Bleu Snow

Bleu Snow

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Conver Python Code to C# Code

Apr 3 2010 10:53 PM

Hi all, recently, my fren send me a python code file which i have limited knowledge about it.. so i hope someone here can help me to convert the code below to c# and thanks in advance! =)
 from random import randint,random
from operator import add
from copy import deepcopy

def individual(length, min, max):
    #'Create a member of the population.'
    return [ randint(min,max) for x in xrange(length) ]
def population(count, length, min, max):
    return [ individual(length, min, max) for x in xrange(count) ]

def fitness(individual, target):
     sum = reduce(add, individual, 0)
     return abs(target-sum)
def grade(pop, target):
     summed = reduce(add, (fitness(x, target) for x in pop), 0)
     return summed / (len(pop) * 1.0)
def evolve(pop, target, retain=0.2, random_select=0.05, mutate=0.01):
    graded = [ (fitness(x, target), x) for x in pop]
    graded = [ x[1] for x in sorted(graded)]
    retain_length = int(len(graded)*retain)
    parents = graded[:retain_length]
    # randomly add other individuals to promote genetic diversity
    for individual in graded[retain_length:]:
        if random_select > random():
            parents.append(individual)
           
    # mutate some individuals
    for individual in parents:
        if mutate > random():
            pos_to_mutate = randint(0, len(individual)-1)
            # this mutation is not ideal, because it
            # restricts the range of possible values,
            # but the function is unaware of the min/max
            # values used to create the individuals,
            individual[pos_to_mutate] = randint(
                min(individual), max(individual))
   
    # crossover parents to create children
    parents_length = len(parents)
    desired_length = len(pop) - parents_length
    children = []
    while len(children) < desired_length:
        male = randint(0, parents_length-1)
        female = randint(0, parents_length-1)
        if male != female:
            male = parents[male]
            female = parents[female]
            half = len(male) / 2
            child = male[:half] + female[half:]
            children.append(child)
    parents.extend(children)
    return parents
 
iTarget = 76
p = population(20,6,0,40)
fitness_history = [grade(p, iTarget),]
arBestP = []
iTryGA = 1000 #try thousand times
iMxScore = iTarget
iLoopCnt = iTryGA
for i in xrange(iTryGA):
   p = evolve(p, iTarget)
   iScore = grade(p, iTarget)
   fitness_history.append(iScore)
   if iScore < iMxScore:
       iMxScore = iScore
       arBestP = deepcopy(p)
   if iScore == 0:
       iLoopCnt = i
       break;
 
print "mx score = %s loop count = %s" % (iMxScore,iLoopCnt)   
print "Target:%s Best GA Pick:%s = %s" % (iTarget, arBestP[0],reduce(add, arBestP[0], 0))
#for datum in fitness_history:
 #  print datum