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fix: rename boxes to bins

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This commit is contained in:
Paul ALNET 2023-06-04 07:11:06 +02:00
parent 8284d7bf03
commit d8b470c9d4
1 changed files with 14 additions and 14 deletions
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28
Probas.py
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@ -55,7 +55,7 @@ def stats_NFBP(R, N):
for n in range(N): for n in range(N):
H[n].append(sim["H"][n]) H[n].append(sim["H"][n])
print("Mean number of boxes : {} (variance {})".format(mean(I), variance(I))) print("Mean number of bins : {} (variance {})".format(mean(I), variance(I)))
for n in range(N): for n in range(N):
print("Mean H_{} : {} (variance {})".format(n, mean(H[n]), variance(H[n]))) print("Mean H_{} : {} (variance {})".format(n, mean(H[n]), variance(H[n])))
@ -94,13 +94,13 @@ def stats_NFBP_iter(R, N):
print(Sum_V) print(Sum_V)
I = ISum/R I = ISum/R
IVariance = sqrt(IVarianceSum/(R-1) - I**2) IVariance = sqrt(IVarianceSum/(R-1) - I**2)
print("Mean number of boxes : {} (variance {})".format(I, IVariance),'\n') print("Mean number of bins : {} (variance {})".format(I, IVariance),'\n')
print(" {} * {} iterations of T".format(R,N),'\n') print(" {} * {} iterations of T".format(R,N),'\n')
for n in range(N): for n in range(N):
Hn = HSum[n]/R # moyenne Hn = HSum[n]/R # moyenne
HVariance = sqrt(HSumVariance[n]/(R-1) - Hn**2) # Variance HVariance = sqrt(HSumVariance[n]/(R-1) - Hn**2) # Variance
print("Index of box containing the {}th package (H_{}) : {} (variance {})".format(n, n, Hn, HVariance)) print("Index of bin containing the {}th package (H_{}) : {} (variance {})".format(n, n, Hn, HVariance))
HSum=[x/R for x in HSum] HSum=[x/R for x in HSum]
print(HSum) print(HSum)
#Plotting #Plotting
@ -112,23 +112,23 @@ def stats_NFBP_iter(R, N):
ax.bar(x,Sum_T, width=1,label='Empirical values', edgecolor="blue", linewidth=0.7,color='red') 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(xlim=(0, N), xticks=np.arange(0, N),ylim=(0,3), yticks=np.linspace(0, 3, 5))
ax.set_ylabel('Items') ax.set_ylabel('Items')
ax.set_xlabel('Boxes (1-{})'.format(N)) ax.set_xlabel('Bins (1-{})'.format(N))
ax.set_title('T histogram for {} packages (Number of packages in each box)'.format(P)) ax.set_title('T histogram for {} packages (Number of packages in each bin)'.format(P))
ax.legend(loc='upper left',title='Legend') ax.legend(loc='upper left',title='Legend')
#V plot #V plot
bx = fig.add_subplot(222) bx = fig.add_subplot(222)
bx.bar(x,Sum_V, width=1,label='Empirical values', edgecolor="blue", linewidth=0.7,color='orange') bx.bar(x,Sum_V, width=1,label='Empirical values', edgecolor="blue", linewidth=0.7,color='orange')
bx.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0, 1), yticks=np.linspace(0, 1, 10)) bx.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0, 1), yticks=np.linspace(0, 1, 10))
bx.set_ylabel('First item size') bx.set_ylabel('First item size')
bx.set_xlabel('Boxes (1-{})'.format(N)) bx.set_xlabel('Bins (1-{})'.format(N))
bx.set_title('V histogram for {} packages (first package size of each box)'.format(P)) bx.set_title('V histogram for {} packages (first package size of each bin)'.format(P))
bx.legend(loc='upper left',title='Legend') bx.legend(loc='upper left',title='Legend')
#H plot #H plot
#We will simulate this part for a asymptotic study #We will simulate this part for a asymptotic study
cx = fig.add_subplot(223) cx = fig.add_subplot(223)
cx.bar(x,HSum, width=1,label='Empirical values', edgecolor="blue", linewidth=0.7,color='green') cx.bar(x,HSum, width=1,label='Empirical values', edgecolor="blue", linewidth=0.7,color='green')
cx.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0, 10), yticks=np.linspace(0, N, 5)) cx.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0, 10), yticks=np.linspace(0, N, 5))
cx.set_ylabel('Box ranking of n-item') cx.set_ylabel('Bin ranking of n-item')
cx.set_xlabel('n-item (1-{})'.format(N)) cx.set_xlabel('n-item (1-{})'.format(N))
cx.set_title('H histogram for {} packages'.format(P)) cx.set_title('H histogram for {} packages'.format(P))
xb=linspace(0,N,10) xb=linspace(0,N,10)
@ -203,7 +203,7 @@ def stats_NFDBP(R, N,t_i):
Sum_T=[x/R for x in Sum_T] #Experimental [Ti=k] 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 Sum_T=[x*100/(sum(Sum_T)) for x in Sum_T] #Pourcentage de la repartition des items
print("Mean number of boxes : {} (variance {})".format(mean(I), variance(I))) print("Mean number of bins : {} (variance {})".format(mean(I), variance(I)))
for n in range(N): for n in range(N):
print("Mean H_{} : {} (variance {})".format(n, mean(H[n]), variance(H[n]))) print("Mean H_{} : {} (variance {})".format(n, mean(H[n]), variance(H[n])))
@ -231,8 +231,8 @@ def stats_NFDBP(R, N,t_i):
ax.bar(x,Sum_T, width=1,label='Empirical values', edgecolor="blue", linewidth=0.7,color='red') 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=(0,20), yticks=np.linspace(0, 20, 2)) 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_ylabel('Items(n) in %')
ax.set_xlabel('Boxes (1-{})'.format(N)) ax.set_xlabel('Bins (1-{})'.format(N))
ax.set_title('Items percentage for each box and {} packages (Number of packages in each box)'.format(P)) ax.set_title('Items percentage for each bin and {} packages (Number of packages in each bin)'.format(P))
ax.legend(loc='upper left',title='Legend') 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. #Mathematical P(Ti=k) plot. It shows the Ti(t_i) law with the probability of each number of items.
@ -241,8 +241,8 @@ def stats_NFDBP(R, N,t_i):
bx.hist(Tk[t_i],bins=10, width=1,label='Empirical values', edgecolor="blue", linewidth=0.7,color='red') 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(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_ylabel('P(T{}=i)'.format(t_i))
bx.set_xlabel('Boxes i=(1-{}) in %'.format(N)) bx.set_xlabel('Bins i=(1-{}) in %'.format(N))
bx.set_title('T{} histogram for {} packages (Number of packages in each box)'.format(t_i,P)) bx.set_title('T{} histogram for {} packages (Number of packages in each bin)'.format(t_i,P))
bx.legend(loc='upper left',title='Legend') bx.legend(loc='upper left',title='Legend')
#Loi mathematique #Loi mathematique
@ -251,7 +251,7 @@ def stats_NFDBP(R, N,t_i):
cx.bar(x,T_maths, width=1,label='Theoretical values', edgecolor="blue", linewidth=0.7,color='red') 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(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_ylabel('P(T{}=i)'.format(t_i))
cx.set_xlabel('Boxes i=(1-{})'.format(N)) cx.set_xlabel('Bins i=(1-{})'.format(N))
cx.set_title('Theoretical T{} values in %'.format(t_i)) cx.set_title('Theoretical T{} values in %'.format(t_i))
cx.legend(loc='upper left',title='Legend') cx.legend(loc='upper left',title='Legend')
plt.show() plt.show()