fix: rename packages to items
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Paul ALNET
parent
d8b470c9d4
commit
5f56b578d2
1 changed files with 14 additions and 14 deletions
28
Probas.py
28
Probas.py
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@ -9,7 +9,7 @@ import matplotlib.pyplot as pt
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def simulate_NFBP(N):
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"""
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Tries to simulate T_i, V_i and H_n for N packages of random size.
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Tries to simulate T_i, V_i and H_n for N items of random size.
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"""
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i = 0 # Nombre de boites
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R = [0] # Remplissage de la i-eme boite
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@ -43,9 +43,9 @@ def simulate_NFBP(N):
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def stats_NFBP(R, N):
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"""
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Runs R runs of NFBP (for N packages) and studies distribution, variance, mean...
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Runs R runs of NFBP (for N items) and studies distribution, variance, mean...
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"""
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print("Running {} NFBP simulations with {} packages".format(R, N))
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print("Running {} NFBP simulations with {} items".format(R, N))
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I = []
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H = [[] for _ in range(N)] # List of empty lists
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@ -62,11 +62,11 @@ def stats_NFBP(R, N):
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def stats_NFBP_iter(R, N):
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"""
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Runs R runs of NFBP (for N packages) and studies distribution, variance, mean...
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Runs R runs of NFBP (for N items) and studies distribution, variance, mean...
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Calculates stats during runtime instead of after to avoid excessive memory usage.
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"""
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P=R*N
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print("Running {} NFBP simulations with {} packages".format(R, N))
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print("Running {} NFBP simulations with {} items".format(R, N))
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ISum = 0
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IVarianceSum = 0
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HSum = [0 for _ in range(N)]
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@ -100,7 +100,7 @@ def stats_NFBP_iter(R, N):
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for n in range(N):
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Hn = HSum[n]/R # moyenne
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HVariance = sqrt(HSumVariance[n]/(R-1) - Hn**2) # Variance
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print("Index of bin containing the {}th package (H_{}) : {} (variance {})".format(n, n, Hn, HVariance))
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print("Index of bin containing the {}th item (H_{}) : {} (variance {})".format(n, n, Hn, HVariance))
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HSum=[x/R for x in HSum]
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print(HSum)
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#Plotting
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@ -113,7 +113,7 @@ def stats_NFBP_iter(R, N):
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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('Bins (1-{})'.format(N))
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ax.set_title('T histogram for {} packages (Number of packages in each bin)'.format(P))
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ax.set_title('T histogram for {} items (Number of items in each bin)'.format(P))
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ax.legend(loc='upper left',title='Legend')
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#V plot
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bx = fig.add_subplot(222)
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@ -121,7 +121,7 @@ def stats_NFBP_iter(R, N):
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bx.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0, 1), yticks=np.linspace(0, 1, 10))
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bx.set_ylabel('First item size')
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bx.set_xlabel('Bins (1-{})'.format(N))
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bx.set_title('V histogram for {} packages (first package size of each bin)'.format(P))
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bx.set_title('V histogram for {} items (first item size of each bin)'.format(P))
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bx.legend(loc='upper left',title='Legend')
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#H plot
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#We will simulate this part for a asymptotic study
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@ -130,7 +130,7 @@ def stats_NFBP_iter(R, N):
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cx.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0, 10), yticks=np.linspace(0, N, 5))
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cx.set_ylabel('Bin ranking of n-item')
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cx.set_xlabel('n-item (1-{})'.format(N))
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cx.set_title('H histogram for {} packages'.format(P))
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cx.set_title('H histogram for {} items'.format(P))
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xb=linspace(0,N,10)
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yb=Hn*xb/10
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wb=HVariance*xb/10
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@ -141,7 +141,7 @@ def stats_NFBP_iter(R, N):
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def simulate_NFDBP(N):
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"""
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Tries to simulate T_i, V_i and H_n for N packages of random size.
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Tries to simulate T_i, V_i and H_n for N items of random size.
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"""
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i = 0 # Nombre de boites
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R = [0] # Remplissage de la i-eme boite
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@ -176,9 +176,9 @@ def simulate_NFDBP(N):
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def stats_NFDBP(R, N,t_i):
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"""
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Runs R runs of NFDBP (for N packages) and studies distribution, variance, mean...
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Runs R runs of NFDBP (for N items) and studies distribution, variance, mean...
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"""
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print("Running {} NFDBP simulations with {} packages".format(R, N))
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print("Running {} NFDBP simulations with {} items".format(R, N))
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P=N*R
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I = []
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H = [[] for _ in range(N)] # List of empty lists
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@ -232,7 +232,7 @@ def stats_NFDBP(R, N,t_i):
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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('Bins (1-{})'.format(N))
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ax.set_title('Items percentage for each bin and {} packages (Number of packages in each bin)'.format(P))
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ax.set_title('Items percentage for each bin and {} items (Number of items in each bin)'.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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@ -242,7 +242,7 @@ def stats_NFDBP(R, N,t_i):
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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('Bins i=(1-{}) in %'.format(N))
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bx.set_title('T{} histogram for {} packages (Number of packages in each bin)'.format(t_i,P))
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bx.set_title('T{} histogram for {} items (Number of items in each bin)'.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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