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Added many thing, check the code Paul

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Clément Lacau 2023-06-04 00:28:24 +02:00
parent 7804bbfc43
commit 8284d7bf03
1 changed files with 46 additions and 30 deletions
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Probas.py
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@ -1,6 +1,6 @@
#!/usr/bin/python3
from random import random
from math import floor, sqrt
from math import floor, sqrt,factorial
from statistics import mean, variance
from matplotlib import pyplot as plt
from pylab import *
@ -185,55 +185,77 @@ def stats_NFDBP(R, N,t_i):
T=[]
Tk=[[] for _ in range(N)]
Ti=[]
T_maths=[]
#First iteration to use zip after
sim=simulate_NFDBP(N)
Sum_T=sim["T"]
Sum_T=[0 for _ in range(N)]
for i in range(R):
sim = simulate_NFDBP(N)
I.append(sim["i"])
for k in range(N):
T.append(0)
T=sim["T"]
for n in range(N):
H[n].append(sim["H"][n])
Tk[n].append(sim["T"][n])
T=sim["T"]
Ti.append(sim["T"])
for k in range(N):
Sum_T.append(0)
T.append(0)
Sum_T=[x+y for x,y in zip(Sum_T,T)]
Sum_T=[x/R for x in Sum_T] #Experimental [Ti=k]
Sum_T=[x*100/(sum(Sum_T)) for x in Sum_T] #Pourcentage de la repartition des items
print(Tk)
print("Mean number of boxes : {} (variance {})".format(mean(I), variance(I)))
for n in range(N):
print("Mean H_{} : {} (variance {})".format(n, mean(H[n]), variance(H[n])))
print("Mean T_{} : {} (variance {})".format(k, mean(Sum_T), variance(Sum_T)))
#Loi math
for u in range(N):
u=u+2
T_maths.append(1/(factorial(u-1))-1/factorial(u))
E=0
sigma2=0
print("hep")
print(T_maths)
for p in range(len(T_maths)):
E=E+(p+1)*T_maths[p]
sigma2=((T_maths[p]-E)**2)/(len(T_maths)-1)
print("Mathematical values : Empiric mean T_{} : {} Variance {})".format(t_i, E, sqrt(sigma2)))
T_maths=[x*100 for x in T_maths]
#Plotting
fig = plt.figure()
#T plot
x = np.arange(N)
print(x)
ax = fig.add_subplot(121)
print(Sum_T)
ax = fig.add_subplot(221)
ax.bar(x,Sum_T, width=1,label='Empirical values', edgecolor="blue", linewidth=0.7,color='red')
ax.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0,3), yticks=np.linspace(0, 3, 5))
ax.set_ylabel('Items')
ax.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0,20), yticks=np.linspace(0, 20, 2))
ax.set_ylabel('Items(n) in %')
ax.set_xlabel('Boxes (1-{})'.format(N))
ax.set_title('T histogram for {} packages (Number of packages in each box)'.format(P))
ax.set_title('Items percentage for each box and {} packages (Number of packages in each box)'.format(P))
ax.legend(loc='upper left',title='Legend')
#Mathematical P(Ti=k) plot. It shows the Ti(t_i) law with the probability of each number of items.
print(len(Tk[t_i]))
bx = fig.add_subplot(222)
bx.hist(Tk[t_i],bins=10, width=1,label='Empirical values', edgecolor="blue", linewidth=0.7,color='red')
bx.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0,len(Tk[t_i])), yticks=np.linspace(0, 1, 1))
bx.set_ylabel('P(T{}=i)'.format(t_i))
bx.set_xlabel('Boxes i=(1-{}) in %'.format(N))
bx.set_title('T{} histogram for {} packages (Number of packages in each box)'.format(t_i,P))
bx.legend(loc='upper left',title='Legend')
#Loi mathematique
print(T_maths)
cx = fig.add_subplot(224)
cx.bar(x,T_maths, width=1,label='Theoretical values', edgecolor="blue", linewidth=0.7,color='red')
cx.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0,100), yticks=np.linspace(0, 100, 10))
cx.set_ylabel('P(T{}=i)'.format(t_i))
cx.set_xlabel('Boxes i=(1-{})'.format(N))
cx.set_title('Theoretical T{} values in %'.format(t_i))
cx.legend(loc='upper left',title='Legend')
plt.show()
#Mathematical P(Ti=k) plot
x = np.arange(N)
print(x)
ax = fig.add_subplot(122)
ax.hist(x,Sum_T, width=1,label='Empirical values', edgecolor="blue", linewidth=0.7,color='red')
ax.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0,3), yticks=np.linspace(0, 3, 5))
ax.set_ylabel('Items')
ax.set_xlabel('Boxes (1-{})'.format(N))
ax.set_title('T histogram for {} packages (Number of packages in each box)'.format(P))
ax.legend(loc='upper left',title='Legend')
plt.show()
N = 10 ** 1
sim = simulate_NFBP(N)
@ -257,10 +279,4 @@ for j in range(sim["i"] + 1):
print()
stats_NFBP_iter(10**3, 10)
#stats_NFDBP(10 ** 3, 10)
#
#pyplot.plot([1, 2, 4, 4, 2, 1], color = 'red', linestyle = 'dashed', linewidth = 2,
#markerfacecolor = 'blue', markersize = 5)
#pyplot.ylim(0, 5)
#pyplot.title('Un exemple')
#show()
stats_NFDBP(10 ** 3, 10,1)