From d8b470c9d4c36d610b939bf167d0c3fea48f6eb1 Mon Sep 17 00:00:00 2001 From: Paul ALNET Date: Sun, 4 Jun 2023 07:11:06 +0200 Subject: [PATCH] fix: rename boxes to bins --- Probas.py | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/Probas.py b/Probas.py index d70d8d6..2fd03ce 100755 --- a/Probas.py +++ b/Probas.py @@ -55,7 +55,7 @@ def stats_NFBP(R, N): for n in range(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): 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) I = ISum/R 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') for n in range(N): Hn = HSum[n]/R # moyenne 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] print(HSum) #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.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.set_xlabel('Bins (1-{})'.format(N)) + ax.set_title('T histogram for {} packages (Number of packages in each bin)'.format(P)) ax.legend(loc='upper left',title='Legend') #V plot bx = fig.add_subplot(222) 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_ylabel('First item size') - bx.set_xlabel('Boxes (1-{})'.format(N)) - bx.set_title('V histogram for {} packages (first package size of each box)'.format(P)) + bx.set_xlabel('Bins (1-{})'.format(N)) + bx.set_title('V histogram for {} packages (first package size of each bin)'.format(P)) bx.legend(loc='upper left',title='Legend') #H plot #We will simulate this part for a asymptotic study cx = fig.add_subplot(223) 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_ylabel('Box ranking of n-item') + cx.set_ylabel('Bin ranking of n-item') cx.set_xlabel('n-item (1-{})'.format(N)) cx.set_title('H histogram for {} packages'.format(P)) 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*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): 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.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('Items percentage for each box and {} packages (Number of packages in each box)'.format(P)) + ax.set_xlabel('Bins (1-{})'.format(N)) + 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') #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.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.set_xlabel('Bins i=(1-{}) in %'.format(N)) + bx.set_title('T{} histogram for {} packages (Number of packages in each bin)'.format(t_i,P)) bx.legend(loc='upper left',title='Legend') #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.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_xlabel('Bins i=(1-{})'.format(N)) cx.set_title('Theoretical T{} values in %'.format(t_i)) cx.legend(loc='upper left',title='Legend') plt.show()