Author | DataWraith |
Submission date | 2012-08-14 10:17:19.172105 |
Rating | 6976 |
Matches played | 792 |
Win rate | 66.54 |
Use rpsrunner.py to play unranked matches on your computer.
# This entry is based on Prediction by Partial Matching, a method that is used
# in data compression.
import random
from collections import defaultdict
CONTEXT_SIZE=16
if input == "":
contexts = defaultdict(lambda: [0.1, 0.1, 0.1])
history = ''
else:
input_idx = ['R', 'P', 'S'].index(input)
# Update empty context
contexts[''][input_idx] += 1
# Update other contexts
for i in range(1, min(CONTEXT_SIZE, len(history)+1)):
contexts[history[-i:]][input_idx] += 1
history += input
history += output
# Find the best context to use for prediction. The best context has the highest
# "Most-probable Symbol"-Probability.
best_context = contexts['']
best_mpsp = max(contexts['']) / (1 + sum(contexts['']))
for i in range(1, min(CONTEXT_SIZE, len(history)+1)):
c = contexts[history[-i:]]
mpsp = max(c) / (1 + sum(c))
if mpsp > best_mpsp:
best_context = c
best_mpsp = mpsp
elif mpsp == best_mpsp:
# Merge equally good contexts
best_context[0] += c[0]
best_context[1] += c[1]
best_context[2] += c[2]
# Predict the opponent move from the context, then beat it.
output = ['P', 'S', 'R'][best_context.index(max(best_context))]