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CS253 Random Numbers

Inclusion

To use C++ random numbers, you need to:

    
#include <random>

To use old C random numbers (don’t ), you need to:

    
#include <cstdlib>

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

Patron Saint of Randomness

• 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';

1737962885
2484516261
77122745

• 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';

289532883
218220617
320114672
1110491947
1217460870

• 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';

1833461605
929074791
1468236060
1967533966
220300564

• 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.

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: 1716209787190588233
1377155518
1317716493
1174693110
1452897422
290513636

• 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';

341808284
253073463
1388071581
1204204506
1179243014

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 hack your own distribution—it’s hard. Just use the standard distributions.

minstd_rand r;
int first_half = 0;
for (int i=0; i<100'000'000; i++)
    if (r() % 1'000'000'000 < 500'000'000)
        first_half++;
cout << first_half << '\n';

53435616

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';
}

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

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';
}

22.798 19.741 18.301 20.678 18.263 18.900 24.420 18.793 20.479 23.370 
22.940 18.659 20.197 19.553 24.772 18.743 20.091 22.064 21.711 23.978 
24.783 23.858 19.373 19.273 23.428 18.562 19.646 23.133 21.465 24.669 
21.754 18.801 24.071 20.075 18.824 19.587 19.852 21.235 20.956 23.840 
22.500 20.100 23.376 19.641 21.392 18.863 21.069 22.641 23.909 22.623 

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: 42.0885%

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';

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('A','~');
for (int y=0; y<8; y++) {
    string pw;
    for (int x=0; x<32; x++)
        pw += dist(gen);
    cout << "Password: " << pw << '\n';
}

Password: YVCO}wXXy|OwrzJjhZvwxzKaDgs^zum`
Password: Dt|h~iJfSbZrKf_NVWd{wctMb}_zTzjQ
Password: szrnIDsSFqf^jDHPrvvMX}lcunLdhBmB
Password: gRVDA^\`[B\`H[\y{}tpSgJNO_{fOyja
Password: |dEqFovcoiHB|HGPYPmFw|Sor]jX\cfC
Password: RjCZlovEJrwml{ws`NRpPAubtMFcLcDV
Password: oiARi}N{yuGKnIvMBWWHbKKG`|o`ON`f
Password: nMB]Gcit~}E[VheXeGBZMRtVXLJB|tIi

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.