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CS253 Random Numbers

Philosophy

“Computers can’t do anything truly random. Only a person can do that.”

• Stop trying to prove your superiority.
  • If you believe that you have something special that distinguishes you from machines, you’re talking religion, not CS.
• My dog is pretty random.
• You’re somewhat predictable.
  • An online rock-paper-scissors program beats people 60% of the time over more than a million games, because people are lousy at being random.

Old Stuff

• There are several C random number generators, of varying degrees of standardization:
  • rand(), srand()
  • random(), srandom()
  • drand48(), erand48(), lrand48(), nrand48(), mrand48(), jrand48(), srand48()
• They still work ok, but avoid them for new C++ code.
• They mix up generation and distribution something terrible.
• Also, each family has a separate seeding function.
• Also also, there’s no way to save/restore state!

Traditional Method

Traditional random number generators work like this:

unsigned long n = 1;
for (int i=0; i<5; i++) {
    n = n * 16807 % 2147483647;
    cout << n << '\n';
}

16807
282475249
1622650073
984943658
1144108930

• It’s fast, simple, and good enough for many tasks. However …
  • What happens if n is zero?
  • What number always follows 16807?
  • How many possible states does this RNG (Random Number Generator) have?

Overview

• In C++, random numbers have:
  • Generators
    Generate uniformly-distributed random integers, typically zero or one to a big number.
  • Distributions
    Take uniformly-distributed random integers, and transform them into other distributions with different ranges.
• Examples:
  • Picking a card (uniform, but discrete)
  • Rolling 3d6 (bell-shaped, but discrete)
  • Human height (bell-shaped, continuous)

Generators

Default Engine

Define a random-number generator, and use () to generate a number. This is not a function call, because gen is an object, not a function. It’s operator().

#include <random>
#include <iostream>
using namespace std;

int main() {
    default_random_engine gen;
    for (int i=0; i<5; i++)
        cout << gen() << '\n';
}

16807
282475249
1622650073
984943658
1144108930

I won’t bother with the #includes in subsequent examples.

Mersenne Twister

• Here’s a different, 64-bit generator.
• Use .min() and .max() to find out the range of a given generator.

mt19937_64 gen;
cout << "range is " << gen.min() << "…" << gen.max() << "\n\n";
for (int i=0; i<3; i++)
    cout << gen() << '\n';

range is 0…18446744073709551615

14514284786278117030
4620546740167642908
13109570281517897720

Ranges

Generators have varying ranges:

ranlux24 rl;
minstd_rand mr;
random_device rd;
mt19937_64 mt;

cout << "ranlux24:      " << rl.min() << "…" << rl.max() << '\n'
     << "minstd_rand:   " << mr.min() << "…" << mr.max() << '\n'
     << "random_device: " << rd.min() << "…" << rd.max() << '\n'
     << "mt19937_64:    " << mt.min() << "…" << mt.max() << '\n';

ranlux24:      0…16777215
minstd_rand:   1…2147483646
random_device: 0…4294967295
mt19937_64:    0…18446744073709551615

Hey, look! Zero is not a possible return value for minstd_rand.

Save/Restore

A generator can save & restore state to an I/O stream:

ranlux24 gen;
cout << gen() << ' ';
cout << gen() << endl;
ofstream("state") << gen;
system("wc -c state");
cout << gen() << ' ';
cout << gen() << '\n';
ifstream("state") >> gen;
cout << gen() << ' ';
cout << gen() << '\n';

15039276 16323925
209 state
14283486 7150092
14283486 7150092

True randomness

random_device a, b, c;
cout << a() << '\n'
     << b() << '\n'
     << c() << '\n';

1269926981
4232381954
2269559708

• random_device is, ideally, truly random, and not pseudo-random.
• Intel computers have an RDRAND instruction.
• It might depend on random things like human typing intervals, network packets arrival times, or radioactive decay.
• If true randomness isn’t available, it resorts to pseudo-random numbers.
• It could pause waiting for randomness to become available.
• Use it sparingly.

Cloudflare

The hosting service Cloudflare uses a unique source of randomness.

Seeding

minstd_rand a, b, c(123);
cout << a() << ' ' << a() << '\n';
cout << b() << ' ' << b() << '\n';
cout << c() << ' ' << c() << '\n';

48271 182605794
48271 182605794
5937333 985676192

• Great—we can “seed” the random number generator with a value.
• This way, we can reproduce our pseudo-random sequences.
• Consider random testing: we want to be able to reproduce the sequence if we find an error.
• How to choose the random seed?
  • It should probably be … random.

Seed with process ID

auto seed = getpid();
minstd_rand a(seed);
for (int i=0; i<5; i++)
    cout << a() << '\n';

855524613
918062313
313371331
2020192880
1645583857

• You can seed with your process id.
• OK for casual use, but the seed is easily guessed.
• Process IDs are usually 15- or 16-bit quantities, so there are generally only 32768 or 65536 of them. Somebody could easily try them all.

Seed with time

// seconds since start of 1970
auto seed = time(nullptr);
minstd_rand a(seed);
for (int i=0; i<5; i++)
    cout << a() << '\n';

823798281
612130652
931203619
1149677392
804983458

• You can seed with a time-related value.
• Two runs may occur within the same second, and so produce identical random sequences.
• OK for casual use, but the seed is easily guessed.
• There are only 86,400 seconds in a day. Somebody could easily try them all.

Y2038

int biggest = 0x7fffffff;
time_t epoch = 0,
       now = time(nullptr),
       end = biggest,
       endp1 = biggest + 1;
cout << "epoch:" << setw(12) << epoch << ' ' << ctime(&epoch);
cout << "now:  " << setw(12) << now   << ' ' << ctime(&now);
cout << "end:  " << setw(12) << end   << ' ' << ctime(&end);
cout << "end+1:" << setw(12) << endp1 << ' ' << ctime(&endp1);

epoch:           0 Wed Dec 31 17:00:00 1969
now:    1729179387 Thu Oct 17 09:36:27 2024
end:    2147483647 Mon Jan 18 20:14:07 2038
end+1: -2147483648 Fri Dec 13 13:45:52 1901

I hope that nobody’s still using 32-bit signed time representations by then!

Seed with more accurate time

Nanoseconds make more possibilities:

auto seed = chrono::high_resolution_clock::now()
            .time_since_epoch().count();
cout << "Seed: " << seed << '\n';
minstd_rand a(seed);
for (int i=0; i<5; i++)
    cout << a() << '\n';

Seed: 1729179387396973085
701363558
440613263
164778385
1885477494
1379669367

• There are 86,400,000,000,000 nanoseconds in a day.

Better Seeding

• Many generators have more than 32 or 64 bits of state.
• Therefore, you can seed them with more than 32 or 64 bits.
  • If you’re doing something very important, and somebody guessing your seed, and hence predicting your sequence, would be catastrophic:
    • on-line poker 🂺 🂻 🂽 🂾 🂱
    • encryption of military communications ⚔️ 🔫 💣 🥆 ☢️
    • encrypted email re: extra-marital affairs 💔
• That’s beyond the scope of this discussion.

Seed with random_device

random_device rd;
auto seed = rd();
minstd_rand0 a(seed);
for (int i=0; i<5; i++)
    cout << a() << '\n';

1770846598
642908813
1378192034
514898896
1694131309

You can seed with random_device, if you know that it’s truly random.

Not good enough.

• Great, so we know how to generate a number 1…2,147,483,646
  or perhaps 0…18,446,744,073,709,551,615
• How often do we want to do that?
• Sometimes, we want integers with different ranges.
• Or, perhaps we want floating-point numbers.
• Maybe spread out linearly, or a bell-shaped curve, Poisson, etc.
• This is a job for a distribution.

Caution

• Resist the urge to create your own distribution using division or modulus.
• This is harder than you think.
• Your home-grown code will by off by one, or have some bias because the range of the generator isn’t a perfect multiple of what you want.
• Just use the standard distributions.

Distributions

uniform_int_distribution

auto seed = random_device()();  //❓❓❓
mt19937 gen(seed);
uniform_int_distribution<int> dist(1,6);
for (int y=0; y<10; y++) {
    for (int x=0; x<40; x++)
        cout << dist(gen) << ' ';
    cout << '\n';
}

6 5 1 1 3 2 6 6 5 4 3 4 2 3 1 1 4 2 5 6 1 6 3 4 6 3 6 4 4 2 3 5 1 5 4 6 5 6 1 4 
3 1 5 2 4 6 5 4 5 3 2 4 6 6 2 2 5 2 3 5 3 1 5 2 6 3 1 6 2 5 3 4 5 4 3 6 2 2 3 2 
1 6 3 2 5 2 1 5 2 5 2 5 6 5 4 1 5 1 4 6 4 1 1 1 6 2 5 1 6 4 3 2 4 6 1 3 1 2 1 5 
4 1 6 3 5 6 2 6 1 3 1 6 1 2 6 2 1 6 4 6 6 6 6 4 6 3 1 6 1 3 4 6 4 5 6 3 2 4 5 6 
4 5 4 6 6 4 1 1 2 4 6 6 6 6 5 2 4 5 3 6 6 4 4 4 2 5 4 6 5 4 5 6 3 5 5 4 5 5 1 3 
1 3 6 1 4 3 4 1 6 1 1 1 2 2 3 6 5 3 2 5 3 6 1 5 6 2 3 3 1 4 5 6 3 1 6 4 2 3 2 1 
4 6 5 6 4 3 4 5 5 4 4 4 1 3 3 4 4 4 2 5 6 1 4 1 5 1 6 5 4 6 1 2 4 4 4 3 6 3 4 6 
3 4 3 6 5 4 3 4 6 2 5 1 2 4 3 6 4 4 4 3 5 6 4 1 3 2 1 4 1 1 3 6 4 3 1 1 4 3 2 4 
3 3 3 2 1 5 1 5 5 2 6 4 3 6 4 4 1 6 1 4 3 3 6 4 1 3 5 2 2 3 6 4 6 6 3 5 1 4 6 3 
3 6 1 5 1 3 1 4 1 5 3 5 2 5 3 1 6 1 3 5 4 2 1 6 1 1 5 2 6 3 2 5 6 4 3 5 4 6 5 2 

uniform_real_distribution

auto seed = random_device()();
ranlux48 gen(seed);
uniform_real_distribution<> dist(18.0, 25.0);
for (int y=0; y<5; y++) {
    for (int x=0; x<10; x++)
        cout << fixed << setprecision(3) << dist(gen) << ' ';
    cout << '\n';
}

22.527 21.785 18.853 23.952 23.652 18.347 21.677 18.526 18.220 23.240 
19.226 19.138 23.130 19.569 19.907 20.334 24.213 20.162 19.260 18.690 
19.911 21.602 20.519 22.746 21.372 20.336 21.818 21.061 23.129 18.346 
22.552 21.195 20.791 22.064 23.465 23.866 19.286 22.409 24.095 23.149 
19.008 22.542 22.602 19.698 20.142 18.303 24.854 23.327 22.404 23.170 

Boolean Values

Yield true 42% of time:

random_device rd;
knuth_b gen(rd());
bernoulli_distribution dist(0.42);
constexpr int nrolls = 1'000'000;

int count=0;
for (int i=0; i<nrolls; i++)
    if (dist(gen))
        count++;

cout << "true: " << count*100.0/nrolls << "%\n";

true: 41.9794%

Histogram

random_device rd;
mt19937_64 gen(rd());
normal_distribution<> dist(21.5, 1.5);
map<int,int> tally;
for (int i=0; i<10000; i++)
    tally[dist(gen)]++;
for (auto p : tally)
    cout << p.first << ": " << string(p.second/100,'#') << '\n';

14: 
16: 
17: 
18: ###
19: ###########
20: #####################
21: #########################
22: #####################
23: ##########
24: ###
25: 
26: 
27: 

Passwords

random_device rd;
auto seed = rd();
ranlux24 gen(seed);
uniform_int_distribution<char> dist('!','~');
for (int y=0; y<8; y++) {
    string pw;
    for (int x=0; x<32; x++)
        pw += dist(gen);
    cout << "Password: " << pw << '\n';
}

Password: Ya#/z(NqlA.no"lohQDM%&BUc+;{8W_K
Password: $A)]<D3yX+ZOf)&cn;a!cA#Ar0+08!zO
Password: DfCg;nn3vkKiEq=bvIN13kf2GW4#,M4}
Password: ob)s;\<O(I]S>?:6""ia$w(2}@tI6&9<
Password: AV,`?)`yejtyb(0(>^~?&5r|v'!2m%o!
Password: !rY_S9*W0)ZyJdS<znVS%,--"@^MJ]@U
Password: 9W8C]sSg~w.TxVYGr})I&U>3a">xrARI
Password: WYQWi&26apA8N"+h]AU\x~W/TU2y+:ru

Even though we’re using uniform_int_distribution, which has int right there in its name, it’s uniform_int_distribution<char>, so we get characters. Think of them as 8-bit integers that display differently.