chore: clean up outputs + add comments
This commit is contained in:
Paul ALNET
parent
5f56b578d2
commit
7bee845a97
1 changed files with 25 additions and 20 deletions
45
Probas.py
45
Probas.py
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@ -11,11 +11,11 @@ def simulate_NFBP(N):
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"""
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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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i = 0 # Nombre de boites
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R = [0] # Remplissage de la i-eme boite
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T = [0] # Nombre de paquets de la i-eme boite
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V = [0] # Taille du premier paquet de la i-eme boite
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H = [] # Rang de la boite contenant le n-ieme paquet
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H = [] # Rang de la boite contenant le n-ieme paquet
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for n in range(N):
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size = random()
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if R[i] + size >= 1:
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@ -41,6 +41,7 @@ def simulate_NFBP(N):
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}
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# unused
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def stats_NFBP(R, N):
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"""
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Runs R runs of NFBP (for N items) and studies distribution, variance, mean...
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@ -65,14 +66,19 @@ def stats_NFBP_iter(R, N):
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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 {} items".format(R, N))
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ISum = 0
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P=R*N # Total number of items
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print("## Running {} NFBP simulations with {} items".format(R, N))
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# number of bins
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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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# index of the bin containing the n-th item
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HSum = [0 for _ in range(N)]
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HSumVariance = [0 for _ in range(N)]
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# number of items in the i-th bin
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Sum_T=[0 for _ in range(N)]
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# size of the first item in the i-th bin
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Sum_V=[0 for _ in range(N)]
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for i in range(R):
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sim = simulate_NFBP(N)
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ISum += sim["i"]
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@ -87,14 +93,13 @@ def stats_NFBP_iter(R, N):
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V.append(0)
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Sum_T=[x+y for x,y in zip(Sum_T,T)]
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Sum_V=[x+y for x,y in zip(Sum_V,V)]
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#we use round to approximate variations of continuous variable V
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# Sum_V= round(sim['V'],2))
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Sum_T=[x/R for x in Sum_T]
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Sum_V=[round(x/R,2) for x in Sum_V]
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print(Sum_V)
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#print(Sum_V)
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I = ISum/R
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IVariance = sqrt(IVarianceSum/(R-1) - I**2)
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print("Mean number of bins : {} (variance {})".format(I, IVariance),'\n')
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# TODO clarify line below
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print(" {} * {} iterations of T".format(R,N),'\n')
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for n in range(N):
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@ -102,12 +107,12 @@ def stats_NFBP_iter(R, N):
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HVariance = sqrt(HSumVariance[n]/(R-1) - Hn**2) # Variance
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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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# print(HSum)
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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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# print(x)
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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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@ -142,12 +147,13 @@ 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 items of random size.
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Next Fit Dual Bin Packing : bins should overflow
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"""
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i = 0 # Nombre de boites
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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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T = [0] # Nombre de paquets de la i-eme boite
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V = [0] # Taille du premier paquet de la i-eme boite
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H = [] # Rang de la boite contenant le n-ieme paquet
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H = [] # Rang de la boite contenant le n-ieme paquet
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for n in range(N):
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size = random()
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R[i] += size
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@ -178,9 +184,9 @@ def stats_NFDBP(R, N,t_i):
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"""
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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 {} items".format(R, N))
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P=N*R
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I = []
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print("## Running {} NFDBP simulations with {} items".format(R, N))
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P=N*R # Total number of items
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I = []
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H = [[] for _ in range(N)] # List of empty lists
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T=[]
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Tk=[[] for _ in range(N)]
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@ -214,14 +220,13 @@ def stats_NFDBP(R, N,t_i):
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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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# 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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#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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@ -277,6 +282,6 @@ for j in range(sim["i"] + 1):
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sim["T"][j],
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sim["V"][j]))
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print()
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stats_NFBP_iter(10**3, 10)
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print('\n\n')
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stats_NFDBP(10 ** 3, 10,1)
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