Author | zdg |

Submission date | 2012-04-21 02:22:16.816997 |

Rating | 7382 |

Matches played | 681 |

Win rate | 76.8 |

Use rpsrunner.py to play unranked matches on your computer.

```
# greedy history pattern match
# greedy - use highest available order and most recent
# order 7
# use my hands, op hands, as well as both to predict next op hand
# switch using a fsa between all 6+6+6 = 18 ways of doing it
# update only the orders that are >= the order last used to make the prediction
# - something from PPM compression
# --------------------- initialization -----------------------------
if not input:
import random, collections
# micro-optimizations
rchoice = random.choice
randint = random.randint
uniform = random.uniform
# global constants and maps
R, P, S = 0, 1, 2
RPS = [R, P, S]
T, W, L = 0, 1, 2
PAYOFFS = [T, W, L]
tonum = {'R':R, 'P':P, 'S':S}
tostr = {R:'R', P:'P', S:'S'}
scoreround = [[T, L, W], [W, T, L], [L, W, T]]
frompayoff = [[R, P, S], [P, S, R], [S, R, P]]
ties, beats, loses = frompayoff[T], frompayoff[W], frompayoff[L]
# more specific variables
topoints = [0.0, 1.0, -1.0]
encode1hand = [1,2,3]
decode1hand = [None, R, P, S]
encode2hands = [[1,2,3], [4,5,6], [7,8,9]]
decode2hands = [None,(R,R),(R,P),(R,S),(P,R),(P,P),(P,S),(S,R),(S,P),(S,S)]
ORDER = 7
power3 = [0] + [3 ** i for i in xrange(ORDER)]
power9 = [0] + [9 ** i for i in xrange(ORDER)]
my_history = collections.defaultdict(lambda: None)
op_history = collections.defaultdict(lambda: None)
both_history = collections.defaultdict(lambda: None)
my_last_used_order = 0
op_last_used_order = 0
both_last_used_order = 0
# DECAY = 1.0 # no decay seems to be better
STRATEGIES = 18
# 0-2 is my my, 3-5 is my op, 6-8 is both op my, 9-11 is op op, 12-14 is both my, 15-17 is both op
next_hands = [None for _ in xrange(STRATEGIES)]
transitions = [[0] * STRATEGIES for _ in xrange(STRATEGIES)]
next_state = randint(0, STRATEGIES-1)
# bookkeeping
my_hands_encoded = []
op_hands_encoded = []
both_hands_encoded = []
hands_played = 0
# first hand
output = tostr[rchoice(RPS)]
# --------------------- turn -----------------------------
else:
# bookkeeping
last_hand_index = hands_played
hands_played += 1
my_last_hand = tonum[output]
my_last_hand_encoded = encode1hand[my_last_hand]
op_last_hand = tonum[input]
op_last_hand_encoded = encode1hand[op_last_hand]
both_last_hand_encoded = encode2hands[my_last_hand][op_last_hand]
my_hands_encoded.append(my_last_hand_encoded)
op_hands_encoded.append(op_last_hand_encoded)
both_hands_encoded.append(both_last_hand_encoded)
# update the transitions if played at least 2 hands
if hands_played > 1:
last_transition = transitions[last_state]
for i in xrange(STRATEGIES):
# last_transition[i] *= DECAY
last_transition[i] += topoints[scoreround[next_hands[i]][op_last_hand]]
if last_transition[i] < 0.0:
last_transition[i] = 0.0
# update the history, order 0 as a special case
my_update_index = 0
op_update_index = 0
both_update_index = 0
if my_last_used_order == 0:
my_history[0] = last_hand_index
if op_last_used_order == 0:
op_history[0] = last_hand_index
if both_last_used_order == 0:
both_history[0] = last_hand_index
# start the prediction with the zeroth order
my_predict = my_history[0]
op_predict = op_history[0]
both_predict = both_history[0]
# update the higher orders and predict the next hand
for order in xrange(1, ORDER+1 if hands_played > ORDER else hands_played):
my_predict_index = my_update_index * 3 + my_last_hand_encoded
op_predict_index = op_update_index * 3 + op_last_hand_encoded
both_predict_index = both_update_index * 9 + both_last_hand_encoded
my_update_index += my_hands_encoded[-order-1] * power3[order]
op_update_index += op_hands_encoded[-order-1] * power3[order]
both_update_index += both_hands_encoded[-order-1] * power9[order]
if order >= my_last_used_order:
my_history[my_update_index] = last_hand_index
if order >= op_last_used_order:
op_history[op_update_index] = last_hand_index
if order >= both_last_used_order:
both_history[both_update_index] = last_hand_index
my_try_get = my_history[my_predict_index]
op_try_get = op_history[op_predict_index]
both_try_get = both_history[both_predict_index]
if my_try_get is not None:
my_predict = my_try_get
my_last_used_order = order
if op_try_get is not None:
op_predict = op_try_get
op_last_used_order = order
if both_try_get is not None:
both_predict = both_try_get
both_last_used_order = order
my_base_prediction = decode2hands[both_hands_encoded[my_predict]]
op_base_prediction = decode2hands[both_hands_encoded[op_predict]]
both_base_prediction = decode2hands[both_hands_encoded[both_predict]]
# update each strategy's prediction
for f in PAYOFFS:
next_hands[f] = frompayoff[f][my_base_prediction[0]]
next_hands[f+3] = frompayoff[f][my_base_prediction[1]]
next_hands[f+6] = frompayoff[f][op_base_prediction[0]]
next_hands[f+9] = frompayoff[f][op_base_prediction[1]]
next_hands[f+12] = frompayoff[f][both_base_prediction[0]]
next_hands[f+15] = frompayoff[f][both_base_prediction[1]]
# pick the next state
last_payoff = scoreround[my_last_hand][op_last_hand]
last_state = next_state
current_transition = transitions[last_state]
next_state = randint(0, STRATEGIES-1)
total = sum(current_transition)
u = uniform(0.0, total)
acc = 0.0
for i in xrange(STRATEGIES):
acc += current_transition[i]
if u < acc:
next_state = i
break
# play the next hand
output = tostr[next_hands[next_state]]
```