diff --git a/Random/__pycache__/generators.cpython-311.pyc b/Random/__pycache__/generators.cpython-311.pyc
index e579af00a05d62910d89bae5f79fead178f00e0d..1c7c6f91a25c41bbce2b366bb6cebb89630411f5 100644
Binary files a/Random/__pycache__/generators.cpython-311.pyc and b/Random/__pycache__/generators.cpython-311.pyc differ
diff --git a/Random/__pycache__/graph.cpython-311.pyc b/Random/__pycache__/graph.cpython-311.pyc
new file mode 100644
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Binary files /dev/null and b/Random/__pycache__/graph.cpython-311.pyc differ
diff --git a/Random/generators.py b/Random/generators.py
index bbd6b193f75ef276e23277792d55a055ab724714..378fb007f6b4471e12ea4cd6ff2ce6e54ba3f0e9 100644
--- a/Random/generators.py
+++ b/Random/generators.py
@@ -1,6 +1,6 @@
## Various Random Generators ##
import random
-
+import numpy as np
class RandomNumberGenerator:
def __init__(self):
@@ -29,7 +29,7 @@ class RandomNumberGenerator:
"""
return n normalized values
"""
- return [self.random() for i in range(n)] / self.max
+ return np.divide(np.array([self.random() for i in range(n)]),self.max)
class LCG(RandomNumberGenerator):
@@ -62,7 +62,7 @@ class ParkMiller(LCG):
"""
def __init__(self):
- super().__init__(16807, 0, 2147483647)
+ super().__init__(16807, 0, (2**31) - 1)
class KnuthLewis(LCG):
@@ -71,7 +71,7 @@ class KnuthLewis(LCG):
"""
def __init__(self):
- super().__init__(1664525, 1013904223, 4294967296)
+ super().__init__(1664525, 1013904223, 2**32)
class Marsaglia(LCG):
@@ -80,7 +80,7 @@ class Marsaglia(LCG):
"""
def __init__(self):
- super().__init__(69069, 0, 4294967296)
+ super().__init__(69069, 0, 2**32)
class LavauxJenssens(LCG):
@@ -89,7 +89,7 @@ class LavauxJenssens(LCG):
"""
def __init__(self):
- super().__init__(31167285, 1, 281474976710656)
+ super().__init__(31167285, 1, 2**48)
class Haynes(LCG):
@@ -98,7 +98,7 @@ class Haynes(LCG):
"""
def __init__(self):
- super().__init__(6364136223846793005, 1, 18446744073709552000)
+ super().__init__(6364136223846793005, 1, 2**64)
class LaggedFibonnaci(RandomNumberGenerator):
@@ -157,6 +157,9 @@ class MersenneTwister(RandomNumberGenerator):
def random(self):
return self.randomNorm()
+ def batchRandomNormalized(self,n):
+ return np.array([self.random() for i in range(n)])
+
class CNS(RandomNumberGenerator):
"""
diff --git a/Random/graph.py b/Random/graph.py
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..64974ff5dfe66ba6560fd7ec4fbb3b70589e2a20 100644
--- a/Random/graph.py
+++ b/Random/graph.py
@@ -0,0 +1,66 @@
+"""
+Module for graphing
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+
+def hist_distributivity_graph(N,RESOLUTION,seed,data):
+ """
+ graphs histograms
+ see values function in main module
+ """
+ for name in data:
+ print("building "+str(name)+" histogram...")
+ xlabel= "Value"
+ ylabel="Count"
+ title_name= name +"\n N = "+str(N)+"\n seed = "+str(seed)
+ graph=sns.displot(data[name], kde=True)
+ graph.set(title=title_name)
+ graph.set_axis_labels(xlabel,ylabel)
+ graph.savefig(name+'_hist.png', bbox_inches='tight')
+ return
+
+def ecdf_graph(N,RESOLUTION,seed,data):
+ """
+ graphs histograms
+ see values function in main module
+ """
+ plt.clf()
+ for name in data:
+ print("building "+str(name)+" ecdf...")
+ xlabel= "Value"
+ ylabel="Cumulative Probability"
+ title_name= name +"\n N = "+str(N)+"\n seed = "+str(seed)
+ graph=sns.displot(data[name], kind="ecdf")
+ graph.set(title=title_name)
+ graph.set_axis_labels(xlabel,ylabel)
+ graph.savefig(name+'_ecdf.png', bbox_inches='tight')
+ return
+
+def compare(N, RESOLUTION, seed, data):
+ """
+ Create graphs superposing two, already generated,
+ distributivity
+ """
+ plt.clf()
+ BW = 0.337
+ name = "compare_"
+ for e in data:
+ name += e+"_"
+
+ #subdata = {k : data[k] for k in names}
+ print("graphing "+str(name)+"...")
+
+ xlabel = "Value"
+ ylabel = "Density"
+ title_name="N = "+str(N)+"\n seed = "+str(seed)
+ if N == 0:
+ title_name = "seed = "+str(seed)
+ graph=sns.kdeplot(data,legend=True,bw_adjust=BW,common_norm=False)
+ graph.set(title=title_name)
+ plt.xlabel(xlabel)
+ plt.ylabel(ylabel)
+ plt.savefig(name+'.png', bbox_inches='tight')
+ return
diff --git a/Random/main.py b/Random/main.py
index 4473b35ba3d99f953f0c9d6a996dc321db4289c1..936aeb7f254edbb3c22b33edb0d41218cc475bea 100755
--- a/Random/main.py
+++ b/Random/main.py
@@ -1,29 +1,23 @@
from generators import *
+import graph
from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
-if __name__=="__main__":
+#shut up stupid warnings from not "the-most-up-to-date" libraries
+import warnings
+warnings.simplefilter(action='ignore', category=FutureWarning)
- ##INIT PARAMS##
- N = 1000000
- RESOLUTION = 100
- #set seaborn params
- sns.set_theme(style="darkgrid")
-
- #bandwidth for smooth density
- #https://en.wikipedia.org/wiki/Kernel_density_estimation#Bandwidth_selection
- BW = 0.337
-
- #init generators
- generators = [ParkMiller(), KnuthLewis(), Marsaglia()
- , LavauxJenssens(), Haynes(), MitchelMoore()
- , MersenneTwister(), BlumBlumShub()]
-
- #init seed on timesystem
- seed = datetime.now().timestamp()
-
+def subdict(keys,dictio):
+ return {k : dictio[k] for k in keys}
+
+def values(N, seed, generators):
+ """
+ random values N elements and given seed.
+ Returns a dictionnary with values
+ """
+ results = {}
#generate each diagrams for each random func
for e in generators:
name = type(e).__name__
@@ -31,14 +25,48 @@ if __name__=="__main__":
print("processing "+name+"...")
e.seed(seed)
res = e.batchRandomNormalized(N)
- results.append(results)
+ results[name] = res
+ return results
+
+def iteratives(N, seed, generator):
+ """
+ Random values iteratively with same seed and list N
+ """
+ results = {}
+ #generate each diagrams for each random func
+ for n in N:
+ name = type(generator).__name__+"_"+str(n)
- print("building histograms..")
- fig, ax = plt.subplots()
- sns.displot(res, kde=True)
- ax.set_xlabel('Value')
- ax.set_ylabel('Probability density')
- ax.set_title(type(e).__name__)
- plt.savefig(type(e).__name__+'.png')
+ print("processing "+name+"...")
+ generator.seed(seed)
+ res = generator.batchRandomNormalized(n)
+ results[name] = res
+ return results
- #generate diagrams with all randoms
+if __name__=="__main__":
+ ##INIT PARAMS##
+ N = 100
+ RESOLUTION = 1000
+
+ #init generators
+ generators = [ParkMiller(), KnuthLewis(), Marsaglia()
+ , LavauxJenssens(), Haynes(), MitchelMoore()
+ , MersenneTwister(), BlumBlumShub()]
+
+ #init seed on timesystem
+ seed = int(datetime.now().timestamp())
+
+ #set seaborn params
+ sns.set_theme(style="darkgrid")
+
+ #generate all diagrams
+ data = values(N,seed,generators)
+
+ #graph.hist_distributivity_graph(N,RESOLUTION,seed,data)
+ #graph.compare(N, RESOLUTION, seed
+ # , subdict(["ParkMiller","KnuthLewis","Haynes"], data))
+
+ #graph.ecdf_graph(N,RESOLUTION,seed,data)
+
+ graph.compare(0, RESOLUTION, seed
+ , iteratives([1000, 100000, 100000000], seed, ParkMiller()))