From f3a01787fcc9e97e21b4bf4532138fe1cbcf90b5 Mon Sep 17 00:00:00 2001
From: Enzo De Carvalho Bittencourt <enzo.decarvalhobittencourt@ensiie.eu>
Date: Fri, 29 Sep 2023 15:35:30 +0200
Subject: [PATCH] updated with multiprocessing
---
Random/__pycache__/graph.cpython-311.pyc | Bin 3608 -> 6734 bytes
Random/graph.py | 57 +++++++++++++++++++-
Random/main.py | 66 ++++++++++++++++++++---
3 files changed, 116 insertions(+), 7 deletions(-)
diff --git a/Random/__pycache__/graph.cpython-311.pyc b/Random/__pycache__/graph.cpython-311.pyc
index 2aa0b3cb28e4111dd0991d20891f1d59472dc43b..c7675dbef627265d7065aaa70c3b625f417f1540 100644
GIT binary patch
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diff --git a/Random/graph.py b/Random/graph.py
index 38f1c6a..037ab66 100644
--- a/Random/graph.py
+++ b/Random/graph.py
@@ -11,6 +11,7 @@ def hist_distributivity_graph(N,RESOLUTION,seed,data):
graphs histograms
see values function in main module
"""
+ plt.clf()
for name in data:
print("building "+str(name)+" histogram...")
xlabel= "Value"
@@ -41,6 +42,33 @@ def ecdf_graph(N,RESOLUTION,seed,data):
graph.savefig(name+'_ecdf.png', bbox_inches='tight')
return
+def compare_ecdf(N, RESOLUTION, seed, data):
+ """
+ Create graphs superposing ecdf
+ """
+ plt.clf()
+ BW = 0.337
+ name = "compare_ecdf_"
+ 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.displot(data,legend=True
+ , kind="ecdf")
+ graph.set(title=title_name)
+ plt.xlabel(xlabel)
+ plt.ylabel(ylabel)
+ #plt.legend()
+ plt.savefig(name+'.png', bbox_inches='tight')
+ return
+
def compare(N, RESOLUTION, seed, data):
"""
Create graphs superposing two, already generated,
@@ -66,6 +94,33 @@ def compare(N, RESOLUTION, seed, data):
graph.set(title=title_name)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
+ graph.axes.set
plt.legend()
- plt.savefig(name+'.png', bbox_inches='tight')
+ plt.savefig(name+'.png', bbox_inches='tight')
return
+
+def lineplot_wrap(data, xlabel, ylabel, title, iterN):
+ plt.clf()
+ print("graphing "+str(title)+"...")
+ for e in data:
+ plt.plot(iterN, data[e], marker="o", label="seed="+str(e))
+ #graph = sns.lineplot(data, legend=True, x=iterN)
+ plt.title(title)
+ plt.legend()
+ plt.xscale('log')
+ plt.xlabel(xlabel)
+ plt.ylabel(ylabel)
+ plt.savefig(title.replace(" ", "_")+".png", bbox_inches="tight")
+
+def lineplot_mean_alot(data, xlabel, ylabel, title, iterN):
+ plt.clf()
+ print("graphing "+str(title)+"...")
+ for e in data:
+ plt.plot(iterN, data[e], marker="o", alpha=0.05
+ , color="red")
+ plt.plot(iterN, [0.5]*len(iterN), color="blue",linestyle='dotted')
+ plt.title(title)
+ plt.xscale('log')
+ plt.xlabel(xlabel)
+ plt.ylabel(ylabel)
+ plt.savefig(title+".png", bbox_inches="tight")
diff --git a/Random/main.py b/Random/main.py
index f10ead0..f6ba337 100755
--- a/Random/main.py
+++ b/Random/main.py
@@ -7,6 +7,7 @@ import stats
import scipy
import seaborn as sns
import csv
+import multiprocessing
#shut up stupid warnings from not "the-most-up-to-date" libraries
import warnings
@@ -72,7 +73,7 @@ def write_stats(data, name):
return
def write_stats_chi(data, name, RES):
- fields=["chi","p"]
+ fields=["name","chi","p"]
val = stats.chisquared_uniform(data,RES)
chi = val[0]
p = val[1]
@@ -86,16 +87,60 @@ def write_stats_chi(data, name, RES):
"p" : str(p)})
return
-if __name__=="__main__":
- ##INIT PARAMS##
+def mean_evolves(generator,number,iterN):
+ datas = {}
+ for i in range(number):
+ seed = int(datetime.now().timestamp()+i)
+ sub_data = iteratives(iterN, seed, generator)
+ res = []
+ for a in sub_data:
+ res.append(np.mean(sub_data[a]))
+ datas[str(seed)] = res
+ return datas
+
+
+def pipeline(generator):
N = 1000000
RESOLUTION = 100
- iterN = [100, 1000000, 100000000]
+ iterN = [100,10000,1000000,100000000]
+ iterNmean = [100, 1000, 10000, 100000, 1000000]
+ seed = int(datetime.now().timestamp())
+ name_ = type(generator).__name__
+
+ sns.set_theme(style="darkgrid")
+ data=values(N,seed,[generator])
+
+ #generate a lot of diagrams
+ graph.hist_distributivity_graph(N,RESOLUTION,seed,data)
+ for name in data:
+ write_stats(data[name],name)
+ write_stats_chi(data[name],name,RESOLUTION)
+
+ data_iter = iteratives(iterN, seed, generator)
+ graph.compare(0, RESOLUTION, seed, data_iter)
+ graph.compare(0, RESOLUTION, seed, data_iter)
+
+ data_alot = mean_evolves(generator, 500, iterNmean)
+ graph.lineplot_mean_alot(data_alot, "Size", "Mean"
+ , "Mean values evolution for "+name_, iterNmean)
+
+
+if __name__=="__main__":
#init generators
generators = [ParkMiller(), KnuthLewis(), Marsaglia()
, LavauxJenssens(), Haynes(), MitchelMoore()
, MersenneTwister(), BlumBlumShub()]
+ for a in generators:
+ p = multiprocessing.Process(target=pipeline, args=[a])
+ p.start()
+
+ """
+ ##INIT PARAMS##
+ N = 1000000
+ RESOLUTION = 100
+ iterN = [100, 10000, 1000000, 100000000]
+
#init seed on timesystem
seed = int(datetime.now().timestamp())
@@ -104,8 +149,16 @@ if __name__=="__main__":
sns.set_theme(style="darkgrid")
#generate N random values for each RNG
- data = values(N,seed,generators)
-
+ #data = values(N,seed,generators)
+
+ #graph.compare_ecdf(0, RESOLUTION, seed
+ # , iteratives([100,1000,1000000], seed, ParkMiller()))
+
+ a = mean_evolves(ParkMiller(), 500, [100,1000])
+ graph.lineplot_mean_alot(a, "Size", "Mean"
+ , "Mean Value evolution for ParkMiller"
+ ,[100,1000])
+
#generate a lot of diagrams
graph.hist_distributivity_graph(N,RESOLUTION,seed,data)
for name in data:
@@ -116,3 +169,4 @@ if __name__=="__main__":
graph.ecdf_graph(0, RESOLUTION, seed, data_iter)
graph.compare(0, RESOLUTION, seed, data_iter)
+ """
--
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