network1

This program has been disqualified.

Source code:

#!/usr/bin/python

import random, math, time

class Network:
	def __init__(self, inp, hid, out):
		self.mat = []
		self.total = inp + hid + out
		self.bias = [0.] * self.total
		self.bcells = [0.] * self.total
		self.cells = [0.] * self.total
		self.inp = inp
		self.hid = hid
		self.out = out

		for i in range(self.total):
			self.bias[i] = random.gauss(0, 0.5)

		variance = 1.0 / math.sqrt(self.total)
		for y in range(self.total):
			row = []
			for x in range(self.total):
				row.append(random.gauss(0, variance))
			self.mat.append(row)


	def learn(self, nout, speed, fade):
		disp = self.inp + self.hid
		for i in range(self.out):
			di = disp + i
			diff = nout[i] - self.cells[di]
			der = speed / (1.0 + self.bcells[di] * self.bcells[di])
			self.bias[di] += der
			self.bias[di] -= fade * self.bias[di]
			for j in range(self.inp + self.hid):
				self.mat[di][j] += der * self.cells[j]
				self.mat[di][j] -= fade * self.mat[di][j]


	def calc(self, ninput):
		for i in range(self.inp):
			self.cells[i] = ninput[i]

		self.bcells = [0.] * self.total
		for i in range(self.inp, self.inp + self.hid):
			self.bcells[i] = self.bias[i]
			for j in range(self.inp + self.hid):
				self.bcells[i] += self.cells[j] * self.mat[i][j]
		for i in range(self.inp, self.inp + self.hid):
			self.cells[i] = math.atan(self.bcells[i])

		for i in range(self.inp + self.hid, self.total):
			self.bcells[i] = self.bias[i]
			for j in range(self.inp + self.hid):
				self.bcells[i] += self.cells[j] * self.mat[i][j]

		return self.bcells[-self.out:]


def prog():
	speed = 0.1
	fade = 0.05
	
	global input, output
	global ioutput
	global netw, netwinput
	letter2index = {"R": 0, "P": 1, "S": 2}
	index2letter = ["R", "P", "S", "R"]

	hidden = 30
	if input == "" or type(input) != type(""):
		netw = Network(6, hidden, 3)
		netwinput = [0., 0., 0., 0., 0., 0.]
		#fade = (time.time() - 1458741477.03602) * 0.001
		print fade
		#input = "R"
		#ioutput = 0
		#prog()
	else:
		netwinput[0] = netwinput[1] = netwinput[2] = 0.
		netwinput[ioutput] = 1.0
		netwinput[3] = netwinput[4] = netwinput[5] = 0.
		netwinput[3 + letter2index[input]] = 1.0

		netw.learn(netwinput[3:6], speed, fade)

	netwoutput = netw.calc(netwinput)
	maxoutput = -100
	for o in range(len(netwoutput)):
		if netwoutput[o] > maxoutput:
			maxoutput = netwoutput[o]
			maxindex = o

	ioutput = maxindex + 1
	output = index2letter[ioutput]


prog()