always be globally unique.
In case you happen to have access to a library and you want to dig into and understand the issue well, take a look at
The Art of Computer Programming, Volume 2: Seminumerical Algorithms
by Donald E. Knuth. Chapter 3 is all about random numbers.
You could just place your numbers in a set. If the size of the set after generation of your N numbers is too small, generate some more.
Do some trial runs. How many numbers do you have to generate on average? Try to find out an optimal solution to the tradeoff "generate too many numbers" / "check too often for duplicates". This optimal is a number M, so that after generating M numbers, your set will likely hold N unique numbers.
Oh, and M can also be calculated: If you need an extra number (your set contains N-1), then the chance of a random number already being in the set is (N-1)/R, with R being the range. I'm going crosseyed here, so you'll have to figure this out yourself (but this kinda stuff is what makes programming fun, no?).
You could put a unique constraint on the column that contains the random number, then handle any constraint voilations by regenerating the number. I think this normally indexes the column as well so this would be faster.
You've tagged the question with C#, so I'm guessing you're using C# to generate the random number. Maybe think about getting the database to generate the random number in a stored proc, and return it.
You could try giving writing usernames by using a starting number and an incremental number. You start at a number (say, 12000), then, for each account created, the number goes up by the incremental value.
id = startValue + (totalNumberOfAccounts * inctrementalNumber)
If incrementalNumber is a prime value, you should be able to loop around the max account value and not hit another value. This creates the illusion of a random id, but should also have very little conflicts. In the case of a conflicts, you could add a number to increase when there's a conflict, so the above code becomes. We want to handle this case, since, if we encounter one account value that is identical, when we increment, we will bump into another conflict when we increment again.
id = startValue + (totalNumberOfAccounts * inctrementalNumber) + totalConflicts
By fallowing line we can get e.g. 6 non repetitive random numbers for range e.g. 1 to 100.
var randomNumbers = Enumerable.Range(1, 100)
.OrderBy(n => Guid.NewGuid())
.Take(6)
.OrderBy(n => n);
I've had to do something like this before (create a "random looking" number for part of a URL). What I did was create a list of keys randomly generated. Each time it needed a new number it simply randomly selected a number from keys.Count and XOR the key and the given sequence number, then outputted XORed value (in base 62) prefixed with the keys index (in base 62).
I also check the output to ensure it does not contain any naught words. If it does simply take the next key and have a second go.
Decrypting the number is equally simple (the first digit is the index to the key to use, a simple XOR and you are done).
I like andora's answer if you are generating new numbers and might have used it had I known. However if I was to do this again I would have simply used UUIDs. Most (if not every) platform has a method for generating them and the length is just not an issue for URLs.
You could try shuffling the set of possible values then using them sequentially.
I like Lazarus's solution, but if you want to avoid effectively pre-allocating the space for every possible number, just store the used numbers in the table, but build an "unused numbers" list in memory by adding all possible numbers to a collection then deleting every one that's present in the database. Then select one of the remaining numbers and use that, adding it to the list in the database, obviously.
But, like I say, I like Lazaru's solution - I think that's your best bet for most scenarios.
function getShuffledNumbers(count) {
var shuffledNumbers = new Array();
var choices = new Array();
for (var i = 0; i<count; i++) {
// choose a number between 1 and amount of numbers remaining
choices[i] = selectedNumber = Math.ceil(Math.random()*(99999999 - i));
// Now to figure out the number based on this selection, work backwards until
// you figure out which choice this number WOULD have been on the first step
for (var j = 0; j < i; j++) {
if (choices[i - 1 - j] >= selectedNumber) {
// This basically says "it was choice number (selectedNumber) on the last step,
// but if it's greater than or equal to this, it must have been choice number
// (selectedNumber + 1) on THIS step."
selectedNumber++;
}
}
shuffledNumbers[i] = selectedNumber;
}
return shuffledNumbers;
}
This is as fast a way I could think of and only uses memory as it needs, however if you run it all the way through it will use double as much memory because it has two arrays, choices and shuffledNumbers.
Running a linear congruential generator once to generate each number is apt to produce rather feeble results. Running it through a number of iterations which is relatively prime to your base (100,000,000 in this case) will improve it considerably. If before reporting each output from the generator, you run it through one or more additional permutation functions, the final output will still be a duplicate-free permutation of as many numbers as you want (up to 100,000,000) but if the proper functions are chosen the result can be cryptographically strong.
create and store ind db two shuffled versions(SHUFFLE_1 and SHUFFLE_2) of the interval [0..N), where N=10'000;
whenever a new order is created, you assign its id like this:
ORDER_FAKE_INDEX = N*SHUFFLE_1[ORDER_REAL_INDEX / N] + SHUFFLE_2[ORDER_REAL_INDEX % N]
I also came with same kind of problem but in C#. I finally solved it. Hope it works for you also.
Suppose I need random number between 0 and some MaxValue and having a Random type object say random.
int n=0;
while(n<MaxValue)
{
int i=0;
i=random.Next(n,MaxValue);
n++;
Write.Console(i.ToString());
}
the stupid way: build a table to record, store all the numble first, and them ,every time the numble used, and flag it as "used"
System.Random rnd = new System.Random();
IEnumerable<int> numbers = Enumerable.Range(0, 99999999).OrderBy(r => rnd.Next());
This gives a randomly shuffled collection of ints in your range. You can then iterate through the collection in order.
The nice part about this is that you're not actually creating the entire collection in memory.
See comments below - this will generate the entire collection in memory when you iterate to the first element.
You can genearate number like below if you are ok with consumption of memory.
import java.util.ArrayList;
import java.util.Collections;
public class UniqueRandomNumbers {
public static void main(String[] args) {
ArrayList<Integer> list = new ArrayList<Integer>();
for (int i=1; i<11; i++) {
list.add(i);
}
Collections.shuffle(list);
for (int i=0; i<11; i++) {
System.out.println(list.get(i));
}
}
}

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