Generate professional pseudo-random number package in python.
Only the pseudo-random number algorithm with good statistical properties is implemented.
There are "methods" that specify the period of a multi-precision pseudo-random sequence.
| PRNG algorithms | Period |
|---|---|
| Quadratic Congruential Generator(QCG) | 2^256 |
| Cubic Congruential Generator(CCG) | 2^256 |
| Inversive Congruential Generator(ICG) | 102*2^256 |
| PCG64_XSL_RR | 2^128 |
| PCG64_DXSM | 2^128 |
| LCG64_32_ext | 2^320 |
| LCG128Mix_XSL_RR | 2^128 |
| LCG128Mix_DXSM | 2^128 |
| LCG128Mix_MURMUR3 | 2^128 |
| PhiloxCounter | 4*2^(4*64) |
| ThreeFryCounter | 4*2^(4*64) |
| AESCounter | 2^128 |
| ChaChaCounter | 2^128 |
| SPECKCounter | 2^129 |
| XSM64 | 2^128 |
| EFIIX64 | 2^64 |
| SplitMix64 | 2^64 |
| Ran64 | 2^64 |
Please inform me if any PRNG fails to pass the new statistical test.
Installation can be done through pip. You must have python version >= 3.8
pip install pure-prng
The statement to import the package:
from pure_prng_package import pure_prng
Example:
>>> seed = 170141183460469231731687303715884105727
>>> period = 115792089237316195423570985008687907853269984665640564039457584007913129639747
>>> prng_instance = pure_prng(seed)
>>> source_random_number = prng_instance.source_random_number()
>>> next(source_random_number)
65852230656997158461166665751696465914198450243194923777324019418213544382100
>>> prng_instance = pure_prng(seed, new_prng_period = 2 ** 512)
>>> source_random_number = prng_instance.source_random_number()
>>> next(source_random_number)
8375486648769878807557228126183349922765245383564825377649864304632902242469125910865615742661048315918259479944116325466004411700005484642554244082978452
>>> prng_instance = pure_prng(seed)
>>> rand_bits = prng_instance.rand_bits(512)
>>> next(rand_bits)
mpz(6144768950704661248519702670268583753928668607451020183407159490385670202458730311510261255705698403097105657582435836672179668357656056427608305574891156)
>>> rand_bits = prng_instance.rand_bits(512, period)
>>> next(rand_bits)
mpz(2954964798889411590155032615694646383408546750268072607273800792672971321854983100133610686738061114434885994588970398525439724215184541467422573311905001)
>>> prng_instance = pure_prng(seed)
>>> rand_float = prng_instance.rand_float(100)
>>> next(rand_float)
mpfr('0.56576176351048513846261940831522',100)
>>> prng_instance = pure_prng(seed)
>>> rand_int = prng_instance.rand_int(100, 1)
>>> next(rand_int)
mpz(21)
>>> prng_instance = pure_prng(seed)
>>> get_randint_set = prng_instance.get_randint_set(100, 1, 6)
>>> next(get_randint_set)
{mpz(34), mpz(99), mpz(37), mpz(45), mpz(19), mpz(21)}
>>> prng_instance = pure_prng(seed)
>>> rand_with_period = prng_instance.rand_with_period(period)
>>> next(rand_with_period)
mpz(65852230656997158461166665751696465914198450243194923777324019418213544381986)
>>> rand_with_period = prng_instance.rand_with_period(period, 'raw_binary_number')
>>> next(rand_with_period)
mpz(53067260390396280968027884646874354062063398901623645439544105836818444733296)
The following algorithm is intended to be implemented:
NLFSR
Cellular automate period pseudo random
GPU Philox