# iocane

 Author Sean Submission date 2017-04-05 14:57:02.707714 Rating 7300 Matches played 364 Win rate 71.98

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

## Source code:

``````if input == "":

import collections
import math
import random

gamma = random.gammavariate
sqrt = math.sqrt
log = math.log
moves = R, P, S = 0, 1, 2
index = {"R": R, "P": P, "S": S}
good_against = (P, S, R)
bad_against = (S, R, P)
name = ("R", "P", "S")

class MarkovPredictor:
def __init__(self):
self.seen = None
self.children = None
def update(self, h):
s = h[-1]
i = len(h) - 2
d = 0
while 0 <= i and d < 10:
self.seen = s
if self.children is None:
self.children = tuple(MarkovPredictor() for _ in range(3))
self = self.children[h[i]]
i -= 1
d += 1
def predict(self, h):
i = len(h) - 1
seen = R
while self.seen is not None:
seen = self.seen
self = self.children[h[i]]
i -= 1
return seen

class FrequencyPredictor:
def __init__(self):
self.frequencies = [0, 0, 0]
def update(self, h):
self.frequencies[h[-1]] += 1
def predict(self, h):
f = self.frequencies
scores = [f[bad_against[m]] - f[good_against[m]] for m in moves]
s = max(scores)
return random.choice([m for m in moves if scores[m] == s])

class MetaPredictor:
def __init__(self, predictor):
self.predictor = predictor
self.prediction = None
self.counts = [[0 for _ in range(3)] for _ in range(3)]
def update(self, h):
s = h[-1]
if self.prediction is not None:
for i, counts in enumerate(self.counts):
m = (self.prediction + i) % 3
if m == good_against[s]:
counts[2] += 1
elif m == bad_against[s]:
counts[0] += 1
else:
counts[1] += 1
self.predictor.update(h)
def predictions(self, h):
self.prediction = self.predictor.predict(h)
for i, counts in enumerate(self.counts):
belief = [random.gammavariate(n + 0.5, 1) for n in counts]
score = (belief[2] - belief[0]) / sum(belief)
yield ((self.prediction + i) % 3, score)

class RandomPredictor:
def __init__(self):
pass
def update(self, h):
pass
def predictions(self, h):
return [(random.choice(moves), 0)]

class PredictionBlender:
def __init__(self, predictors):
self.predictors = predictors
def update(self, h):
for predictor in self.predictors:
predictor.update(h)
def predict(self, h):
predictions = [p for predictor in self.predictors for p in predictor.predictions(h)]
best_score = float('-inf')
for m, score in predictions:
if score > best_score:
move = m
best_score = score
return move

p0 = MetaPredictor(MarkovPredictor())
p1 = MetaPredictor(FrequencyPredictor())
p2 = RandomPredictor()
model = PredictionBlender([p0, p1, p2])
history = []
output = random.choice(name)
else:
us = index[output]
them = index[input]
history.append(them)
model.update(history)
history.append(us)
output = name[model.predict(history)]``````