Adding T,V,H graphs for NFBP algorithm

Probas.py

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