This program has been disqualified.
| Author | JFreegman |
| Submission date | 2012-07-26 23:56:15.004528 |
| Rating | 6113 |
| Matches played | 53 |
| Win rate | 64.15 |
# Author: JFreegman
# Contact: JFreegman@gmail.com
# Date: July 25, 2012
# v1.2
# Update from v1.1: a few new meta-strategies and lots of cleanup
# This is the BUTT DESTROYER 9001 v1.1, a rock paper scissors playing bot
# originally written for Udacity's CS212 rock, paper scissors tournament and
# adapted for RPSContest.com.
# All code is written from scratch. The general idea is based off Iocaine Powder
# by Dan Egnor (http://ofb.net/~egnor/iocaine.html).
# What this bot attempts to do is create a prediction heuristic based on
# frequency of the opponent's moves and opp_moves in their history. The bot will
# keep track of the performance of a number of strategies and meta-strategies
# while playing the move that its current most successfull strategy comes up with.
import random
def get_history_match(n=200):
"""
Searches moves history and tries to find previous opp_moves that match
the last n moves (n starting at half the number of moves and decrementing).
If match found, returns the move made after the last pattern match.
"""
start = len(opp_moves) - min(len(opp_moves) / 2, n)
end = len(opp_moves)
for i in xrange(start, end):
partition = opp_moves[i:end]
match = opp_moves[:-1].find(partition)
if match != -1:
return opp_moves[match+len(partition)]
return random_weapon()
def get_probs(total_moves, n):
"""
Returns a dictionary containing the probabilities that the opponent
will make a given move based on their last n moves.
"""
last = get_move_freq(total_moves[-n:])
probs = {}
probs['R'] = float(last['R']) / last['total'] * 100
probs['S'] = float(last['S']) / last['total'] * 100
probs['P'] = float(last['P']) / last['total'] * 100
return probs
def get_move_freq(moves):
"""
Returns a dictionary containing frequencies of moves, as well as
a count for the total number of moves
"""
mov_freq = {'R': 0, 'P': 0, 'S': 0}
count = 0
for move in moves:
if move == 'R':
mov_freq['R'] += 1
elif move == 'P':
mov_freq['P'] += 1
elif move == 'S':
mov_freq['S'] += 1
else:
raise ValueError, 'Invalid move'
count += 1
mov_freq['total'] = count
return mov_freq
def winning_move(m):
"Returns the move that beats m"
d = {'R': 'P', 'P': 'S', 'S': 'R'}
return d[m]
def losing_move(m):
"Returns the move that loses to m"
d = {'R': 'S', 'P': 'R', 'S': 'P'}
return d[m]
def random_weapon():
"Randomly chooses R, P or S"
moves = ['R', 'P', 'S']
return random.choice(moves)
if not input:
last_strats = {}
res_history = []
opp_moves = ""
strat_success = {'freq20': 0, 'c1_freq20': 0, 'c2_freq20': 0, 'hist': 0, 'random': 0,
'c1_hist': 0, 'c2_hist': 0, 'c1_freq100': 0, 'c2_freq100': 0,
'freq100': 0, 'freqtot': 0, 'c1_freqtot': 0, 'c2_freqtot': 0,
'freq5': 0, 'c1_freq5': 0,'c2_freq5': 0, 'hist5': 0,
'hist20': 0, 'c1_hist5': 0, 'c1_hist20': 0, 'c2_hist5': 0,
'c2_hist20': 0,}
output = random_weapon()
else:
# update strategy success rates based on last round results
opp_moves += input
last_opp_move = input
beat_opp = winning_move(last_opp_move)
lose_opp = losing_move(last_opp_move)
for s in last_strats:
if last_strats[s] == beat_opp:
strat_success[s] += 1
elif last_strats[s] == lose_opp:
strat_success[s] -= 1
# get opponent's most probable move based on frequency and history
# pattern matches
opp_freq5 = get_probs(opp_moves, 5)
opp_prob_f_5 = max(opp_freq5, key=opp_freq5.get)
opp_freq20 = get_probs(opp_moves, 20)
opp_prob_f_20 = max(opp_freq20, key=opp_freq20.get)
opp_freq100 = get_probs(opp_moves, 100)
opp_prob_f_100 = max(opp_freq100, key=opp_freq100.get)
opp_freqtot = get_probs(opp_moves, len(opp_moves))
opp_prob_f_tot = max(opp_freqtot, key=opp_freqtot.get)
opp_prob_h = get_history_match()
opp_prob_h20 = get_history_match(20)
opp_prob_h5 = get_history_match(5)
# naive moves for each strategy
my_move_freq5 = winning_move(opp_prob_f_5)
my_move_freq20 = winning_move(opp_prob_f_20)
my_move_freq100 = winning_move(opp_prob_f_100)
my_move_freqtot = winning_move(opp_prob_f_tot)
my_move_hist = winning_move(opp_prob_h)
my_move_hist20 = winning_move(opp_prob_h20)
my_move_hist5 = winning_move(opp_prob_h5)
# counter-moves in case opp predicts my naive moves
c1_move_freq5 = losing_move(my_move_freq5)
c1_move_freq20 = losing_move(my_move_freq20)
c1_move_freq100 = losing_move(my_move_freq100)
c1_move_freqtot = losing_move(my_move_freqtot)
c1_move_hist = losing_move(my_move_hist)
c1_move_hist20 = losing_move(my_move_hist20)
c1_move_hist5 = losing_move(my_move_hist5)
# counter-counter moves in case opp predicts my first counter
c2_move_freq5 = losing_move(c1_move_freq5)
c2_move_freq20 = losing_move(c1_move_freq20)
c2_move_freq100 = losing_move(c1_move_freq100)
c2_move_freqtot = losing_move(c1_move_freqtot)
c2_move_hist = losing_move(c1_move_hist)
c2_move_hist5 = losing_move(c1_move_hist5)
c2_move_hist20 = losing_move(c1_move_hist20)
random_move = random_weapon()
# dict of all available strategies and their move
strats = {'freq20': my_move_freq20, 'freq100': my_move_freq100,
'hist': my_move_hist, 'random': random_move,
'c1_freq20': c1_move_freq20, 'c2_freq20': c2_move_freq20,
'c1_freq100': c1_move_freq100, 'c2_freq100': c2_move_freq100,
'c1_hist': c1_move_hist, 'c2_hist': c2_move_hist,
'freqtot': my_move_freqtot, 'c1_freqtot': c1_move_freqtot,
'c2_freqtot': c2_move_freqtot, 'c1_freq5': c1_move_freq5,
'c2_freq5': c2_move_freq5, 'freq5': my_move_freq5,
'hist5': my_move_hist5, 'hist20': my_move_hist20,
'c1_hist5': c1_move_hist5, 'c1_hist20': c1_move_hist20,
'c2_hist5': c2_move_hist5, 'c2_hist20': c1_move_hist20,}
# Pick the strategy with the highest current success rate
strat = max(strats, key=lambda x: strat_success[x])
output = strats[strat]
last_strats = strats