Adding T,V,H graphs for NFBP algorithm
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Clément Lacau
parent
e9dbc054fe
commit
ebf87d231c
1 changed files with 67 additions and 22 deletions
77
Probas.py
Normal file → Executable file
77
Probas.py
Normal file → Executable file
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@ -1,11 +1,14 @@
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#!/usr/bin/python3
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from random import random
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from math import floor, sqrt
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from statistics import mean, variance
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# from matplotlib import pyplot
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from matplotlib import pyplot as plt
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from pylab import *
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import numpy as np
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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 boxes of random size.
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Tries to simulate T_i, V_i and H_n for N packages 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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@ -61,12 +64,15 @@ def stats_NFBP_iter(R, N):
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Runs R runs of NFBP (for N packages) 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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ISum = 0
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IVarianceSum = 0
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HSum = [0 for _ in range(N)]
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HSumVariance = [0 for _ in range(N)]
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Sum_T=[]
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Sum_V=[]
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Sum_H=[]
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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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@ -74,11 +80,49 @@ def stats_NFBP_iter(R, N):
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for n in range(N):
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HSum[n] += sim["H"][n]
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HSumVariance[n] += sim["H"][n]**2
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Sum_T=Sum_T+sim['T']
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Sum_H=Sum_H+sim['H']
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for k in range(sim['i']):
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#we use round to approximate variations of continuous variable V
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Sum_V.append(round(sim['V'][k],2))
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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 boxes : {} (variance {})".format(I, IVariance),'\n')
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print(" {} * {} iterations of T".format(R,N),'\n')
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print("Mean number of boxes : {} (variance {})".format(I, IVariance))
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#Plotting
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#fig = plt.figure()
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#ax = fig.add_subplot(111)
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#matplotlib.stairs(Sum_T,bins=[0,1,2,3,4])
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#ax.hist(Sum_T, bins=8, edgecolor='k', density=True, label='Valeurs empiriques')
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#ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
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#ylim=(0, 500), yticks=np.linspace(0, 56, 9))
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#ax.legend()
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#plt.show()
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#plt.style.use('_mpl-gallery')
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#make data
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#plot:
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#fig = plt.subplots()
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fig = plt.figure()
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#T plot
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ax = fig.add_subplot(221)
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ax.hist(Sum_T, bins=6, linewidth=0.5, edgecolor="white", label='Empirical values')
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ax.set(xlim=(0, 6), xticks=np.arange(0, 6),ylim=(0, 6000), yticks=np.linspace(0, 6000, 9))
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ax.set_title('T histogram for {} packages (Number of packages in each box)'.format(P))
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ax.legend()
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#V plot
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bx = fig.add_subplot(222)
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bx.hist(Sum_V, bins=10, linewidth=0.5, edgecolor="white", label='Empirical values')
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bx.set(xlim=(0, 1), xticks=np.arange(0, 1),ylim=(0, 1000), yticks=np.linspace(0, 1000, 9))
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bx.set_title('V histogram for {} packages (first package size of each box)'.format(P))
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bx.legend()
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#H plot
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cx = fig.add_subplot(223)
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cx.hist(Sum_H, bins=10, linewidth=0.5, edgecolor="white", label='Empirical values')
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cx.set(xlim=(0, 10), xticks=np.arange(0, 10),ylim=(0, 2000), yticks=np.linspace(0, 2000, 9))
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cx.set_title('H histogram for {} packages'.format(P))
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cx.legend()
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plt.show()
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for n in range(n):
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Hn = HSum[n]/R
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HVariance = sqrt(HSumVariance[n]/(R-1) - Hn**2)
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@ -86,7 +130,7 @@ 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 boxes of random size.
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Tries to simulate T_i, V_i and H_n for N packages 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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@ -127,22 +171,24 @@ def stats_NFDBP(R, N):
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I = []
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H = [[] for _ in range(N)] # List of empty lists
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Tmean=[]
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T=[]
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for i in range(R):
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sim = simulate_NFDBP(N)
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I.append(sim["i"])
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for n in range(N):
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H[n].append(sim["H"][n])
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T=sim["T"]
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for k in range(sim["i"]):
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# for o in range(sim["i"]):
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Tmean+=sim["T"]
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#Tmean+=sim["T"]
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Tmean.append(T[k])
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print("Mean number of boxes : {} (variance {})".format(mean(I), variance(I)))
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for n in range(N):
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print("Mean H_{} : {} (variance {})".format(n, mean(H[n]), variance(H[n])))
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for k in range(int(mean(I))+1):
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print(Tmean[7])
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# print("Mean T_{} : {} (variance {})".format(k, mean(Tmean[k]), variance(Tmean[k])))
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for k in range(int(sim["i"])):
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print("Mean T_{} : {} (variance {})".format(k, mean(Tmean), variance(Tmean)))
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N = 10 ** 1
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sim = simulate_NFBP(N)
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@ -154,7 +200,7 @@ for j in range(sim["i"] + 1):
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sim["V"][j]))
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print()
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stats_NFBP(10 ** 4, 10)
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stats_NFBP(10 ** 3, 10)
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N = 10 ** 1
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sim = simulate_NFDBP(N)
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@ -166,12 +212,11 @@ for j in range(sim["i"] + 1):
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sim["V"][j]))
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print()
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stats_NFDBP(10 ** 4, 10)
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stats_NFBP_iter(10**6, 10)
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stats_NFBP_iter(10**3, 10)
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stats_NFDBP(10 ** 3, 10)
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#
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#pyplot.plot([1, 2, 4, 4, 2, 1], color = 'red', linestyle = 'dashed', linewidth = 2,
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#markerfacecolor = 'blue', markersize = 5)
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#pyplot.ylim(0, 5)
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#pyplot.title('Un exemple')
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#show()
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