| Author | Barlow |
| Submission date | 2013-12-06 06:39:25.633760 |
| Rating | 4762 |
| Matches played | 642 |
| Win rate | 49.07 |
Use rpsrunner.py to play unranked matches on your computer.
'''
Barlow Q Odin
written 12/5/2013
added several different strategies
'''
import random
import operator
def new_mutant():
ranked = sorted(xmen, key=lambda recruit: recruit[1])
best = ranked[len(ranked)-1]
second = ranked[len(ranked)-2]
new_genes = []
for gene_num in range(best[0]):
if best[0][gene_num] == 0 and second[0][gene_num]:
new_genes[gene_num] = random.choice(strats)
elif best[0][gene_num] == 0:
new_genes[gene_num] = second[gene_nume]
elif second[0][gene_num] == 0:
new_genes[gene_num] = best[gene_num]
else:
new_genes[gene_num] = random.choice([best[gene_num], second[gene_num]])
new_mutant = (list(new_genes), 0, 0) #new mutant with inherited genes, zero mutant power, and age of zero
xmen.append(new_mutant)
def apocalypse():
for baggage in xmen:
if baggage[2] == num_genes or baggage[1] < -10: #mutant is too old or has lost too much power
xmen.remove(baggage)
new_mutant()
if not input:
#setup stage
beats = {'P': 'S', 'S': 'R', 'R':'P'}
my_history = "" #historys are strings of previous choices
opp_history = ""
hind_sight = 3
size_of_xmen = 50
num_genes = 30
strats = ["pattern", "Lizard", "Spock", "Shotgun", "Jellyfish", "Same", "Ditto", "Dynamite", "Zombie", "Mummy"]
strat_predict = {"pattern": 0, "Lizard": 0, "Spock": 0, "Shotgun": 0, "Jellyfish": 0, "Same": 0, "Ditto": 0, "Dynamite": 0, "Zombie": 0, "Mummy": 0}
#Possible strategies:
#pattern is pure pattern matching
#Lizard is Anti-Rotation of pattern
#Spock is Rotation of pattern
#Shotgun is Anti-Rotation of opponent's previous
#Jellyfish is Rotation of opponent's previous
#Same is the same move of opponent's previous move
#Ditto is copying my previous move
#Dynamite is Random
#Zombie is Decayed Frequency Maximum
#Mummy is Decayed Frequency Minimum
frequency = {'P': 0, 'S': 0, 'R': 0}
xmen = []
for i in range(size_of_xmen): #create a new set of xmen, hopefully no one like Choir
genes = []
mutation_level = 0 #mutation level is the mutant's current strength
age = 0 #how long has the mutant been alive? hard to tell with Wolverine...
for j in range(num_genes): #create a genome for this mutant randomly of specified length
genes += random.choice(strats)
mutant = (list(genes),mutation_level, age)
xmen += tuple(mutant) #give the mutant a uniform and add them to the roster of the xmen
output = random.choice(['R','P','S'])
elif len(my_history) <= 3: #random choice at beginning
output = random.choice(['R','P','S'])
else:
last_move = output
last_opp_move = input
if beats[input] == output: #won
won = True
else: #tie/loss
if output == input:
tied = True
else:
tied = False
won = False
for hero in xmen: #cycle through mutants
age = hero[2]
mutant_power = hero[0][age] #mutant power = current strat choice
if beats[input] == strat_predict[mutant_power]: #if mutant power would have won, add to their mutation level
hero[1] += 2
elif beats[strat_predict[mutant_power]] == input: #if mutant power would have lost, subtract from the mutation level
hero[1] -= 2
hero[0][age] = 0 #strip the gene from the genome for later replacement if it reproduces
else:
hero[1] += .5 #mild increase of mutant power for a tie, but can keep gene
hero[2] += 1 #age mutant by 1
apocalypse()
frequency[input] += 1
for item in frequency:
frequency[item] = 0.9 * frequency[item] #Decay all frequencies
my_history += output #concat my previous choice to my history
opp_history += input #concat opponent's previous choice to their history
predict = {'P': 0, 'S': 0, 'R': 0}
counter = 1
if len(my_history) < 50: #only look at the last 50 matches for history
begin = 0
else:
begin = len(my_history) - 50
while counter <= hind_sight:
current_my_match = my_history[-counter:] #my recent choices
current_opp_match = opp_history[-counter:] #opponent recent choices
while begin <= len(my_history) - counter: #step through history strings until loop reaches current length of recent choices
if current_my_match == my_history[begin:begin+counter] and current_opp_match == opp_history[begin:begin+counter]: #compare recent choices to previous history
predict[opp_history[begin+counter+1]] += counter * (len(my_history) - begin) / len(my_history) #if similar decision path is found, increment prediction based on amount of recent choices that were matched
if predict['R'] > predict['S'] and predict['R'] > predict['P']:
prediction = 'R'
elif predict['S'] > predict['P'] and predict['S'] > predict['R']:
prediction = 'S'
else:
prediction = 'P'
strat_predict["pattern"] = prediction #record predictions from each strategy
strat_predict["Lizard"] = beats[beats[prediction]]
strat_predict["Spock"] = beats[prediction]
strat_predict["Shotgun"] = beats[beats[last_opp_move]]
strat_predict["Jellyfish"] = beats[last_opp_move]
strat_predict["Same"] = last_opp_move
strat_predict["Ditto"] = last_move
strat_predict["Dynamite"] = random.choice(['R','P','S'])
best = 0
worst = 100000
for item in frequency:
if frequency[item] < worst:
worst = frequency[item]
strat_predict["Mummy"] = item
if frequency[item] > best:
best = frequency[item]
strat_predict["Zombie"] = item
rogue = max(xmen, key=itemgetter(1)) #rogue takes on the powers of the best mutant
rogue_age = rogue[2]
chosen_power = rogue[0][rogue_age] #strategy gene at this mutant's age
output = strat_predict[chosen_power]