Added many thing, check the code Paul
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1 changed files with 46 additions and 30 deletions
76
Probas.py
76
Probas.py
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@ -1,6 +1,6 @@
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#!/usr/bin/python3
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from random import random
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from math import floor, sqrt
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from math import floor, sqrt,factorial
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from statistics import mean, variance
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from matplotlib import pyplot as plt
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from pylab import *
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@ -185,55 +185,77 @@ def stats_NFDBP(R, N,t_i):
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T=[]
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Tk=[[] for _ in range(N)]
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Ti=[]
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T_maths=[]
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#First iteration to use zip after
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sim=simulate_NFDBP(N)
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Sum_T=sim["T"]
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Sum_T=[0 for _ in range(N)]
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for i in range(R):
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sim = simulate_NFDBP(N)
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I.append(sim["i"])
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for k in range(N):
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T.append(0)
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T=sim["T"]
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for n in range(N):
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H[n].append(sim["H"][n])
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Tk[n].append(sim["T"][n])
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T=sim["T"]
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Ti.append(sim["T"])
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for k in range(N):
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Sum_T.append(0)
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T.append(0)
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Sum_T=[x+y for x,y in zip(Sum_T,T)]
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Sum_T=[x/R for x in Sum_T] #Experimental [Ti=k]
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Sum_T=[x*100/(sum(Sum_T)) for x in Sum_T] #Pourcentage de la repartition des items
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print(Tk)
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print("Mean number of boxes : {} (variance {})".format(mean(I), variance(I)))
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for n in range(N):
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print("Mean H_{} : {} (variance {})".format(n, mean(H[n]), variance(H[n])))
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print("Mean T_{} : {} (variance {})".format(k, mean(Sum_T), variance(Sum_T)))
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#Loi math
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for u in range(N):
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u=u+2
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T_maths.append(1/(factorial(u-1))-1/factorial(u))
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E=0
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sigma2=0
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print("hep")
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print(T_maths)
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for p in range(len(T_maths)):
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E=E+(p+1)*T_maths[p]
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sigma2=((T_maths[p]-E)**2)/(len(T_maths)-1)
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print("Mathematical values : Empiric mean T_{} : {} Variance {})".format(t_i, E, sqrt(sigma2)))
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T_maths=[x*100 for x in T_maths]
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#Plotting
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fig = plt.figure()
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#T plot
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x = np.arange(N)
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print(x)
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ax = fig.add_subplot(121)
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print(Sum_T)
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ax = fig.add_subplot(221)
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ax.bar(x,Sum_T, width=1,label='Empirical values', edgecolor="blue", linewidth=0.7,color='red')
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ax.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0,3), yticks=np.linspace(0, 3, 5))
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ax.set_ylabel('Items')
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ax.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0,20), yticks=np.linspace(0, 20, 2))
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ax.set_ylabel('Items(n) in %')
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ax.set_xlabel('Boxes (1-{})'.format(N))
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ax.set_title('T histogram for {} packages (Number of packages in each box)'.format(P))
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ax.set_title('Items percentage for each box and {} packages (Number of packages in each box)'.format(P))
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ax.legend(loc='upper left',title='Legend')
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#Mathematical P(Ti=k) plot. It shows the Ti(t_i) law with the probability of each number of items.
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print(len(Tk[t_i]))
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bx = fig.add_subplot(222)
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bx.hist(Tk[t_i],bins=10, width=1,label='Empirical values', edgecolor="blue", linewidth=0.7,color='red')
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bx.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0,len(Tk[t_i])), yticks=np.linspace(0, 1, 1))
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bx.set_ylabel('P(T{}=i)'.format(t_i))
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bx.set_xlabel('Boxes i=(1-{}) in %'.format(N))
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bx.set_title('T{} histogram for {} packages (Number of packages in each box)'.format(t_i,P))
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bx.legend(loc='upper left',title='Legend')
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#Loi mathematique
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print(T_maths)
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cx = fig.add_subplot(224)
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cx.bar(x,T_maths, width=1,label='Theoretical values', edgecolor="blue", linewidth=0.7,color='red')
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cx.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0,100), yticks=np.linspace(0, 100, 10))
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cx.set_ylabel('P(T{}=i)'.format(t_i))
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cx.set_xlabel('Boxes i=(1-{})'.format(N))
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cx.set_title('Theoretical T{} values in %'.format(t_i))
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cx.legend(loc='upper left',title='Legend')
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plt.show()
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#Mathematical P(Ti=k) plot
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x = np.arange(N)
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print(x)
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ax = fig.add_subplot(122)
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ax.hist(x,Sum_T, width=1,label='Empirical values', edgecolor="blue", linewidth=0.7,color='red')
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ax.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0,3), yticks=np.linspace(0, 3, 5))
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ax.set_ylabel('Items')
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ax.set_xlabel('Boxes (1-{})'.format(N))
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ax.set_title('T histogram for {} packages (Number of packages in each box)'.format(P))
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ax.legend(loc='upper left',title='Legend')
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plt.show()
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N = 10 ** 1
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sim = simulate_NFBP(N)
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@ -257,10 +279,4 @@ for j in range(sim["i"] + 1):
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print()
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stats_NFBP_iter(10**3, 10)
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#stats_NFDBP(10 ** 3, 10)
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#
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#pyplot.plot([1, 2, 4, 4, 2, 1], color = 'red', linestyle = 'dashed', linewidth = 2,
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#markerfacecolor = 'blue', markersize = 5)
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#pyplot.ylim(0, 5)
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#pyplot.title('Un exemple')
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#show()
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stats_NFDBP(10 ** 3, 10,1)
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