From e479b6ae1db323fee59fea8ff9ddee3e6e0173f6 Mon Sep 17 00:00:00 2001
From: Paul ALNET <paul@shimaore.net>
Date: Mon, 13 Mar 2023 10:25:04 +0100
Subject: [PATCH] feat: add NFBP iterative algo

---
 Probas.py | 30 +++++++++++++++++++++++++++++-
 1 file changed, 29 insertions(+), 1 deletion(-)

diff --git a/Probas.py b/Probas.py
index 2b58e1c..240a19e 100644
--- a/Probas.py
+++ b/Probas.py
@@ -1,5 +1,5 @@
 from random import random
-from math import floor
+from math import floor, sqrt
 from statistics import mean, variance
 # from matplotlib import pyplot
 
@@ -56,6 +56,33 @@ def stats_NFBP(R, N):
     for n in range(N):
         print("Mean H_{} : {} (variance {})".format(n, mean(H[n]), variance(H[n])))
 
+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.
+    """
+    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)]
+
+    for i in range(R):
+        sim = simulate_NFBP(N)
+        ISum += sim["i"]
+        IVarianceSum += sim["i"]**2
+        for n in range(N):
+            HSum[n] += sim["H"][n]
+            HSumVariance[n] += sim["H"][n]**2
+
+    I = ISum/R
+    IVariance = sqrt(IVarianceSum/(R-1) - I**2)
+
+    print("Mean number of boxes : {} (variance {})".format(I, IVariance))
+    for n in range(n):
+        Hn = HSum[n]/R
+        HVariance = sqrt(HSumVariance[n]/(R-1) - Hn**2)
+        print("Index of box containing the {}th package (H_{}) : {} (variance {})".format(n, n, Hn, HVariance))
 
 def simulate_NFDBP(N):
     """
@@ -140,6 +167,7 @@ for j in range(sim["i"] + 1):
 
 print()
 stats_NFDBP(10 ** 4, 10)
+stats_NFBP_iter(10**6, 10)
 
 #
 # pyplot.plot([1, 2, 4, 4, 2, 1], color = 'red', linestyle = 'dashed', linewidth = 2,