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fix: rename packages to items

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Paul ALNET 2023-06-04 07:14:37 +02:00
parent d8b470c9d4
commit 5f56b578d2
1 changed files with 14 additions and 14 deletions
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28
Probas.py
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@ -9,7 +9,7 @@ import matplotlib.pyplot as pt
def simulate_NFBP(N): def simulate_NFBP(N):
""" """
Tries to simulate T_i, V_i and H_n for N packages of random size. Tries to simulate T_i, V_i and H_n for N items of random size.
""" """
i = 0 # Nombre de boites i = 0 # Nombre de boites
R = [0] # Remplissage de la i-eme boite R = [0] # Remplissage de la i-eme boite
@ -43,9 +43,9 @@ def simulate_NFBP(N):
def stats_NFBP(R, N): def stats_NFBP(R, N):
""" """
Runs R runs of NFBP (for N packages) and studies distribution, variance, mean... Runs R runs of NFBP (for N items) and studies distribution, variance, mean...
""" """
print("Running {} NFBP simulations with {} packages".format(R, N)) print("Running {} NFBP simulations with {} items".format(R, N))
I = [] I = []
H = [[] for _ in range(N)] # List of empty lists H = [[] for _ in range(N)] # List of empty lists
@ -62,11 +62,11 @@ def stats_NFBP(R, N):
def stats_NFBP_iter(R, N): def stats_NFBP_iter(R, N):
""" """
Runs R runs of NFBP (for N packages) and studies distribution, variance, mean... Runs R runs of NFBP (for N items) and studies distribution, variance, mean...
Calculates stats during runtime instead of after to avoid excessive memory usage. Calculates stats during runtime instead of after to avoid excessive memory usage.
""" """
P=R*N P=R*N
print("Running {} NFBP simulations with {} packages".format(R, N)) print("Running {} NFBP simulations with {} items".format(R, N))
ISum = 0 ISum = 0
IVarianceSum = 0 IVarianceSum = 0
HSum = [0 for _ in range(N)] HSum = [0 for _ in range(N)]
@ -100,7 +100,7 @@ def stats_NFBP_iter(R, 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 bin containing the {}th package (H_{}) : {} (variance {})".format(n, n, Hn, HVariance)) print("Index of bin containing the {}th item (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
@ -113,7 +113,7 @@ def stats_NFBP_iter(R, N):
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('Bins (1-{})'.format(N)) ax.set_xlabel('Bins (1-{})'.format(N))
ax.set_title('T histogram for {} packages (Number of packages in each bin)'.format(P)) ax.set_title('T histogram for {} items (Number of items 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)
@ -121,7 +121,7 @@ def stats_NFBP_iter(R, N):
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('Bins (1-{})'.format(N)) bx.set_xlabel('Bins (1-{})'.format(N))
bx.set_title('V histogram for {} packages (first package size of each bin)'.format(P)) bx.set_title('V histogram for {} items (first item 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
@ -130,7 +130,7 @@ def stats_NFBP_iter(R, N):
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('Bin 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 {} items'.format(P))
xb=linspace(0,N,10) xb=linspace(0,N,10)
yb=Hn*xb/10 yb=Hn*xb/10
wb=HVariance*xb/10 wb=HVariance*xb/10
@ -141,7 +141,7 @@ def stats_NFBP_iter(R, N):
def simulate_NFDBP(N): def simulate_NFDBP(N):
""" """
Tries to simulate T_i, V_i and H_n for N packages of random size. Tries to simulate T_i, V_i and H_n for N items of random size.
""" """
i = 0 # Nombre de boites i = 0 # Nombre de boites
R = [0] # Remplissage de la i-eme boite R = [0] # Remplissage de la i-eme boite
@ -176,9 +176,9 @@ def simulate_NFDBP(N):
def stats_NFDBP(R, N,t_i): def stats_NFDBP(R, N,t_i):
""" """
Runs R runs of NFDBP (for N packages) and studies distribution, variance, mean... Runs R runs of NFDBP (for N items) and studies distribution, variance, mean...
""" """
print("Running {} NFDBP simulations with {} packages".format(R, N)) print("Running {} NFDBP simulations with {} items".format(R, N))
P=N*R P=N*R
I = [] I = []
H = [[] for _ in range(N)] # List of empty lists H = [[] for _ in range(N)] # List of empty lists
@ -232,7 +232,7 @@ def stats_NFDBP(R, N,t_i):
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('Bins (1-{})'.format(N)) 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.set_title('Items percentage for each bin and {} items (Number of items 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.
@ -242,7 +242,7 @@ def stats_NFDBP(R, N,t_i):
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('Bins 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 bin)'.format(t_i,P)) bx.set_title('T{} histogram for {} items (Number of items 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