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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';

2749403236
379734022
1203931365

• 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';

1606056402
1788924242
697156065
1351665125
1379085721

• 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';

1874249993
582847640
455171093
658637746
1754726978

• 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: 1725063758428930836
779963574
2085864997
2018480592
596108395
614948892

• 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';

1022712714
273473610
655958690
1664142779
420668125

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 1 5 6 2 6 6 5 2 6 3 4 3 4 2 3 1 5 3 3 4 6 6 1 1 3 1 1 1 1 4 6 2 2 4 1 2 1 3 5 
4 3 1 3 5 1 3 6 6 4 1 2 2 4 4 4 1 5 1 1 2 2 1 6 5 3 1 1 2 3 3 3 1 2 4 6 1 2 6 6 
6 5 3 3 2 5 6 5 6 3 3 2 5 4 5 6 6 6 4 2 2 3 1 4 5 5 2 6 6 1 5 2 6 6 2 5 3 3 5 2 
6 2 6 4 4 3 4 2 1 3 3 5 2 2 4 5 6 6 1 2 5 4 1 3 4 5 4 6 4 2 3 2 4 3 6 5 2 1 4 2 
1 6 2 4 4 3 3 4 6 5 5 4 3 5 1 4 3 5 4 2 3 1 4 1 1 6 6 4 6 1 1 1 6 2 3 3 5 1 6 6 
1 1 1 4 1 4 3 4 4 4 5 2 3 4 1 6 4 6 5 5 2 5 5 4 3 2 4 6 5 4 4 6 1 3 6 5 2 4 1 2 
1 3 3 4 2 6 3 1 5 6 2 1 3 1 6 3 2 4 1 3 6 3 2 3 4 3 5 3 5 6 3 5 4 2 1 4 6 5 5 5 
4 4 3 5 3 5 6 1 5 2 2 3 1 5 5 2 6 1 4 1 3 3 3 1 3 6 6 2 2 1 4 3 5 3 5 1 5 1 6 3 
2 5 6 4 6 5 2 6 1 4 4 2 4 5 4 1 5 6 4 4 2 1 5 4 4 6 4 3 5 5 6 5 4 2 3 2 3 3 3 4 
2 1 4 4 2 5 6 6 3 5 2 4 6 5 3 4 2 2 6 5 2 5 5 2 2 1 4 1 2 3 6 3 5 2 3 4 5 5 3 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';
}

22.020 19.373 18.102 23.924 21.933 22.331 24.650 18.349 20.854 20.074 
23.595 20.929 18.811 22.169 21.138 18.856 23.231 21.855 18.557 19.786 
19.068 20.307 24.300 20.144 24.569 19.815 18.610 23.748 18.821 20.384 
23.280 21.044 22.675 21.824 18.097 18.248 24.960 23.095 20.926 23.889 
20.986 18.012 19.657 19.398 20.712 24.570 22.401 22.615 24.806 24.527 

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: 42.0273%

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: 
28: 

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: sdAzow[fm^xxWWxVum`|^b~aQRevpAxG
Password: jneL~FYcGaYm\Xpvg|Z[_oAFrscvlxWD
Password: JRkfeMSGcJU]LZdYkxdbEc_Bn`Q\JC__
Password: HSuEMZomdxVVtAJJZFkimxZAKV^jqEgp
Password: FUlFz`oMYsea{G`|xFhjomHrxUzFUmFZ
Password: CiDXxBt{[ecEaFxyvhNNBkW\FQLLU~yi
Password: GK_QjASlclOOmPuVFNWaYDjskXD}zBV`
Password: Tk{sNlQReesmFsOtXK|}jSfOP\a~\x|f

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.