From c0dff5ad9de38ac3250b4c732a8cef686fd75c6f Mon Sep 17 00:00:00 2001
From: Clement Lacau <lacau@noreply.etud.insa-toulouse.fr>
Date: Mon, 13 Mar 2023 10:05:27 +0100
Subject: [PATCH] =?UTF-8?q?Transf=C3=A9rer=20les=20fichiers=20vers=20''?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 Probas.py | 149 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 149 insertions(+)
 create mode 100644 Probas.py

diff --git a/Probas.py b/Probas.py
new file mode 100644
index 0000000..2b58e1c
--- /dev/null
+++ b/Probas.py
@@ -0,0 +1,149 @@
+from random import random
+from math import floor
+from statistics import mean, variance
+# from matplotlib import pyplot
+
+def simulate_NFBP(N):
+    """
+    Tries to simulate T_i, V_i and H_n for N boxes of random size.
+    """
+    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
+    for n in range(N):
+        size = random()
+        if R[i] + size >= 1:
+            # Il y n'y a plus de la place dans la boite pour le paquet.
+            # On passe à la boite suivante (qu'on initialise)
+            i += 1
+            R.append(0)
+            T.append(0)
+            V.append(0)
+        R[i] += size
+        T[i] += 1
+        if V[i] == 0:
+            # C'est le premier paquet de la boite
+            V[i] = size
+        H.append(i)
+
+    return {
+        "i": i,
+        "R": R,
+        "T": T,
+        "V": V,
+        "H": H
+    }
+
+
+def stats_NFBP(R, N):
+    """
+    Runs R runs of NFBP (for N packages) and studies distribution, variance, mean...
+    """
+    print("Running {} NFBP simulations with {} packages".format(R, N))
+    I = []
+    H = [[] for _ in range(N)]  # List of empty lists
+
+    for i in range(R):
+        sim = simulate_NFBP(N)
+        I.append(sim["i"])
+        for n in range(N):
+            H[n].append(sim["H"][n])
+
+    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])))
+
+
+def simulate_NFDBP(N):
+    """
+    Tries to simulate T_i, V_i and H_n for N boxes of random size.
+    """
+    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
+    for n in range(N):
+        size = random()
+        R[i] += size
+        T[i] += 1
+        if R[i] + size >= 1:
+            # Il y n'y a plus de la place dans la boite pour le paquet.
+            # On passe à la boite suivante (qu'on initialise)
+            i += 1
+            R.append(0)
+            T.append(0)
+            V.append(0)
+
+        if V[i] == 0:
+            # C'est le premier paquet de la boite
+            V[i] = size
+        H.append(i)
+
+    return {
+        "i": i,
+        "R": R,
+        "T": T,
+        "V": V,
+        "H": H
+    }
+
+
+def stats_NFDBP(R, N):
+    """
+    Runs R runs of NFDBP (for N packages) and studies distribution, variance, mean...
+    """
+    print("Running {} NFDBP simulations with {} packages".format(R, N))
+    I = []
+    H = [[] for _ in range(N)]  # List of empty lists
+    Tmean=[]
+    for i in range(R):
+        sim = simulate_NFDBP(N)
+        I.append(sim["i"])
+        for n in range(N):
+            H[n].append(sim["H"][n])
+
+        for k in range(sim["i"]):
+            # for o in range(sim["i"]):
+                Tmean+=sim["T"]
+    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):
+        print(Tmean[7])
+       # print("Mean T_{} : {} (variance {})".format(k, mean(Tmean[k]), variance(Tmean[k])))
+
+N = 10 ** 1
+sim = simulate_NFBP(N)
+
+print("Simulation NFBP pour {} packaets. Contenu des boites :".format(N))
+for j in range(sim["i"] + 1):
+    remplissage = floor(sim["R"][j] * 100)
+    print("Boite {} : Rempli à {} % avec {} paquets. Taille du premier paquet : {}".format(j, remplissage, sim["T"][j],
+                                                                                           sim["V"][j]))
+
+print()
+stats_NFBP(10 ** 4, 10)
+
+N = 10 ** 1
+sim = simulate_NFDBP(N)
+print("Simulation NFDBP pour {} packaets. Contenu des boites :".format(N))
+for j in range(sim["i"] + 1):
+    remplissage = floor(sim["R"][j] * 100)
+    print("Boite {} : Rempli à {} % avec {} paquets. Taille du premier paquet : {}".format(j, remplissage,
+                                                                                               sim["T"][j],
+                                                                                               sim["V"][j]))
+
+print()
+stats_NFDBP(10 ** 4, 10)
+
+#
+# pyplot.plot([1, 2, 4, 4, 2, 1], color = 'red', linestyle = 'dashed', linewidth = 2,
+#   markerfacecolor = 'blue', markersize = 5)
+# pyplot.ylim(0, 5)
+# pyplot.title('Un exemple')
+