From c3cd051095e25822f2859aeb663e995d1bfc9c7f Mon Sep 17 00:00:00 2001
From: Enzo De Carvalho Bittencourt <enzo.decarvalhobittencourt@ensiie.eu>
Date: Fri, 22 Sep 2023 17:23:56 +0200
Subject: [PATCH] added RNG + creating plots
---
Random/.generators.py.swp | Bin 12288 -> 0 bytes
Random/__pycache__/generators.cpython-36.pyc | Bin 0 -> 7548 bytes
Random/generators.py | 165 ++++++++++++++++++-
Random/graph.py | 0
Random/main.py | 29 +++-
5 files changed, 183 insertions(+), 11 deletions(-)
delete mode 100644 Random/.generators.py.swp
create mode 100644 Random/__pycache__/generators.cpython-36.pyc
create mode 100644 Random/graph.py
diff --git a/Random/.generators.py.swp b/Random/.generators.py.swp
deleted file mode 100644
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diff --git a/Random/generators.py b/Random/generators.py
index 3534ead..2d8a444 100644
--- a/Random/generators.py
+++ b/Random/generators.py
@@ -1,37 +1,188 @@
## Various Random Generators ##
+import random
+
class RandomNumberGenerator:
def __init__(self):
return NotImplementedError
- def random():
+ def random(self):
"""
yield a number from the RNG
"""
return NotImplementedError
-
- def seed():
+
+ def seed(self):
"""
set the seed of the generator
"""
return NotImplementedError
+ def randomNorm(self):
+ """
+ return random between 0 and 1
+ """
+ return NotImplementedError
+
+ def batchRandomNormalized(self, n):
+ """
+ return n normalized values
+ """
+ return [self.randomNorm() for i in range(n)]
+
class LCG(RandomNumberGenerator):
"""
Linear Congruential generator algorithm
"""
- def __init__(self, m, c, a):
+ def __init__(self, a, c, m):
super().__init__()
self.m = m
self.c = c
self.a = a
- self.x = 0 #default seed
+ self.x = 0 # default seed
+
+ def random(self):
+ self.x = (self.a * self.x + self.c) % self.m
+ return self.x
+
+ def seed(self, seed):
+ self.x = seed
+
+ def randomNorm(self):
+ return self.random() / (self.m + 1)
- def random():
- self.x = self.a * self.x + self.c % self.m
+
+class ParkMiller(LCG):
+ """
+ Park and Miller's LCG
+ """
+
+ def __init__(self):
+ super().__init__(16807, 0, 2147483647)
+
+
+class KnuthLewis(LCG):
+ """
+ Knuth and Lewis' LCG
+ """
+
+ def __init__(self):
+ super().__init__(1664525, 1013904223, 4294967296)
+
+
+class Marsaglia(LCG):
+ """
+ Marsaglia's LCG
+ """
+
+ def __init__(self):
+ super().__init__(69069, 0, 4294967296)
+
+
+class LavauxJenssens(LCG):
+ """
+ Lavaux And Jenssens' LCG
+ """
+
+ def __init__(self):
+ super().__init__(31167285, 1, 281474976710656)
+
+
+class Haynes(LCG):
+ """
+ Haynes' LCG
+ """
+
+ def __init__(self):
+ super().__init__(6364136223846793005, 1, 18446744073709552000)
+class LaggedFibonnaci(RandomNumberGenerator):
+ """
+ Lagged Fibonnaci generator algorithm
+ """
+
+ def __init__(self, l, k, m):
+ self.l = l
+ self.k = k
+ self.m = m
+ self.ParkMiller = ParkMiller()
+ self.values = []
+
+ def seed(self, seed):
+ self.ParkMiller.seed(seed)
+ self.values = [self.ParkMiller.random() for i in range(max(self.l, self.k))]
+
+ def random(self):
+ # init values if non-existents
+ if not (self.values):
+ self.values = [self.ParkMiller.random() for i in range(max(self.l, self.k))]
+ val = (self.values[-self.l] + self.values[-self.k]) % self.m
+ self.values.append(val)
+ return self.values[-1]
+
+ def randomNorm(self):
+ return random() / (self.m + 1)
+
+
+class MitchelMoore(LaggedFibonnaci):
+ """
+ Mitchel and Moore's Lagged Fibonnaci
+ we use m = 2^32
+ """
+
+ def __init__(self):
+ super().__init__(24, 55, 4294967296)
+
+
+class MersenneTwister(RandomNumberGenerator):
+ """
+ MersenneTwister algorithm (native random.random())
+ """
+
+ def __init__(self):
+ super().__init__()
+
+ def seed(self, seed):
+ random.seed(seed)
+
+ def randomNorm(self):
+ return random.random()
+
+ def random(self):
+ return self.randomNorm()
+
+
+class CNS(RandomNumberGenerator):
+ """
+ Cryptography Network Security
+ """
+
+ def __init__(self, x0, M):
+ super().__init__()
+ self.M = M
+ self.x = x0 # must verify pgcd(M,x0) = 1
+
def seed(self, seed):
self.x = seed
+
+ def random(self):
+ self.x = pow(self.x, 2) % self.M
+ return self.x
+
+ def randomNorm(self):
+ return self.random() / (self.M + 1)
+
+
+class BlumBlumShub(CNS):
+ """
+ Blum Blum Shub
+ """
+
+ def __init__(self):
+ super().__init__(7817, 56923661)
+
+ def seed(self, seed):
+ return # do nothing
diff --git a/Random/graph.py b/Random/graph.py
new file mode 100644
index 0000000..e69de29
diff --git a/Random/main.py b/Random/main.py
index 3ab482e..3fc8978 100644
--- a/Random/main.py
+++ b/Random/main.py
@@ -1,7 +1,28 @@
-import generators
+from generators import *
+from datetime import datetime
+import matplotlib.pyplot as plt
+import numpy as np
+
if __name__=="__main__":
- LCG = generators.LCG(5, 1, 1)
- LCG.seed(0)
- print(LCG.random())
+ generators = [ParkMiller(), KnuthLewis(), Marsaglia()
+ , LavauxJenssens(), Haynes(), MitchelMoore()
+ , MersenneTwister(), BlumBlumShub()]
+
+ seed = datetime.now().timestamp()
+ N = 1000
+ RESOLUTION = 100
+
+ for e in generators:
+ e.seed(seed)
+ res = e.batchRandomNormalized(N)
+
+ #matplotlib prep
+ #see https://matplotlib.org/stable/gallery/statistics/histogram_features.html#sphx-glr-gallery-statistics-histogram-features-py
+ fig, ax = plt.subplots()
+ n, bins, patches = ax.hist(res, RESOLUTION, density=True)
+ ax.set_xlabel('Value')
+ ax.set_ylabel('Probability density')
+ ax.set_title(type(e).__name__)
+ plt.savefig(type(e).__name__+'.png')
--
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