daily pastebin goal
62%
SHARE
TWEET

countgain8.py

dechicom Aug 20th, 2018 67 Never
Not a member of Pastebin yet? Sign Up, it unlocks many cool features!
  1. #! /usr/bin/python
  2. import copy as CO
  3. import bisect as BI
  4. import math as MA
  5. '''
  6. dice game
  7. copyright 2018 Marc Dechico
  8. '''
  9.  
  10. class CountingValueError(ValueError):
  11.     pass
  12.  
  13. def arangements_ie( n, l ):
  14.     """ arrangements_ie ( n,( k1, k2, k3,...) )
  15.    Return the number of possible arrangements of n elements
  16.    with k1, k2,.... elements being indistinguishable.
  17.    """
  18.     a= MA.factorial( n )
  19.     K=0
  20.     for k in l:
  21.         K += k
  22.         if K > n:
  23.             raise CountingValueError ("Can't have k indistinguishable objects taken from n objects if k > n")
  24.         a= a / MA.factorial( k )
  25.     return a
  26. def result(result):
  27.     print "gain\t\tresult"
  28.     for r in result:
  29.         pass
  30.  
  31.  
  32. def storegain(trialgain,trialproba):
  33.     #print "### trial gain:",trialgain,"|proba:",trialproba*100,"%"
  34.     global tp
  35.     tp+=trialproba
  36.     i = BI.bisect_right(totwin,trialgain) -1
  37.     totproba [i] += trialproba
  38.  
  39. def trial(t):
  40.     global g_no
  41.     global g_N
  42.     trialproba = 0.0
  43.     trialgain = 0.0
  44.     for i  in range (g_no):
  45.         k=t[i]
  46.         if k!=0:
  47.             p = gamewin[i][0]
  48.             gain = gamewin[i][1]
  49.             trialgain += gain * k
  50.             proba=MA.pow(p,k)
  51.             if trialproba == 0:
  52.                 trialproba= proba
  53.             else:
  54.                trialproba*= proba
  55.             #print "-- outcome:",i,"|k:",k,"|p:",p,"|totgain:", gain * k
  56.     trialproba*=arangements_ie(g_N,t)
  57.                
  58.     storegain(trialgain, trialproba)
  59.  
  60.  
  61. def callback ( t):
  62.  
  63.     #print t
  64.     #print "."
  65.     global cpt
  66.     cpt+=1
  67.     trial(t)
  68.  
  69. def rec_gen ( co , t ,s ):
  70.     '''
  71.     N: number of experiment still  available
  72.     co: current outcome (position in t )
  73.     t : list of occurence number for each outcome
  74.    '''
  75.    
  76.     for i in xrange ( 0,g_N-s+1 ):
  77.         t[ co ] = i
  78.         ss=s+i
  79.         if ss == g_N :
  80.              callback ( t )
  81.              continue
  82.  
  83.         if  co > 0 :
  84.             t2 = CO.copy ( t )
  85.             rec_gen ( co-1 , t2,ss )
  86.  
  87.     del(t)
  88.     return
  89.  
  90. def gen ( no , N ):
  91.     '''
  92.    no: number outcome  
  93.    N : Number of subsequent experiment (trial of N experiment)
  94.    t : contain the number of occurence for each outcomes
  95.    '''
  96.     global g_no  # number of outcome
  97.     g_no = no
  98.     global g_N  # number of experiment
  99.     g_N = N
  100.     print "===", g_no,g_N
  101.     t = [ 0 ] * no
  102.     rec_gen ( no-1 , t,0 )
  103.    
  104. if __name__ == "__main__":
  105.  
  106.     # number of expiriment (how many time I throw the dices)
  107.     N = 100
  108.  
  109.     #list of outcomes  (proba, gain)
  110.     gamewin = [ [1.0/6, 200], [2.0/6, 10], [3.0/6, 0] ]
  111.  
  112.     # game expectation E(X)
  113.     gameexpect= 0
  114.     for g in gamewin:
  115.         gameexpect += g[ 0 ] * g[ 1 ]
  116.     print "Game expectation = ", gameexpect
  117.     # maxwin after N experiments
  118.     maxwin = gameexpect * N
  119.  
  120.     # to have the total gains  we get after N experiments  well distributed in
  121.     #  the result  list [(gains,proba), .... ]
  122.    
  123.     totwin = range( 0, int(maxwin), int(maxwin/10))
  124.     totproba = [0.0] * len( totwin )
  125.  
  126.     cpt = 0
  127.     tp=0
  128.     gen( len(gamewin),N )
  129.     print "cpt",cpt,"tp",tp
  130.     result = zip( totwin , totproba)
  131.     print result
  132.     # verifying that the calculus are corrects total probability for all final
  133.     # gains = 1
  134.     print "should get near 1:",sum(totproba)
RAW Paste Data
We use cookies for various purposes including analytics. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. OK, I Understand
 
Top