Suicidal Nazi Monkey

This program has been disqualified.

Source code:

# after a wrong prediction, drops the strategy with 50% probability

import random

if not input:
	beat = {'R':'P','P':'S','S':'R'}
	fusion = {'RP':'a','PS':'b','SR':'c','PR':'d','SP':'e','RS':'f','RR':'g','PP':'h','SS':'i'}
	limits = [5,12,28,64]
	moves = ["","",""]
	numPredictors = 6*len(limits)*len(moves)
	numMetapredictors = 6
	predictors = [random.choice("RPS") for i in range(numPredictors)]
	predictorscore = [2*random.random() for i in range(numPredictors)]
	opponentscore = [2*random.random() for i in range(numPredictors)]
	metapredictors = [random.choice("RPS") for i in range(numMetapredictors)]
	metascore = [20,10,0,0,0,0]
	threat = [0,0,0]
	outcome = 0
	length = 0
	output = random.choice(['R','P','S'])
	wait = random.randint(18, 30)
	wins = 0
else:
	oldoutcome = outcome
	if (beat[input] == output):
		outcome = 1
	elif (input == beat[output]):
		outcome = -1
	else:
		outcome = 0
	if wins < 50:
		wins += outcome
	threat[oldoutcome + 1] *= 0.958
	threat[oldoutcome + 1] -= 0.042*outcome
	for i in range(numPredictors):
		predictorscore[i] *= 0.8
		predictorscore[i] += (beat[input] == predictors[i])
		predictorscore[i] -= (input == beat[predictors[i]])
		opponentscore[i] *= 0.8
		opponentscore[i] += (beat[output] == predictors[i])
		opponentscore[i] -= (output == beat[predictors[i]])
		if beat[input] == predictors[i] and random.random() < 0.5:
			predictorscore[i] = 0
		if beat[output] == predictors[i] and random.random() < 0.5:
			opponentscore[i] = 0
	for i in range(numMetapredictors):
		metascore[i] *= 0.958
		metascore[i] += (beat[input] == metapredictors[i])
		metascore[i] -= (input == beat[metapredictors[i]])
		if beat[input] == metapredictors[i] and random.random() < 0.5:
			metascore[i] = 0
	moves[0] += input
	moves[1] += output
	moves[2] += fusion[input+output]
	length += 1
	
	for z in range(3*len(limits)):
		j = min([length-1, limits[z//3]])
		while not moves[z%3][length-j:length] in moves[z%3][0:length-1]:
			j-=1
		i = moves[z%3].rfind(moves[z%3][length-j:length], 0, length-1)
		predictors[2 * z] = moves[0][i+j]
		predictors[2*z+1] = moves[1][i+j]
	for i in range(numPredictors/3, numPredictors):
		predictors[i] = beat[predictors[i - numPredictors/3]]
	metapredictors[0] = predictors[predictorscore.index(min(predictorscore))]
	metapredictors[1] = beat[predictors[opponentscore.index(min(opponentscore))]]
	for i in range(numMetapredictors/3, numMetapredictors):
		metapredictors[i] = beat[metapredictors[i - numMetapredictors/3]]
	
	if wins >= 50:
		output = "R"
	elif length < wait or random.random() < 0.1-0.4*threat[outcome+1]:
		output = random.choice("RPS")
	else:
		output = metapredictors[metascore.index(min(metascore))]