I would like to genrate a random permutation as fast as possible.
The problem: The knuth shuffle which is O(n) involves generating n random numbers.
Since generating random numbers is quite expensive.
I would like to find an O(n) function involving a fixed O(1) amount of random numbers.
I realize that this question has been asked before, but I did not see any relevant answers.
Just to stress a point: I am not looking for anything less than O(n), just an algorithm involving less generation of random numbers.
Thanks
Create a 1-1 mapping of each permutation to a number from 1 to n! (n factorial). Generate a random number in 1 to n!, use the mapping, get the permutation.
For the mapping, perhaps this will be useful: http://en.wikipedia.org/wiki/Permutation#Numbering_permutations
Of course, this would get out of hand quickly, as n! can become really large soon.
Generating a random number takes long time you say? The implementation of Javas Random.nextInt is roughly
oldseed = seed;
nextseed = (oldseed * multiplier + addend) & mask;
return (int)(nextseed >>> (48 - bits));
Is that too much work to do for each element?
See https://doi.org/10.1145/3009909 for a careful analysis of the number of random bits required to generate a random permutation. (It's open-access, but it's not easy reading! Bottom line: if carefully implemented, all of the usual methods for generating random permutations are efficient in their use of random bits.)
And... if your goal is to generate a random permutation rapidly for large N, I'd suggest you try the MergeShuffle algorithm. An article published in 2015 claimed a factor-of-two speedup over Fisher-Yates in both parallel and sequential implementations, and a significant speedup in sequential computations over the other standard algorithm they tested (Rao-Sandelius).
An implementation of MergeShuffle (and of the usual Fisher-Yates and Rao-Sandelius algorithms) is available at https://github.com/axel-bacher/mergeshuffle. But caveat emptor! The authors are theoreticians, not software engineers. They have published their experimental code to github but aren't maintaining it. Someday, I imagine someone (perhaps you!) will add MergeShuffle to GSL. At present gsl_ran_shuffle() is an implementation of Fisher-Yates, see https://www.gnu.org/software/gsl/doc/html/randist.html?highlight=gsl_ran_shuffle.
Not what you asked exactly, but if provided random number generator doesn't satisfy you, may be you should try something different. Generally, pseudorandom number generation can be very simple.
Probably, best-known algorithm
http://en.wikipedia.org/wiki/Linear_congruential_generator
More
http://en.wikipedia.org/wiki/List_of_pseudorandom_number_generators
As other answers suggest, you can make a random integer in the range 0 to N! and use it to produce a shuffle. Although theoretically correct, this won't be faster in general since N! grows fast and you'll spend all your time doing bigint arithmetic.
If you want speed and you don't mind trading off some randomness, you will be much better off using a less good random number generator. A linear congruential generator (see http://en.wikipedia.org/wiki/Linear_congruential_generator) will give you a random number in a few cycles.
Usually there is no need in full-range of next random value, so to use exactly the same amount of randomness you can use next approach (which is almost like random(0,N!), I guess):
// ...
m = 1; // range of random buffer (single variant)
r = 0; // random buffer (number zero)
// ...
for(/* ... */) {
while (m < n) { // range of our buffer is too narrow for "n"
r = r*RAND_MAX + random(); // add another random to our random-buffer
m *= RAND_MAX; // update range of random-buffer
}
x = r % n; // pull-out next random with range "n"
r /= n; // remove it from random-buffer
m /= n; // fix range of random-buffer
// ...
}
P.S. of course there will be some errors related with division by value different from 2^n, but they will be distributed among resulted samples.
Generate N numbers (N < of the number of random number you need) before to do the computation, or store them in an array as data, with your slow but good random generator; then pick up a number simply incrementing an index into the array inside your computing loop; if you need different seeds, create multiple tables.
Are you sure that your mathematical and algorithmical approach to the problem is correct?
I hit exactly same problem where Fisher–Yates shuffle will be bottleneck in corner cases. But for me the real problem is brute force algorithm that doesn't scale well to all problems. Following story explains the problem and optimizations that I have come up with so far.
Dealing cards for 4 players
Number of possible deals is 96 bit number. That puts quite a stress for random number generator to avoid statical anomalies when selecting play plan from generated sample set of deals. I choose to use 2xmt19937_64 seeded from /dev/random because of the long period and heavy advertisement in web that it is good for scientific simulations.
Simple approach is to use Fisher–Yates shuffle to generate deals and filter out deals that don't match already collected information. Knuth shuffle takes ~1400 CPU cycles per deal mostly because I have to generate 51 random numbers and swap 51 times entries in the table.
That doesn't matter for normal cases where I would only need to generate 10000-100000 deals in 7 minutes. But there is extreme cases when filters may select only very small subset of hands requiring huge number of deals to be generated.
Using single number for multiple cards
When profiling with callgrind (valgrind) I noticed that main slow down was C++ random number generator (after switching away from std::uniform_int_distribution that was first bottleneck).
Then I came up with idea that I can use single random number for multiple cards. The idea is to use least significant information from the number first and then erase that information.
int number = uniform_rng(0, 52*51*50*49);
int card1 = number % 52;
number /= 52;
int cards2 = number % 51;
number /= 51;
......
Of course that is only minor optimization because generation is still O(N).
Generation using bit permutations
Next idea was exactly solution asked in here but I ended up still with O(N) but with larger cost than original shuffle. But lets look into solution and why it fails so miserably.
I decided to use idea Dealing All the Deals by John Christman
void Deal::generate()
{
// 52:26 split, 52!/(26!)**2 = 495,918,532,948,1041
max = 495918532948104LU;
partner = uniform_rng(eng1, max);
// 2x 26:13 splits, (26!)**2/(13!)**2 = 10,400,600**2
max = 10400600LU*10400600LU;
hands = uniform_rng(eng2, max);
// Create 104 bit presentation of deal (2 bits per card)
select_deal(id, partner, hands);
}
So far good and pretty good looking but select_deal implementation is PITA.
void select_deal(Id &new_id, uint64_t partner, uint64_t hands)
{
unsigned idx;
unsigned e, n, ns = 26;
e = n = 13;
// Figure out partnership who owns which card
for (idx = CARDS_IN_SUIT*NUM_SUITS; idx > 0; ) {
uint64_t cut = ncr(idx - 1, ns);
if (partner >= cut) {
partner -= cut;
// Figure out if N or S holds the card
ns--;
cut = ncr(ns, n) * 10400600LU;
if (hands > cut) {
hands -= cut;
n--;
} else
new_id[idx%NUM_SUITS] |= 1 << (idx/NUM_SUITS);
} else
new_id[idx%NUM_SUITS + NUM_SUITS] |= 1 << (idx/NUM_SUITS);
idx--;
}
unsigned ew = 26;
// Figure out if E or W holds a card
for (idx = CARDS_IN_SUIT*NUM_SUITS; idx-- > 0; ) {
if (new_id[idx%NUM_SUITS + NUM_SUITS] & (1 << (idx/NUM_SUITS))) {
uint64_t cut = ncr(--ew, e);
if (hands >= cut) {
hands -= cut;
e--;
} else
new_id[idx%NUM_SUITS] |= 1 << (idx/NUM_SUITS);
}
}
}
Now that I had the O(N) permutation solution done to prove algorithm could work I started searching for O(1) mapping from random number to bit permutation. Too bad it looks like only solution would be using huge lookup tables that would kill CPU caches. That doesn't sound good idea for AI that will be using very large amount of caches for double dummy analyzer.
Mathematical solution
After all hard work to figure out how to generate random bit permutations I decided go back to maths. It is entirely possible to apply filters before dealing cards. That requires splitting deals to manageable number of layered sets and selecting between sets based on their relative probabilities after filtering out impossible sets.
I don't yet have code ready for that to tests how much cycles I'm wasting in common case where filter is selecting major part of deal. But I believe this approach gives the most stable generation performance keeping the cost less than 0.1%.
Generate a 32 bit integer. For each index i (maybe only up to half the number of elements in the array), if bit i % 32 is 1, swap i with n - i - 1.
Of course, this might not be random enough for your purposes. You could probably improve this by not swapping with n - i - 1, but rather by another function applied to n and i that gives better distribution. You could even use two functions: one for when the bit is 0 and another for when it's 1.
Related
I want to sort a huge array, say 10^8 entries of type X with at most N different keys, where N is ~10^2. Because I don't know the range or spacing of the elements, count sort is not an option. So my best guess so far is to use a hash map for the counts like so
std::unordered_map< X, unsigned > counts;
for (auto x : input)
counts[x]++;
This works ok-ish and is ~4 times faster than 3-way quicksort, but I'm a nervous person and it's still not fast enough.
I wonder: am I missing something? Can I make better use of the fact that N is known in advance? Or is it possible to tune the hash map to my needs?
EDIT An additional pre-condition is that the input sequence is badly sorted and the frequency of the keys is about the same.
STL implementations are often not perfect in terms of performance (no holy wars, please).
If you know a guaranteed and sensible upper on the number of unique elements (N), then you can trivially implement your own hash table of size 2^s >> N. Here is how I usually do it myself:
int size = 1;
while (size < 3 * N) size <<= 1;
//Note: at least 3X size factor, size = power of two
//count = -1 means empty entry
std::vector<std::pair<X, int>> table(size, make_pair(X(), -1));
auto GetHash = [size](X val) -> int { return std::hash<X>()(val) & (size-1); };
for (auto x : input) {
int cell = GetHash(x);
bool ok = false;
for (; table[cell].second >= 0; cell = (cell + 1) & (size-1)) {
if (table[cell].first == x) { //match found -> stop
ok = true;
break;
}
}
if (!ok) { //match not found -> add entry on free place
table[cell].first = x;
table[cell].second = 0;
}
table[cell].second++; //increment counter
}
On MSVC2013, it improves time from 0.62 secs to 0.52 secs compared to your code, given that int is used as type X.
Also, we can choose a faster hash function. Note however, that the choice of hash function depends heavily on the properties of the input. Let's take Knuth's multiplicative hash:
auto GetHash = [size](X val) -> int { return (val*2654435761) & (size-1); };
It further improves time to 0.34 secs.
As a conclusion: do you really want to reimplement standard data structures to achieve a 2X speed boost?
Notes: Speedup may be entirely different on another compiler/machine. You may have to do some hacks if your type X is not POD.
Counting sort really would by best, but isnt applicable due to unknown range or spacing.
Seems to be easily parallelized with fork-join, e.g. boost::thread.
You could also try a more efficient, handrolled hashmap. Unorded_map typically uses linked lists to counter potentially bad hash functions. The memory overhead of linked lists may hurt performance if the hashtable doesnt fit into L1 cache. Closed Hashing may use less memory. Some hints for optimizing:
Closed Hashing with linear probing and without support for removal
power of two sized hashtable for bit shifting instead of modulo (division requires multiple cycles and there is only one hardware divider per core)
Low LoadFactor (entries through size) to minimize collisions. Thats a tradeof between memory usage and number of collisions. A LoadFactor over 0.5 should be avoided. A hashtable-size of 256 seems suitable for 100 entries.
cheapo hash function. You havent shown the type of X, so perhaps a cheaper hash function could outweigh more collisions.
I would look to store items in a sorted vector, as about 100 keys, would mean inserting into the vector would only occur 1 in 10^6 entries. Lookup would be processor efficient bsearch in vector
Given are an iterator it over data points, the number of data points we have n, and the maximum number of samples we want to use to do some calculations (maxSamples).
Imagine a function calculateStatistics(Iterator it, int n, int maxSamples). This function should use the iterator to retrieve the data and do some (heavy) calculations on the data element retrieved.
if n <= maxSamples we will of course use each element we get from the iterator
if n > maxSamples we will have to choose which elements to look at and which to skip
I've been spending quite some time on this. The problem is of course how to choose when to skip an element and when to keep it. My approaches so far:
I don't want to take the first maxSamples coming from the iterator, because the values might not be evenly distributed.
Another idea was to use a random number generator and let me create maxSamples (distinct) random numbers between 0 and n and take the elements at these positions. But if e.g. n = 101 and maxSamples = 100 it gets more and more difficult to find a new distinct number not yet in the list, loosing lot of time just in the random number generation
My last idea was to do the contrary: to generate n - maxSamples random numbers and exclude the data elements at these positions elements. But this also doesn't seem to be a very good solution.
Do you have a good idea for this problem? Are there maybe standard known algorithms for this?
To provide some answer, a good way to collect a set of random numbers given collection size > elements needed, is the following. (in C++ ish pseudo code).
EDIT: you may need to iterate over and create the "someElements" vector first. If your elements are large they can be "pointers" to these elements to save space.
vector randomCollectionFromVector(someElements, numElementsToGrab) {
while(numElementsToGrab--) {
randPosition = rand() % someElements.size();
resultVector.push(someElements.get(randPosition))
someElements.remove(randPosition);
}
return resultVector;
}
If you don't care about changing your vector of elements, you could also remove random elements from someElements, as you mentioned. The algorithm would look very similar, and again, this is conceptually the same idea, you just pass someElements by reference, and manipulate it.
Something worth noting, is the quality of psuedo random distributions as far as how random they are, grows as the size of the distribution you used increases. So, you may tend to get better results if you pick which method you use based on which method results in the use of more random numbers. Example: if you have 100 values, and need 99, you should probably pick 99 values, as this will result in you using 99 pseudo random numbers, instead of just 1. Conversely, if you have 1000 values, and need 99, you should probably prefer the version where you remove 901 values, because you use more numbers from the psuedo random distribution. If what you want is a solid random distribution, this is a very simple optimization, that will greatly increase the quality of "fake randomness" that you see. Alternatively, if performance matters more than distribution, you would take the alternative or even just grab the first 99 values approach.
interval = n/(n-maxSamples) //an euclidian division of course
offset = random(0..(n-1)) //a random number between 0 and n-1
totalSkip = 0
indexSample = 0;
FOR it IN samples DO
indexSample++ // goes from 1 to n
IF totalSkip < (n-maxSamples) AND indexSample+offset % interval == 0 THEN
//do nothing with this sample
totalSkip++
ELSE
//work with this sample
ENDIF
ENDFOR
ASSERT(totalSkip == n-maxSamples) //to be sure
interval represents the distance between two samples to skip.
offset is not mandatory but it allows to have a very little diversity.
Based on the discussion, and greater understanding of your problem, I suggest the following. You can take advantage of a property of prime numbers that I think will net you a very good solution, that will appear to grab pseudo random numbers. It is illustrated in the following code.
#include <iostream>
using namespace std;
int main() {
const int SOME_LARGE_PRIME = 577; //This prime should be larger than the size of your data set.
const int NUM_ELEMENTS = 100;
int lastValue = 0;
for(int i = 0; i < NUM_ELEMENTS; i++) {
lastValue += SOME_LARGE_PRIME;
cout << lastValue % NUM_ELEMENTS << endl;
}
}
Using the logic presented here, you can create a table of all values from 1 to "NUM_ELEMENTS". Because of the properties of prime numbers, you will not get any duplicates until you rotate all the way around back to the size of your data set. If you then take the first "NUM_SAMPLES" of these, and sort them, you can iterate through your data structure, and grab a pseudo random distribution of numbers(not very good random, but more random than a pre-determined interval), without extra space and only one pass over your data. Better yet, you can change the layout of the distribution by grabbing a random prime number each time, again must be larger than your data set, or the following example breaks.
PRIME = 3, data set size = 99. Won't work.
Of course, ultimately this is very similar to the pre-determined interval, but it inserts a level of randomness that you do not get by simply grabbing every "size/num_samples"th element.
This is called the Reservoir sampling
I am looking for an algorithm to find if a given number is a perfect number.
The most simple that comes to my mind is :
Find all the factors of the number
Get the prime factors [except the number itself, if it is prime] and add them up to check if it is a perfect number.
Is there a better way to do this ?.
On searching, some Euclids work came up, but didnt find any good algorithm. Also this golfscript wasnt helpful: https://stackoverflow.com/questions/3472534/checking-whether-a-number-is-mathematically-a-perfect-number .
The numbers etc can be cached etc in real world usage [which I dont know where perfect nos are used :)]
However, since this is being asked in interviews, I am assuming there should be a "derivable" way of optimizing it.
Thanks !
If the input is even, see if it is of the form 2^(p-1)*(2^p-1), with p and 2^p-1 prime.
If the input is odd, return "false". :-)
See the Wikipedia page for details.
(Actually, since there are only 47 perfect numbers with fewer than 25 million digits, you might start with a simple table of those. Ask the interviewer if you can assume you are using 64-bit numbers, for instance...)
Edit: Dang, I failed the interview! :-(
In my over zealous attempt at finding tricks or heuristics to improve upon the "factorize + enumerate divisors + sum them" approach, I failed to note that being 1 modulo 9 was merely a necessary, and certainly not a sufficient condition for at number (other than 6) to be perfect...
Duh... with on average 1 in 9 even number satisfying this condition, my algorithm would sure find a few too many perfect numbers ;-).
To redeem myself, persist and maintain the suggestion of using the digital root, but only as a filter, to avoid the more expensive computation of the factor, in most cases.
[Original attempt: hall of shame]
If the number is even,<br>
compute its [digital root][1].
if the digital root is 1, the number is perfect, otherwise it isn't.
If the number is odd...
there are no shortcuts, other than...
"Not perfect" if the number is smaller than 10^300
For bigger values, one would then need to run the algorithm described in
the question, possibly with a few twists typically driven by heuristics
that prove that the sum of divisors will be lacking when the number
doesn't have some of the low prime factors.
My reason for suggesting the digital root trick for even numbers is that this can be computed without the help of an arbitrary length arithmetic library (like GMP). It is also much less computationally expensive than the decomposition in prime factors and/or the factorization (2^(p-1) * ((2^p)-1)). Therefore if the interviewer were to be satisfied with a "No perfect" response for odd numbers, the solution would be both very efficient and codable in most computer languages.
[Second and third attempt...]
If the number is even,<br>
if it is 6
The number is PERFECT
otherwise compute its [digital root][1].
if the digital root is _not_ 1
The number is NOT PERFECT
else ...,
Compute the prime factors
Enumerate the divisors, sum them
if the sum of these divisor equals the 2 * the number
it is PERFECT
else
it is NOT PERFECT
If the number is odd...
same as previously
On this relatively odd interview question...
I second andrewdski's comment to another response in this post, that this particular question is rather odd in the context of an interview for a general purpose developer. As with many interview questions, it can be that the interviewer isn't seeking a particular solution, but rather is providing an opportunity for the candidate to demonstrate his/her ability to articulate the general pros and cons of various approaches. Also, if the candidate is offered an opportunity to look-up generic resources such as MathWorld or Wikipedia prior to responding, this may also be a good test of his/her ability to quickly make sense of the info offered there.
Here's a quick algorithm just for fun, in PHP - using just a simple for loop. You can easliy port that to other languages:
function isPerfectNumber($num) {
$out = false;
if($num%2 == 0) {
$divisors = array(1);
for($i=2; $i<$num; $i++) {
if($num%$i == 0)
$divisors[] = $i;
}
if(array_sum($divisors) == $num)
$out = true;
}
return $out ? 'It\'s perfect!' : 'Not a perfect number.';
}
Hope this helps, not sure if this is what you're looking for.
#include<stdio.h>
#include<stdlib.h>
int sumOfFactors(int );
int main(){
int x, start, end;
printf("Enter start of the range:\n");
scanf("%d", &start);
printf("Enter end of the range:\n");
scanf("%d", &end);
for(x = start;x <= end;x++){
if(x == sumOfFactors(x)){
printf("The numbers %d is a perfect number\n", x);
}
}
return 0;
}
int sumOfFactors(int x){
int sum = 1, i, j;
for(j=2;j <= x/2;j++){
if(x % j == 0)
sum += j;
}
return sum;
}
This question already has answers here:
Which is the fastest algorithm to find prime numbers?
(20 answers)
Closed 9 years ago.
First of all - I checked a lot in this forum and I haven't found something fast enough.
I try to make a function that returns me the prime numbers in a specified range.
For example I did this function (in C#) using the sieve of Eratosthenes. I tried also Atkin's sieve but the Eratosthenes one runs faster (in my implementation):
public static void SetPrimesSieve(int Range)
{
Primes = new List<uint>();
Primes.Add(2);
int Half = (Range - 1) >> 1;
BitArray Nums = new BitArray(Half, false);
int Sqrt = (int)Math.Sqrt(Range);
for (int i = 3, j; i <= Sqrt; )
{
for (j = ((i * i) >> 1) - 1; j < Half; j += i)
Nums[j] = true;
do
i += 2;
while (i <= Sqrt && Nums[(i >> 1) - 1]);
}
for (int i = 0; i < Half; ++i)
if (!Nums[i])
Primes.Add((uint)(i << 1) + 3);
}
It runs about twice faster than codes & algorithms I found...
There's should be a faster way to find prime numbers, could you help me?
When searching around for algorithms on this topic (for project Euler) I don't remember finding anything faster. If speed is really the concern, have you thought about just storing the primes so you simply need to look it up?
EDIT: quick google search found this, confirming that the fastest method would be just to page the results and look them up as needed.
One more edit - you may find more information here, essentially a duplicate of this topic. Top post there states that atkin's sieve was faster than eras' as far as generating on the fly.
The fastest algorithm in my experience so far is the Sieve of Erathostenes with wheel factorization for 2, 3 and 5, where the primes among the remaining numbers are represented as bits in a byte array. In Java on one core of my 3 year old Laptop it takes 23 seconds to compute the primes up to 1 billion.
With wheel factorization the Sieve of Atkin was about a factor of two slower, while with an ordinary BitSet it was about 30% faster.
See also this answer.
I did an algorithm that can find prime numbers from range 2-90 000 000 for 0.65 sec on I 350M-notebook, written in C .... you have to use bitwise operations and have "code" for recalculating index of your array to index of concrete bit you want. for example If you want folds of number 2, concrete bits will be for example ....10101000 ... so if you read from left ... you get index 4,6,8 ... thats it
Several comments.
For speed, precompute then load from disk. It's super fast. I did it in Java long ago.
Don't store as an array, store as a bitsequence for odd numbers. Way more efficient on memory
If your speed question is that you want this particular computation to run fast (you need to justify why you can't precompute and load it from disk) you need to code a better Atkin's sieve. It is faster. But only slightly.
You haven't indicated the end use for these primes. We may be missing something completely because you've not told us the application. Tell us a sketch of the application and the answers will be targetted better for your context.
Why on earth do you think something faster exists? You haven't justified your hunch. This is a very hard problem. (that is to find something faster)
You can do better than that using the Sieve of Atkin, but it is quite tricky to implement it fast and correctly. A simple translation of the Wikipedia pseudo-code is probably not good enough.
Closed. This question needs to be more focused. It is not currently accepting answers.
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Closed 6 years ago.
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I want to generate random numbers manually. I know that every language have the rand or random function, but I'm curious to know how this is working.
Does anyone have code for that?
POSIX.1-2001 gives the following example of an implementation of rand() and srand(), possibly useful when one needs the same sequence on two different machines.
static unsigned long next = 1;
/* RAND_MAX assumed to be 32767 */
int myrand(void) {
next = next * 1103515245 + 12345;
return((unsigned)(next/65536) % 32768);
}
void mysrand(unsigned seed) {
next = seed;
}
Have a look at the following:
Random Number Generation
Linear Congruential Generator - a popular approach also used in Java
List of Random Number Generators
And here's another link which elaborates on the use of LCG in Java's Random class
static void Main()
{
DateTime currentTime = DateTime.Now;
int maxValue = 100;
int hour = currentTime.Hour;
int minute = currentTime.Minute;
int second = currentTime.Second;
int milisecond = currentTime.Millisecond;
int randNum = (((hour + 1) * (minute + 1) * (second + 1) * milisecond) % maxValue);
Console.WriteLine(randNum);
Console.ReadLine();
}
Above shows a very simple piece of code to generate random numbers. It is a console program written in C#. If you know any kind of basic programming this should be understandable and easy to convert to any other language desired.
The DateTime simply takes in a current date and time, most programming languages have a facility to do this.
The hour, minute, second and milisecond variables break the date time value it up into its component parts. We are only interested in these parts so can ignore day. Again, in most languages dates and times are usually presented as strings. In .Net we have facilities that allow us to parse this information easily. But in most other languages where times are presented as strings, its is not overly difficult to parse the string for the parts that you want and convert them to their numbers. These facilities are usually provided even in the oldest of languages.
The seed essentially gives us a starting number which always changes. Traditionally you would just multiply this number by a decimal value between 0 and 1 this cuts out that step.
The upperRange defines the maximum value. So the number generated will never be above this value. Also it will never be below 0. So no ngeatives. But if you want negatives you could just negate it manually. (by multiplying it by -1)
The actual variable randNumis what holds the random value you are interested in.
The trick is to get the remainder (the modulus) after dividing the seed by the upper range. The remainder will always be smaller than the divisor which in this case is 100. Simple maths tells you that you cant have a remainder greater than the divisor. So if you divide by 10 you cant have a remainder greater than 10. It is this simple law that gets us our random number between 0 and 100 in this case.
The console.writeline simply outputs it to the screen.
The console.readline simply pauses the program so you can see it.
This is a very simple piece of code to generate random numbers. If you ran this program at the exact same intervil every day (but you would have to do it at the same hour, minute, second and milisecond) for more than 1 day you would begin to generate the same set of numbers again and again each additional day. This is because it is tied to the time. That is the resolution of the generator. So if you know the code of this program, and the time it is run at, you can predict the number generated, but it wont be easy. That is why I used miliseconds. Use seconds or minutes only to see what I mean. So you could write a table showing when 1 goes in, 0 comes out, when 2 goes in 0 comes out and so on. You could then predict the output for every second, and the range of numbers generated. The more you increase the resolution (by increasing the numbers that change) the harder it is and the longer it takes to get a predictable pattern. This method is good enough for most peoples use.
That is the old school way of doing random number generation for basic games. It needed to be fast, and simple. It is. This also highlights exactly why, random numbers genaerators are not really random but psudo random.
I hope this is a reasonable answer to your question.
I assume you mean pseudo-random numbers. The simplest one I know (from writing videogames games back on old machines) worked like this:
seed=seed*5+1;
You do that every time random is called and then you use however many low bits you want. *5+1 has the nice property (IIRC) of hitting every possibility before repeating, no matter how many bits you are looking at.
The downside, of course, is its predictability. But that didn't matter in the games. We were grabbing random numbers like crazy for all sorts of things, and you'd never know what number was coming next.
Do a couple things like this in parallel, and combine the results. This is a linear congruential generator.
http://en.wikipedia.org/wiki/Random_number_generator
Describes the different types of random number generators and how they are created.
Aloha!
By manually do you mean "not using computer" or "write my own code"?
IF it is not using computer you can use things like dice, numbers in a bag and all those methods seen on telly when they select teams, winning Bingo series etc. Las Vegas is filled with these kinds of method used in processes (games) aimed at giving you bad odds and ROI. You can also get the great RAND book and turn to a randomly selected page:
http://www.amazon.com/Million-Random-Digits-Normal-Deviates/dp/0833030477
(Also, for some amusement, read the reviews)
For writing your own code you need to consider why not using the system provided RNG is not good enough. If you are using a modern OS it will have a RNG available for user programs that should be good enough for your application.
If you really need to implement your own there are a huge bunch of generators available. For non security usage you can look at LFSR chains, Congruential generators etc. Whatever the distribution you need (uniform, normal, exponential etc) you should be able to find algorithm descriptions and libraries with implementations.
For security usage you should look at things like Yarrow/Fortuna the NIST SP 800-89 specified PRNGs and RFC 4086 for good entropy sources needed to feed the PRNG. Or even better, use the one in the OS that should meet security RNG requirements.
Implementation of RNGs can be a fun exercise, but is very rarely needed. And don't invent your own algorithm unless it is for toy applications. Do NOT, repeat NOT invent RNGs for security applications (generating cryptographic keys for example), at least unless you do some seripus reading and investigation. You will thank me for it (I hope).
hopefuly im not redundant because i havent read all the links, but i believe you can get pretty close to true random generator. nowadays systems are often so complex that even the best geeks around need a lot of time to understand whats happening inside :) just open your mind and think if you can monitor some global system property, use it to seed to ... pick a network packet (not intended for you?) and compute "something" out of its content and use it to seed to ... etc. you can design the best for your needs with all those hints around ;)
The Mersenne twister has a very long period (2^19937-1).
Here's a very basic implementation in C++:
struct MT{
unsigned int *mt, k, g;
~MT(){ delete mt; }
MT(unsigned int seed) : mt(new unsigned int[624]), k(0), g(0){
for (int i=0; i<624; i++)
mt[i]=!i?seed:(1812433253U*(mt[i-1]^(mt[i-1]>>30))+i);
}
unsigned int operator()(){
unsigned int q=(mt[k]&0x80000000U)|(mt[(k+1)%624]&0x7fffffffU);
mt[k]=mt[(k+397)%624]^(q>>1)^((q&1)?0x9908b0dfU:0);
unsigned int y = mt[k];
y ^= (y >> 11);
y ^= (y << 7) & 0x9d2c5680U;
y ^= (y << 15) & 0xefc60000U;
y ^= (y >> 18);
k = (k+1)%624;
return y;
}
};
One good way to get random numbers is to monitor the ambient level of noise coming through your computer's microphone. If you can get a driver (or language that supports mic input) and convert this to a number, you're well on your way!
It has also been researched in how to get "true randomness" - since computers are nothing more than binary machines, they can't give us "true randomness". After a while, the sequence will begin to repeat itself. The quest for better random number generation is still going, but they say monitoring ambient noise levels in a room is one good way to prevent pattern forming in your random generation.
You can look up this wiki article for more information on the science behind random number generation.
If you are looking for a theoretical treatment on random numbers, probably you can have a look at Volume 2 of the The art of computer programming. It has a chapter dedicated to random numbers. See if it helps you out.
If you are wanting to manually, hard code, your own random generator I can't give you efficiency, however, I can give you reliability. I actually decided to write some code using time to test a computer's processing speed by counting in time and that turned into me writing my own random number generator using the counting algorithm for modulo (the count is random). Please, try it for yourselves and test on number distributions within a large test-set. By the way, this is written in python.
def count_in_time(n):
import time
count = 0
start_time = time.clock()
end_time = start_time + n
while start_time < end_time:
count += 1
start_time += (time.clock() - start_time)
return count
def generate_random(time_to_count, range_nums, rand_lst_size):
randoms = []
iterables = range(range_nums)
count = 0
for i in range(rand_lst_size):
count += count_in_time(time_to_count)
randoms.append(iterables[count%len(iterables)])
return randoms
This document is a very nice write up of pseudo-random number generation and has a number of routines included (in C). It also discusses the need for appropriate seeding of the random number generators (see rule 3). Particularly useful for this is the use of /dev/randon/ (if you are on a linux machine).
Note: the routines included in this document are alot simpler to code up than the Mersenne Twister. See also the WELLRNG generator, which is supposed to have better theoretical properties, as an alternative to the MT.
Read the rands book of random numbers (monte carlo book of random numbers) the numbers in it are randomly generated for you!!! My grandfather worked for rand.
Most RNGs(random number generators) will require a small bit of initialization. This is usually to perform a seeding operation and store the results of the seeded values for later use. Here is an example of a seeding method from a randomizer I wrote for a game engine:
/// <summary>
/// Initializes the number array from a seed provided by <paramref name="seed">seed</paramref>.
/// </summary>
/// <param name="seed">Unsigned integer value used to seed the number array.</param>
private void Initialize(uint seed)
{
this.randBuf[0] = seed;
for (uint i = 1; i < 100; i++)
{
this.randBuf[i] = (uint)(this.randBuf[i - 1] >> 1) + i;
}
}
This is called from the constructor of the randomizing class. Now the real random numbers can be rolled/calculated using the aforementioned seeded values. This is usually where the actual randomizing algorithm is applied. Here is another example:
/// <summary>
/// Refreshes the list of values in the random number array.
/// </summary>
private void Roll()
{
for (uint i = 0; i < 99; i++)
{
uint y = this.randBuf[i + 1] * 3794U;
this.randBuf[i] = (((y >> 10) + this.randBuf[i]) ^ this.randBuf[(i + 399) % 100]) + i;
if ((this.randBuf[i] % 2) == 1)
{
this.randBuf[i] = (this.randBuf[i + 1] << 21) ^ (this.randBuf[i + 1] * (this.randBuf[i + 1] & 30));
}
}
}
Now the rolled values are stored for later use in this example, but those numbers can also be calculated on the fly. The upside to precalculating is a slight performance increase. Depending on the algorithm used, the rolled values could be directly returned or go through some last minute calculations when requested by the code. Here is an example that takes from the prerolled values and spits out a very good looking pseudo random number:
/// <summary>
/// Retrieves a value from the random number array.
/// </summary>
/// <returns>A randomly generated unsigned integer</returns>
private uint Random()
{
if (this.index == 0)
{
this.Roll();
}
uint y = this.randBuf[this.index];
y = y ^ (y >> 11);
y = y ^ ((y << 7) + 3794);
y = y ^ ((y << 15) + 815);
y = y ^ (y >> 18);
this.index = (this.index + 1) % 100;
return y;
}