From 7bee845a975fc1509bc45891c79f6bc86628a525 Mon Sep 17 00:00:00 2001
From: Paul ALNET <paul@shimaore.net>
Date: Sun, 4 Jun 2023 08:10:42 +0200
Subject: [PATCH] chore: clean up outputs + add comments

---
 Probas.py | 45 +++++++++++++++++++++++++--------------------
 1 file changed, 25 insertions(+), 20 deletions(-)

diff --git a/Probas.py b/Probas.py
index 14baec0..1c1c00b 100755
--- a/Probas.py
+++ b/Probas.py
@@ -11,11 +11,11 @@ def simulate_NFBP(N):
     """
     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
     T = [0]  # Nombre de paquets de la i-eme boite
     V = [0]  # Taille du premier paquet de la i-eme boite
-    H = []  # Rang de la boite contenant le n-ieme paquet
+    H = []   # Rang de la boite contenant le n-ieme paquet
     for n in range(N):
         size = random()
         if R[i] + size >= 1:
@@ -41,6 +41,7 @@ def simulate_NFBP(N):
     }
 
 
+# unused
 def stats_NFBP(R, N):
     """
     Runs R runs of NFBP (for N items) and studies distribution, variance, mean...
@@ -65,14 +66,19 @@ def stats_NFBP_iter(R, N):
     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.
     """
-    P=R*N
-    print("Running {} NFBP simulations with {} items".format(R, N))
-    ISum = 0
+    P=R*N  # Total number of items
+    print("## Running {} NFBP simulations with {} items".format(R, N))
+    # number of bins
+    ISum = 0  
     IVarianceSum = 0
-    HSum = [0 for _ in range(N)]
+    # index of the bin containing the n-th item
+    HSum = [0 for _ in range(N)] 
     HSumVariance = [0 for _ in range(N)]
+    # number of items in the i-th bin
     Sum_T=[0 for _ in range(N)]
+    # size of the first item in the i-th bin
     Sum_V=[0 for _ in range(N)]
+
     for i in range(R):
         sim = simulate_NFBP(N)
         ISum += sim["i"]
@@ -87,14 +93,13 @@ def stats_NFBP_iter(R, N):
             V.append(0)
         Sum_T=[x+y for x,y in zip(Sum_T,T)]
         Sum_V=[x+y for x,y in zip(Sum_V,V)]
-            #we use round to approximate variations of continuous variable V
-       # Sum_V=  round(sim['V'],2))
     Sum_T=[x/R for x in Sum_T]
     Sum_V=[round(x/R,2) for x in Sum_V]
-    print(Sum_V)
+    #print(Sum_V)
     I = ISum/R
     IVariance = sqrt(IVarianceSum/(R-1) - I**2)
     print("Mean number of bins : {} (variance {})".format(I, IVariance),'\n')
+    # TODO clarify line below
     print(" {} * {} iterations of T".format(R,N),'\n')
     
     for n in range(N):
@@ -102,12 +107,12 @@ def stats_NFBP_iter(R, N):
         HVariance = sqrt(HSumVariance[n]/(R-1) - Hn**2) # Variance
         print("Index of bin containing the {}th item (H_{}) : {} (variance {})".format(n, n, Hn, HVariance))
     HSum=[x/R for x in HSum]
-    print(HSum)
+    # print(HSum)
 #Plotting
     fig = plt.figure()
     #T plot
     x =  np.arange(N)
-    print(x)
+    # print(x)
     ax = fig.add_subplot(221)
     ax.bar(x,Sum_T, width=1,label='Empirical values', edgecolor="blue", linewidth=0.7,color='red')
     ax.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0,3), yticks=np.linspace(0, 3, 5))
@@ -142,12 +147,13 @@ def stats_NFBP_iter(R, N):
 def simulate_NFDBP(N):
     """
     Tries to simulate T_i, V_i and H_n for N items of random size.
+    Next Fit Dual Bin Packing : bins should overflow
     """
-    i = 0  # Nombre de boites
+    i = 0    # Nombre de boites
     R = [0]  # Remplissage de la i-eme boite
     T = [0]  # Nombre de paquets de la i-eme boite
     V = [0]  # Taille du premier paquet de la i-eme boite
-    H = []  # Rang de la boite contenant le n-ieme paquet
+    H = []   # Rang de la boite contenant le n-ieme paquet
     for n in range(N):
         size = random()
         R[i] += size
@@ -178,9 +184,9 @@ def stats_NFDBP(R, N,t_i):
     """
     Runs R runs of NFDBP (for N items) and studies distribution, variance, mean...
     """
-    print("Running {} NFDBP simulations with {} items".format(R, N))
-    P=N*R
-    I = []
+    print("## Running {} NFDBP simulations with {} items".format(R, N))
+    P=N*R  # Total number of items
+    I = [] 
     H = [[] for _ in range(N)]  # List of empty lists
     T=[]
     Tk=[[] for _ in range(N)]
@@ -214,14 +220,13 @@ def stats_NFDBP(R, N,t_i):
         T_maths.append(1/(factorial(u-1))-1/factorial(u))
     E=0
     sigma2=0
-    print("hep")
-    print(T_maths)
+    # print(T_maths)
     for p in range(len(T_maths)):
         E=E+(p+1)*T_maths[p]
         sigma2=((T_maths[p]-E)**2)/(len(T_maths)-1)
     print("Mathematical values : Empiric mean T_{} : {} Variance {})".format(t_i, E, sqrt(sigma2)))
     T_maths=[x*100 for x in T_maths]
-#Plotting
+    #Plotting
     fig = plt.figure()
     #T plot
     x =  np.arange(N)
@@ -277,6 +282,6 @@ for j in range(sim["i"] + 1):
                                                                                                sim["T"][j],
                                                                                                sim["V"][j]))
 
-print()
 stats_NFBP_iter(10**3, 10)
+print('\n\n')
 stats_NFDBP(10 ** 3, 10,1)