# BUTT DESTROYER V2

This program has been disqualified.

 Author JFreegman Submission date 2012-07-27 22:37:25.891673 Rating 6484 Matches played 40 Win rate 67.5

## Source code:

``````# Author: JFreegman
# Contact: JFreegman@gmail.com
# Date: July 27, 2012
# v2.0

# All code is written from scratch. The general idea is based off Iocaine Powder
# by Dan Egnor (http://ofb.net/~egnor/iocaine.html).

import random

def get_history_match(hist, n=200):
start = len(hist) - min(len(hist) / 2, n)
end = len(hist)
for i in xrange(start, end):
partition = hist[i:end]
match = hist[:-1].find(partition)
if match != -1:
return hist[match+len(partition)]
return random_weapon()

def get_probs():
freqs = tot_move_freq
probs = {}
probs['R'] = float(freqs ['R']) / freqs ['total']
probs['S'] = float(freqs ['S']) / freqs ['total']
probs['P'] = float(freqs ['P']) / freqs ['total']
return probs

def random_weapon():
return random.choice(['R', 'P', 'S'])

if not input:
last_strats = {}
res_history = []
winning_move = {'R': 'P', 'P': 'S', 'S': 'R'}
losing_move = {'R': 'S', 'P': 'R', 'S': 'P'}
tot_move_freq = {'R': 0, 'P': 0, 'S': 0, 'total': 0}
opp_moves = ""
strat_success = {'hist': 0, 'random': 0, 'freqtot': 0, 'my_hist': 0, 'my_freq': 0,}
output = random_weapon()
my_moves = output
else:
# update strategy success rates based on last round results
opp_moves += input
last_opp_move = input
tot_move_freq[last_opp_move] += 1
tot_move_freq['total'] += 1
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

# opponent's most probable move based on frequency and historic patterns
opp_freqtot = get_probs()
opp_prob_f_tot = max(opp_freqtot, key=opp_freqtot.get)
opp_prob_h = get_history_match(opp_moves)

# my most probable move based on history pattern matches
my_prob_h = get_history_match(my_moves, 150)

# naive moves for each strategy
my_move_freqtot = winning_move[opp_prob_f_tot]
my_move_hist = winning_move[opp_prob_h]
my_move_my_hist = losing_move[my_prob_h]
random_move = random_weapon()

# dict of all strategies and their move
strats = {'hist': my_move_hist, 'random': random_move,
'freqtot': my_move_freqtot,'my_hist': my_move_my_hist}

# Pick the strategy with the highest current success rate
strat = max(strats, key=lambda x: strat_success[x])
output = strats[strat]
my_moves += output
last_strats = strats``````