def cal_value_fector(self,gene):
gene = gene.copy()
Comparison_1 = [1] * 50
Comparison_2 = [3] * 50
(rounds_1, c1_1, c1_2) = _PyPacwar.battle(gene, Comparison_1)
(rounds_2, c2_1, c2_2) = _PyPacwar.battle(gene, Comparison_2)
return (rounds_1 + rounds_2,c1_1 - c1_2,c2_1 - c2_2)
def ref_value(self):
'''
:param gene:
:return value factor of gene combat
'''
gene = self.gene
Comparison_1 = [1] * 50
Comparison_2 = [3] * 50
(rounds_1, c1_1, c1_2) = _PyPacwar.battle(gene, Comparison_1)
(rounds_2, c2_1, c2_2) = _PyPacwar.battle(gene, Comparison_2)
return rounds_1 + rounds_2,c1_1 - c1_2,c2_1 - c2_2
def next(original): #change one
res = []
for i in range(len(original)):
nbr = original[:]
for j in range(4):
nbr[i] = j
(rounds1, c11, c12) = _PyPacwar.battle(nbr, ones)
(rounds2, c21, c22) = _PyPacwar.battle(nbr, threes)
if c11 > c12 and c21 > c22:
res.append([rounds1+rounds2, c11, c12, nbr[:]])
res.sort()
if res==[]:
return []
else:
return res[0]
def main():
res = []
threes = [3] * 50
ones = [1] * 50
for pp in range(4):
origin = [1] * 50
for i in range(50):
test = origin[:]
for j in range(4):
test[i] = j
(rounds1, c11, c12) = _PyPacwar.battle(test, ones)
(rounds2, c21, c22) = _PyPacwar.battle(test, threes)
if (c11 > c12):
res.append([rounds1, test[:]])
res.sort()
print('test gene', res, '\n')
def run_pacWar(GE1, GE2, idx):
(rounds, c1, c2) = _PyPacwar.battle(GE1[idx[0]], GE2[idx[1]])
scores = GAPacWar.get_scores(rounds, c1, c2)
ge1_scores[idx[0]] += scores[0]
ge2_scores[idx[1]] += scores[1]
#print(scores)
return scores
def check_if_champions(pop):
champs = writers.read_champions()
new_champs = []
new_scores = []
for p in pop:
gene1 = p[0]
for champ in champs:
gene2 = champ[0]
if (gene2, 0) not in new_champs:
rounds, survivors1, survivors2 = battle(gene1, gene2)
gene2_score = real_score((gene2, 0), pop)
new_champs.append((gene2, 0))
new_scores.append(gene2_score)
if (gene1, 0) not in new_champs:
if survivors1 > survivors2:
gene1_score = real_score((gene1, 0), pop)
new_champs.append((gene1, 0))
new_scores.append(gene1_score)
new_champs = sort_genes(new_champs, new_scores)[0:12]
writers.clear_champions()
writers.write_genes(new_champs, "champions.txt")
#print(new_champs)
print("Best Gene: ", new_champs[0][0], max(new_scores))
return new_champs
def main(): ones = [1]*50 threes = [3]*50 print "Example Python module in C for Pacwar" print "all ones versus all threes ..." (rounds,c1,c2) = _PyPacwar.battle(ones, threes) print "Number of rounds:", rounds print "Ones PAC-mites remaining:", c1 print "Threes PAC-mites remaining:", c2
def main():
ones = [1] * 50
threes = [3] * 50
print "Example Python module in C for Pacwar"
print "all ones versus all threes ..."
(rounds, c1, c2) = _PyPacwar.battle(ones, threes)
print "Number of rounds:", rounds
print "Ones PAC-mites remaining:", c1
print "Threes PAC-mites remaining:", c2
def main():
minheap = []
for i in range(50):
minheap = []
Gene = [3]*50
Gene = Gene.copy()
for j in range(4):
Gene = Gene.copy()
Gene[i] = j
test_gene = PacMite(Gene)
values = test_gene.value_factor
if values[1] > 0 and values[2] > 0 and values[0]<500:
#print(Gene,values)
heappush(minheap,(Gene,values))
print(minheap[0])
Comparison_1 = [1] * 50
Comparison_2 = [3] * 50
res = [1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
print(_PyPacwar.battle(res, Comparison_1), _PyPacwar.battle(res, Comparison_2))
def run_GA(population):
# generate random population of 50
if len(population) == 0:
for x in range(0,50):
population.append(generate_random_gene())
for run in range(0,100):
# initialize score and freq dictionaries to 0 for each gene in random
# population
population_to_score = {}
population_to_freq = {}
for gene in population:
population_to_score[gene] = 0
population_to_freq[gene] = 0
# battle and score each random gene in population again random competitor
random_competitor = generate_random_gene()
for gene in population:
(rounds, c1, c2) = _PyPacwar.battle(transform_string_to_array(gene), transform_string_to_array(random_competitor))
population_to_score[gene] = score_battle(rounds, c1, c2)
# find the sum of scores
score_sum = 0
for gene in population_to_score:
score_sum += population_to_score[gene]
# find the proportion of each score
for gene in population_to_score:
population_to_freq[gene] = population_to_score[gene]*1.0 / score_sum
# sort population frequencies in desc order
population_to_freq_sorted = sorted(population_to_freq.items(), key=operator.itemgetter(1), reverse=True)
# selection for mating
mating_pool = []
for x in range(0,50):
rand = random.random()
cum_sum = 0
for pair in population_to_freq_sorted:
cum_sum += pair[1]
if (cum_sum > rand):
mating_pool.append(pair[0])
break
population = []
while len(population) < 50:
mom = mating_pool[random.randint(0, 49)]
dad = mating_pool[random.randint(0, 49)]
(child1, child2) = crossover(mom, dad)
population.append(transform_array_to_string(child1))
population.append(transform_array_to_string(child2))
return population[random.randint(0,49)]
def HC(iteration_limit,firstgeneration,initial_mutation_sitenumber):
res=[]
# choose the training set
#random_start=[]
#random_set_length=1
#for _ in range(random_set_length):
#random_start.append([random.randint(0,3) for i in range(50)])
#start=random_start[:][0]
k=500
while k!=0:
select_position_mutation=[random.randint(0,49) for i in range(initial_mutation_sitenumber)]
for i in select_position_mutation:
start=firstgeneration[:]
start[i]=random.randint(0,3)
(rounds1, c11, c12) = _PyPacwar.battle(start, ones)
(rounds2, c21, c22) = _PyPacwar.battle(start, threes)
if c11>c12 and c21>c22:
break
k-=1
currentrounds=rounds1+rounds2
while iteration_limit!=0:
nbrnode=next(start) # best after battle
if nbrnode==[]:
return start
else:
next_gene=nbrnode[3]
next_rounds=nbrnode[0]
if next_gene == start:
return start
else:
if currentrounds <next_rounds:
return start
else:
start=next_gene[:]
iteration_limit -= 1
return next_gene
def dueling_stage(population):
duels = [population[i:i + 2] for i in range(0, len(population), 2)]
newpop = []
for duel in duels:
gene1 = duel[0][0]
gene2 = duel[1][0]
rounds, survivors1, survivors2 = battle(gene1, gene2)
if survivors1 > survivors2:
newpop.append((gene1, 0.0))
else:
newpop.append((gene2, 0.0))
print(len(newpop))
return newpop
def main():
Comparison_1 = [1] * 50
Comparison_2 = [3] * 50
prev = ([1] * 50, 500)
pos = [0, 1, 2, 3]
cur = ([1] * 50, 500)
while prev[1] >= cur[1]:
i = 1
res = []
cur = prev
for i in range(50):
Gene = cur[0].copy()
for j in pos:
Gene[i] = j
Gene.copy()
(rounds_1, c1_1, c1_2) = _PyPacwar.battle(Gene, Comparison_1)
(rounds_2, c2_1, c2_2) = _PyPacwar.battle(Gene, Comparison_2)
if int(c1_1) - int(c1_2) > 0 and c2_1 - c2_2 > 0:
a = _PyPacwar.battle(Gene, Comparison_1)
b = _PyPacwar.battle(Gene, Comparison_2)
heappush(res, (a[0] + b[0], a[0], b[0], Gene))
cur = (res[0][0], res[0][3])
print('Iteration Number:', i, res[0], '/n')
'''
def vanilla_score(gene1, gene2):
rounds, survivors1, survivors2 = battle(gene1, gene2)
i = 0
if survivors2 > survivors1: i = 1
if rounds < 100: return (20, 0)[i]
elif rounds < 200: return (19, 1)[i]
elif rounds < 300: return (18, 2)[i]
elif rounds < 500: return (17, 3)[i]
else:
if survivors1 == 0 or survivors2 == 0:
return (17, 3)[i]
if survivors1 > survivors2: c = survivors1 / survivors2
else: c = survivors2 / survivors1
if c > 10: return (13, 7)[i]
elif c > 3: return (12, 8)[i]
elif c > 1.5: return (11, 9)[i]
return 10
def hillClimb():
current = generateRandomGene()
newBestScore = -9999999
newBest = None
while 1:
for i in range(len(current)):
for j in range(0, 4):
new = list(current)
if current[i] == j:
continue
new[i] = j
(rounds, c1, c2) = _PyPacwar.battle(new, current)
thisScore = battleScore((rounds, c1, c2))
if thisScore >= newBestScore:
newBestScore = thisScore
newBest = new
if newBest != current:
current = newBest
else:
return current
def rankGenes(genes, wantToLog):
n = len(genes)
scores = [0] * n
for i in range(n):
for j in range(i + 1, n):
iscore = battleScore(_PyPacwar.battle(genes[i], genes[j]))
scores[i] += iscore / float(n)
scores[j] += (20 - iscore) / float(n)
scoresAndGenes = zip(genes, scores)
scoresAndGenes.sort(lambda x, y: 1 if x[1] - y[1] > 0 else -1, None, True)
r = 1
for s in scoresAndGenes:
logMsg = "Rank " + str(r) + ", avg score " + str(s[1]) + ": " + str(
s[0]) + "."
print logMsg
if wantToLog:
with open("log.txt", "a") as f:
f.write(logMsg)
r += 1
return scoresAndGenes
def main(): good_population = [] for x in range(0,50): print "GA run#: " + str(x+1) good_population.append(run_GA([])) print "" best_gene = run_GA(good_population) print "Best Gene: " + best_gene # best_gene = "30022011131103123330231133202322103221230311223123" print "" # test "best" gene against 100 random ones. print stats num_wins = 0 num_losses = 0 num_rounds = 0 num_remaining = 0 for x in range(0,250): random_gene = generate_random_gene() (rounds,c1,c2) = _PyPacwar.battle(transform_string_to_array(best_gene), transform_string_to_array(random_gene)) if c1 == 0: num_losses += 1 else: num_wins += 1 num_remaining += c1 num_rounds += rounds percentage_win = 1.0*num_wins/250 avg_rounds = 1.0*num_rounds/num_wins avg_remaining = 1.0*num_remaining/num_wins print "Num Wins: " + str(num_wins) print "Num Losses: " + str(num_losses) print "Percentage Win: " + str(percentage_win) print "Average Rounds to Win: " + str(avg_rounds) print "Average Remaining when Win:" + str(avg_remaining)
def battleResults(gene1, gene2, wantToLog):
print "gene1: " + str(gene1)
print "gene2: " + str(gene2)
(rounds, c1, c2) = _PyPacwar.battle(gene1, gene2)
gene1Score = battleScore((rounds, c1, c2))
gene2Score = 20 - gene1Score
print "Number of rounds:", rounds
print "gene1 PAC-mites remaining:", c1
print "gene2 PAC-mites remaining:", c2
print "gene1 score: " + str(gene1Score) + ". gene2 score: " + str(
gene2Score)
print ""
if wantToLog:
with open("log.txt", "a") as f:
f.write("gene1: " + str(gene1) + "\n")
f.write("gene2: " + str(gene2) + "\n")
f.write("Number of rounds:" + str(rounds) + "\n")
f.write("gene1 PAC-mites remaining:" + str(c2) + "\n")
f.write("gene2 PAC-mites remaining:" + str(c2) + "\n")
f.write("gene1 score: " + str(gene1Score) + ". gene2 score: " +
str(gene2Score) + "\n")
f.write("\n\n")
def outcome_of_duel(i, j): #i and j are in list
(rounds, c1, c2) = _PyPacwar.battle(i, j)
if c1 > c2: # i win
if rounds < 100:
return 20, 0
elif rounds < 199:
return 19, 1
elif rounds < 299:
return 18, 2
elif rounds < 500:
return 17, 3
elif c1 / c2 > 10:
return 13, 7
elif c1 / c2 > 3:
return 12, 8
elif c1 / c2 > 1.5:
return 11, 9
else:
return 10, 10
elif c1 == c2:
return 10, 10
else:
if rounds < 100:
return 0, 20
elif rounds < 199:
return 1, 19
elif rounds < 299:
return 2, 18
elif rounds < 500:
return 3, 17
elif c1 / c2 > 10:
return 7, 13
elif c1 / c2 > 3:
return 8, 12
elif c1 / c2 > 1.5:
return 9, 11
else:
return 10, 10
def main():
# read the input file
candidates = []
with open('dummy.txt', 'r') as f:
candidates_temp = f.readlines()
for i in range(len(candidates_temp)):
temp1 = candidates_temp[i].strip('\n')
if temp1 not in candidates:
candidates.append(temp1)
# read the test set
with open('testset.txt', 'r') as f:
test_warrior = f.readlines()
for i in range(len(test_warrior)):
test_warrior[i] = test_warrior[i].strip('\n')
test_warrior = test_warrior[0:10000]
# battle now!!
battle_res = []
for i in candidates:
score = 0
candidates_ = [int(x) for x in i]
wintimes = 0
for j in test_warrior:
(rounds, c1, c2) = _PyPacwar.battle(candidates_,
[int(x) for x in j])
if c1 > c2: # win!!
wintimes += 1
if rounds < 50:
score += 3
elif rounds < 200:
score += 2
else:
score += 1
battle_res.append([score, wintimes, i])
battle_res.sort(reverse=1)
with open('battleres.txt', 'a') as f:
for m in battle_res:
f.write(str(m) + '\n')
def hillClimbVSCompetitors(competitors,
restartUntilBetterFound,
mutateOrNot,
wantToLog,
startGene=generateRandomGene(),
restartFunction=None,
restartThresholdScore=10,
strictlyBeats=False,
mutateAmount=4):
current = startGene
newBestScore = -9999999
newBest = None
newGeneSucks = False
preMutateBest = None
preMutateBestScore = 0
roundsStuckAtMutation = 0
roundsStuckThreshold = 200
while 1:
keepClimbing = False
new = list(current)
for i in range(len(current)):
og = new[i]
geneSucks = False
for j in range(0, 4):
if new[i] == j:
continue
new[i] = j
totalScore = 0
for competitor in competitors:
(rounds, c1, c2) = _PyPacwar.battle(new, competitor)
theScore = battleScore((rounds, c1, c2))
if strictlyBeats and theScore <= restartThresholdScore:
geneSucks = True
# print "Gene sucks! Only got score of " + str(theScore) + " while required is " + str(restartThresholdScore) + " versus gene " + str(competitor)
totalScore += theScore
# print "Avg = " + str(float(totalScore) / len(competitors))
if totalScore > newBestScore:
keepClimbing = True
newBestScore = totalScore
newBest = list(new)
newGeneSucks = geneSucks
# print "Found better gene with score " + str(newBestScore) + ". " + str(newBest)
new[i] = og
if keepClimbing:
current = newBest
else:
if restartUntilBetterFound and (
float(newBestScore) / len(competitors) <=
restartThresholdScore or newGeneSucks):
# print "Hill climb found local maxima, and it sucked with average score of " + \
# str(float(newBestScore)/len(competitors)) + "..."
if mutateOrNot or restartFunction is None:
if newBestScore > preMutateBestScore:
preMutateBest = list(current)
preMutateBestScore = newBestScore
print "New Best Score: " + str(newBestScore)
roundsStuckAtMutation = 0
else:
roundsStuckAtMutation += 1
if roundsStuckAtMutation == roundsStuckThreshold:
print "Stuck at mutation for " + str(
roundsStuckThreshold
) + " rounds! Mega mutating."
# Fishy line below. This will most probably decrease the strength of the preMutateBest...
# Not sure if this is the correct approach.
preMutateBest = mutate(preMutateBest, wantToLog,
mutateAmount * 2)
roundsStuckAtMutation = 0
current = list(preMutateBest)
current = mutate(current, wantToLog, mutateAmount)
else:
current = restartFunction()
newBest = None
newBestScore = -9999999
newGeneSucks = False
else:
logMsg = "Hill Climb found "
if strictlyBeats:
logMsg += "strictly "
logMsg += "better gene! " + str(current)
print logMsg
if wantToLog:
with open("log.txt", "a") as f:
f.write(logMsg + "\n")
return current
def evaluate(self, GE1, GE2, parallel=True, verbose=0):
'''
GE1: genes for specie 1
GE2: genes for specie 2
'''
#assert len(GE1) == len(GE2)
#pop_size = len(GE1)
#start_time = time.time()
if parallel:
assert verbose == 0
global ge1_scores
global ge2_scores
ge1_scores = Array('d', [0] * len(GE1))
ge2_scores = Array('d', [0] * len(GE2))
self.p = Pool(multiprocessing.cpu_count())
for i in xrange(len(GE1)):
for j in xrange(len(GE2)):
# bottleneck
res = self.p.apply_async(run_pacWar,
args=(
GE1,
GE2,
(i, j),
))
#scores = res.get(timeout=1)
if verbose:
print(
"The duel lasted %d rounds, specie 1 remains: %d, specie 2 remains: %d"
% (rounds, c1, c2))
print(
"The final scores for GE1[%d] vs GE2[%d] are: %d : %d"
% (i, j, scores[0], scores[1]))
self.p.close()
self.p.join()
ge1_scores = list(ge1_scores)
ge2_scores = list(ge2_scores)
#print(time.time() - start_time)
if not parallel:
ge1_scores = [0.0] * len(GE1)
ge2_scores = [0.0] * len(GE2)
for i in xrange(len(GE1)):
for j in xrange(len(GE2)):
# bottleneck
(rounds, c1, c2) = _PyPacwar.battle(GE1[i], GE2[j])
scores = self.get_scores(rounds, c1, c2)
ge1_scores[i] += scores[0]
ge2_scores[j] += scores[1]
if verbose:
print(
"The duel lasted %d rounds, specie 1 remains: %d, specie 2 remains: %d"
% (rounds, c1, c2))
print(
"The final scores for GE1[%d] vs GE2[%d] are: %d : %d"
% (i, j, scores[0], scores[1]))
ge1_scores = [i / len(GE2) for i in ge1_scores]
ge2_scores = [i / len(GE1) for i in ge2_scores]
return ge1_scores, ge2_scores
def main():
base_threes = [3] * 50
threes = [3] * 50
nfittest = 50
initial = []
initial += [[
0, 1, 1, 0, 3, 3, 1, 1, 3, 0, 2, 2, 1, 1, 3, 2, 2, 3, 3, 2, 0, 2, 2, 3,
2, 1, 3, 2, 3, 1, 3, 1, 3, 1, 1, 1, 1, 2, 3, 0, 2, 1, 0, 1, 1, 2, 3, 1,
2, 3
],
[
3, 0, 3, 3, 3, 1, 1, 3, 0, 0, 1, 2, 3, 1, 2, 3, 3, 3, 0, 1,
2, 3, 0, 1, 1, 1, 1, 0, 0, 3, 1, 0, 3, 3, 0, 2, 2, 1, 1, 1,
0, 0, 1, 2, 3, 1, 2, 2, 1, 2
],
[
3, 3, 2, 0, 3, 2, 1, 2, 1, 0, 0, 3, 0, 1, 1, 1, 1, 1, 3, 3,
2, 0, 0, 1, 2, 3, 3, 3, 3, 3, 2, 1, 0, 3, 3, 1, 2, 0, 2, 0,
1, 1, 2, 2, 2, 3, 0, 1, 1, 0
],
[
3, 1, 1, 1, 3, 1, 0, 1, 2, 2, 0, 1, 3, 1, 0, 2, 2, 3, 2, 2,
1, 1, 2, 2, 2, 3, 0, 0, 0, 1, 2, 0, 2, 1, 3, 3, 0, 1, 1, 1,
0, 3, 3, 0, 1, 3, 3, 1, 0, 3
],
[
2, 0, 0, 0, 3, 1, 1, 3, 2, 0, 2, 3, 1, 3, 1, 3, 2, 2, 2, 2,
0, 2, 0, 1, 0, 0, 1, 3, 3, 3, 2, 3, 0, 2, 1, 2, 2, 0, 2, 3,
2, 1, 1, 0, 3, 3, 0, 2, 3, 1
],
[
2, 1, 1, 3, 3, 2, 1, 3, 0, 2, 0, 0, 1, 1, 1, 1, 1, 0, 3, 3,
0, 2, 3, 2, 0, 0, 2, 1, 0, 0, 3, 0, 2, 0, 1, 0, 0, 2, 1, 0,
2, 0, 3, 0, 3, 1, 1, 2, 2, 1
],
[
2, 2, 3, 0, 3, 1, 0, 0, 3, 0, 3, 0, 3, 2, 1, 3, 0, 2, 0, 0,
1, 0, 0, 0, 3, 3, 1, 3, 0, 3, 3, 0, 0, 1, 2, 1, 1, 0, 2, 1,
1, 3, 0, 2, 0, 2, 1, 0, 2, 1
],
[
0, 1, 3, 0, 3, 3, 3, 3, 1, 0, 2, 0, 0, 1, 2, 2, 3, 0, 3, 3,
1, 3, 3, 3, 2, 3, 0, 3, 1, 1, 3, 2, 3, 2, 2, 0, 2, 2, 0, 3,
0, 2, 3, 0, 1, 0, 2, 0, 1, 0
],
[
0, 3, 1, 3, 2, 1, 3, 2, 0, 1, 0, 0, 3, 0, 2, 3, 3, 2, 0, 1,
1, 1, 1, 1, 2, 2, 1, 2, 1, 2, 2, 1, 2, 1, 0, 0, 3, 3, 0, 2,
3, 2, 3, 0, 0, 2, 3, 3, 3, 1
]]
for i in range(0, 75):
initial += [randomSpecies()]
while True:
myHeap = []
for i in range(0, len(initial)):
(rounds, c1, c2) = _PyPacwar.battle(initial[i], threes)
if c2 < c1:
print "Win against 3! "
print initial[
i], " candidate: ", c1, " threes: ", c2, " rounds: ", rounds
heapq.heappush(myHeap, (-20 - (c1 / (c2 + 1.0)), initial[i]))
continue
else:
score = -(pow(c1, 0.5) + pow(rounds, 0.3) +
(14.0 / math.log(c2 - c1 + 2, 2)))
# if i<10:
# print initial[i]," candidate: ",c1," threes: ",c2," rounds: ",rounds," score: ",score
heapq.heappush(myHeap, (score, initial[i]))
fittest = heapq.nsmallest(
nfittest, myHeap
) #fittest is a list of 'nfittest' no. of tuples where each tuple is (score,gene)
(rounds, c1, c2) = _PyPacwar.battle(fittest[0][1], threes)
print()
print "Fittest after tournament: ", fittest[
0], " candidate: ", c1, " threes: ", c2, " rounds: ", rounds
(rounds, c1, c2) = _PyPacwar.battle(fittest[1][1], threes)
print "2nd Fittest after tournament: ", fittest[
1], " candidate: ", c1, " threes: ", c2, " rounds: ", rounds
initial = crossAndMutate(fittest)
print "intial size: ", len(initial)
