View difference between Paste ID: JSE4iQ3Y and ZZmSNaHX

 -- MarI/O by SethBling
 -- Feel free to use this code, but please do not redistribute it.
 -- Intended for use with the BizHawk emulator and Super Mario World or Super Mario Bros. ROM.
 -- For SMW, make sure you have a save state named "DP1.state" at the beginning of a level,
 -- and put a copy in both the Lua folder and the root directory of BizHawk.
 
 if gameinfo.getromname() == "Super Mario World (USA)" then
 	Filename = "DP1.state"
 	ButtonNames = {
 		"A",
 		"B",
 		"X",
 		"Y",
 		"Up",
 		"Down",
 		"Left",
 		"Right",
 	}
 elseif gameinfo.getromname() == "Super Mario Bros." then
 	Filename = "SMB1-1.state"
 	ButtonNames = {
 		"A",
 		"B",
 		"Up",
 		"Down",
 		"Left",
 		"Right",
 	}
 end
 
 BoxRadius = 6
 InputSize = (BoxRadius*2+1)*(BoxRadius*2+1)
 
 Inputs = InputSize+1
 Outputs = #ButtonNames
 
 Population = 300
 DeltaDisjoint = 2.0
 DeltaWeights = 0.4
 DeltaThreshold = 1.0
 
 StaleSpecies = 15
 
 MutateConnectionsChance = 0.25
 PerturbChance = 0.90
 CrossoverChance = 0.75
 LinkMutationChance = 2.0
 NodeMutationChance = 0.50
 BiasMutationChance = 0.40
 StepSize = 0.1
 DisableMutationChance = 0.4
 EnableMutationChance = 0.2
 
 TimeoutConstant = 20
 
 MaxNodes = 1000000
 
 function getPositions()
 	if gameinfo.getromname() == "Super Mario World (USA)" then
 		marioX = memory.read_s16_le(0x94)
 		marioY = memory.read_s16_le(0x96)
 
 		local layer1x = memory.read_s16_le(0x1A);
 		local layer1y = memory.read_s16_le(0x1C);
 
 		screenX = marioX-layer1x
 		screenY = marioY-layer1y
 	elseif gameinfo.getromname() == "Super Mario Bros." then
 		marioX = memory.readbyte(0x6D) * 0x100 + memory.readbyte(0x86)
 		marioY = memory.readbyte(0x03B8)+16
 
 		screenX = memory.readbyte(0x03AD)
 		screenY = memory.readbyte(0x03B8)
 	end
 end
 
 function getTile(dx, dy)
 	if gameinfo.getromname() == "Super Mario World (USA)" then
 		x = math.floor((marioX+dx+8)/16)
 		y = math.floor((marioY+dy)/16)
 
 		return memory.readbyte(0x1C800 + math.floor(x/0x10)*0x1B0 + y*0x10 + x%0x10)
 	elseif gameinfo.getromname() == "Super Mario Bros." then
 		local x = marioX + dx + 8
 		local y = marioY + dy - 16
 		local page = math.floor(x/256)%2
 
 		local subx = math.floor((x%256)/16)
 		local suby = math.floor((y - 32)/16)
 		local addr = 0x500 + page*13*16+suby*16+subx
 
 		if suby >= 13 or suby < 0 then
 			return 0
 		end
 
 		if memory.readbyte(addr) ~= 0 then
 			return 1
 		else
 			return 0
 		end
 	end
 end
 
 function getSprites()
 	if gameinfo.getromname() == "Super Mario World (USA)" then
 		local sprites = {}
 		for slot=0,11 do
 			local status = memory.readbyte(0x14C8+slot)
 			if status ~= 0 then
 				spritex = memory.readbyte(0xE4+slot) + memory.readbyte(0x14E0+slot)*256
 				spritey = memory.readbyte(0xD8+slot) + memory.readbyte(0x14D4+slot)*256
 				sprites[#sprites+1] = {["x"]=spritex, ["y"]=spritey}
 			end
 		end
 
 		return sprites
 	elseif gameinfo.getromname() == "Super Mario Bros." then
 		local sprites = {}
 		for slot=0,4 do
 			local enemy = memory.readbyte(0xF+slot)
 			if enemy ~= 0 then
 				local ex = memory.readbyte(0x6E + slot)*0x100 + memory.readbyte(0x87+slot)
 				local ey = memory.readbyte(0xCF + slot)+24
 				sprites[#sprites+1] = {["x"]=ex,["y"]=ey}
 			end
 		end
 
 		return sprites
 	end
 end
 
 function getExtendedSprites()
 	if gameinfo.getromname() == "Super Mario World (USA)" then
 		local extended = {}
 		for slot=0,11 do
 			local number = memory.readbyte(0x170B+slot)
 			if number ~= 0 then
 				spritex = memory.readbyte(0x171F+slot) + memory.readbyte(0x1733+slot)*256
 				spritey = memory.readbyte(0x1715+slot) + memory.readbyte(0x1729+slot)*256
 				extended[#extended+1] = {["x"]=spritex, ["y"]=spritey}
 			end
 		end
 
 		return extended
 	elseif gameinfo.getromname() == "Super Mario Bros." then
 		return {}
 	end
 end
 
 function getInputs()
 	getPositions()
 
 	sprites = getSprites()
 	extended = getExtendedSprites()
 
 	local inputs = {}
 
 	for dy=-BoxRadius*16,BoxRadius*16,16 do
 		for dx=-BoxRadius*16,BoxRadius*16,16 do
 			inputs[#inputs+1] = 0
 
 			tile = getTile(dx, dy)
 			if tile == 1 and marioY+dy < 0x1B0 then
 				inputs[#inputs] = 1
 			end
 
 			for i = 1,#sprites do
 				distx = math.abs(sprites[i]["x"] - (marioX+dx))
 				disty = math.abs(sprites[i]["y"] - (marioY+dy))
 				if distx <= 8 and disty <= 8 then
 					inputs[#inputs] = -1
 				end
 			end
 
 			for i = 1,#extended do
 				distx = math.abs(extended[i]["x"] - (marioX+dx))
 				disty = math.abs(extended[i]["y"] - (marioY+dy))
 				if distx < 8 and disty < 8 then
 					inputs[#inputs] = -1
 				end
 			end
 		end
 	end
 
 	--mariovx = memory.read_s8(0x7B)
 	--mariovy = memory.read_s8(0x7D)
 
 	return inputs
 end
 
 function sigmoid(x)
 	return 2/(1+math.exp(-4.9*x))-1
 end
 
 function newInnovation()
 	pool.innovation = pool.innovation + 1
 	return pool.innovation
 end
 
 function newPool()
 	local pool = {}
 	pool.species = {}
 	pool.generation = 0
 	pool.innovation = Outputs
 	pool.currentSpecies = 1
 	pool.currentGenome = 1
 	pool.currentFrame = 0
 	pool.maxFitness = 0
 
 	return pool
 end
 
 function newSpecies()
 	local species = {}
 	species.topFitness = 0
 	species.staleness = 0
 	species.genomes = {}
 	species.averageFitness = 0
 
 	return species
 end
 
 function newGenome()
 	local genome = {}
 	genome.genes = {}
 	genome.fitness = 0
 	genome.adjustedFitness = 0
 	genome.network = {}
 	genome.maxneuron = 0
 	genome.globalRank = 0
 	genome.mutationRates = {}
 	genome.mutationRates["connections"] = MutateConnectionsChance
 	genome.mutationRates["link"] = LinkMutationChance
 	genome.mutationRates["bias"] = BiasMutationChance
 	genome.mutationRates["node"] = NodeMutationChance
 	genome.mutationRates["enable"] = EnableMutationChance
 	genome.mutationRates["disable"] = DisableMutationChance
 	genome.mutationRates["step"] = StepSize
 
 	return genome
 end
 
 function copyGenome(genome)
 	local genome2 = newGenome()
 	for g=1,#genome.genes do
 		table.insert(genome2.genes, copyGene(genome.genes[g]))
 	end
 	genome2.maxneuron = genome.maxneuron
 	genome2.mutationRates["connections"] = genome.mutationRates["connections"]
 	genome2.mutationRates["link"] = genome.mutationRates["link"]
 	genome2.mutationRates["bias"] = genome.mutationRates["bias"]
 	genome2.mutationRates["node"] = genome.mutationRates["node"]
 	genome2.mutationRates["enable"] = genome.mutationRates["enable"]
 	genome2.mutationRates["disable"] = genome.mutationRates["disable"]
 
 	return genome2
 end
 
 function basicGenome()
 	local genome = newGenome()
 	local innovation = 1
 
 	genome.maxneuron = Inputs
 	mutate(genome)
 
 	return genome
 end
 
 function newGene()
 	local gene = {}
 	gene.into = 0
 	gene.out = 0
 	gene.weight = 0.0
 	gene.enabled = true
 	gene.innovation = 0
 
 	return gene
 end
 
 function copyGene(gene)
 	local gene2 = newGene()
 	gene2.into = gene.into
 	gene2.out = gene.out
 	gene2.weight = gene.weight
 	gene2.enabled = gene.enabled
 	gene2.innovation = gene.innovation
 
 	return gene2
 end
 
 function newNeuron()
 	local neuron = {}
 	neuron.incoming = {}
 	neuron.value = 0.0
 
 	return neuron
 end
 
 function generateNetwork(genome)
 	local network = {}
 	network.neurons = {}
 
 	for i=1,Inputs do
 		network.neurons[i] = newNeuron()
 	end
 
 	for o=1,Outputs do
 		network.neurons[MaxNodes+o] = newNeuron()
 	end
 
 	table.sort(genome.genes, function (a,b)
 		return (a.out < b.out)
 	end)
 	for i=1,#genome.genes do
 		local gene = genome.genes[i]
 		if gene.enabled then
 			if network.neurons[gene.out] == nil then
 				network.neurons[gene.out] = newNeuron()
 			end
 			local neuron = network.neurons[gene.out]
 			table.insert(neuron.incoming, gene)
 			if network.neurons[gene.into] == nil then
 				network.neurons[gene.into] = newNeuron()
 			end
 		end
 	end
 
 	genome.network = network
 end
 
 function evaluateNetwork(network, inputs)
 	table.insert(inputs, 1)
 	if #inputs ~= Inputs then
 		console.writeline("Incorrect number of neural network inputs.")
 		return {}
 	end
 
 	for i=1,Inputs do
 		network.neurons[i].value = inputs[i]
 	end
 
 	for _,neuron in pairs(network.neurons) do
 		local sum = 0
 		for j = 1,#neuron.incoming do
 			local incoming = neuron.incoming[j]
 			local other = network.neurons[incoming.into]
 			sum = sum + incoming.weight * other.value
 		end
 
 		if #neuron.incoming > 0 then
 			neuron.value = sigmoid(sum)
 		end
 	end
 
 	local outputs = {}
 	for o=1,Outputs do
 		local button = "P1 " .. ButtonNames[o]
 		if network.neurons[MaxNodes+o].value > 0 then
 			outputs[button] = true
 		else
 			outputs[button] = false
 		end
 	end
 
 	return outputs
 end
 
 function crossover(g1, g2)
 	-- Make sure g1 is the higher fitness genome
 	if g2.fitness > g1.fitness then
 		tempg = g1
 		g1 = g2
 		g2 = tempg
 	end
 
 	local child = newGenome()
 
 	local innovations2 = {}
 	for i=1,#g2.genes do
 		local gene = g2.genes[i]
 		innovations2[gene.innovation] = gene
 	end
 
 	for i=1,#g1.genes do
 		local gene1 = g1.genes[i]
 		local gene2 = innovations2[gene1.innovation]
 		if gene2 ~= nil and math.random(2) == 1 and gene2.enabled then
 			table.insert(child.genes, copyGene(gene2))
 		else
 			table.insert(child.genes, copyGene(gene1))
 		end
 	end
 
 	child.maxneuron = math.max(g1.maxneuron,g2.maxneuron)
 
 	for mutation,rate in pairs(g1.mutationRates) do
 		child.mutationRates[mutation] = rate
 	end
 
 	return child
 end
 
 function randomNeuron(genes, nonInput)
 	local neurons = {}
 	if not nonInput then
 		for i=1,Inputs do
 			neurons[i] = true
 		end
 	end
 	for o=1,Outputs do
 		neurons[MaxNodes+o] = true
 	end
 	for i=1,#genes do
 		if (not nonInput) or genes[i].into > Inputs then
 			neurons[genes[i].into] = true
 		end
 		if (not nonInput) or genes[i].out > Inputs then
 			neurons[genes[i].out] = true
 		end
 	end
 
 	local count = 0
 	for _,_ in pairs(neurons) do
 		count = count + 1
 	end
 	local n = math.random(1, count)
 
 	for k,v in pairs(neurons) do
 		n = n-1
 		if n == 0 then
 			return k
 		end
 	end
 
 	return 0
 end
 
 function containsLink(genes, link)
 	for i=1,#genes do
 		local gene = genes[i]
 		if gene.into == link.into and gene.out == link.out then
 			return true
 		end
 	end
 end
 
 function pointMutate(genome)
 	local step = genome.mutationRates["step"]
 
 	for i=1,#genome.genes do
 		local gene = genome.genes[i]
 		if math.random() < PerturbChance then
 			gene.weight = gene.weight + math.random() * step*2 - step
 		else
 			gene.weight = math.random()*4-2
 		end
 	end
 end
 
 function linkMutate(genome, forceBias)
 	local neuron1 = randomNeuron(genome.genes, false)
 	local neuron2 = randomNeuron(genome.genes, true)
 
 	local newLink = newGene()
 	if neuron1 <= Inputs and neuron2 <= Inputs then
 		--Both input nodes
 		return
 	end
 	if neuron2 <= Inputs then
 		-- Swap output and input
 		local temp = neuron1
 		neuron1 = neuron2
 		neuron2 = temp
 	end
 
 	newLink.into = neuron1
 	newLink.out = neuron2
 	if forceBias then
 		newLink.into = Inputs
 	end
 
 	if containsLink(genome.genes, newLink) then
 		return
 	end
 	newLink.innovation = newInnovation()
 	newLink.weight = math.random()*4-2
 
 	table.insert(genome.genes, newLink)
 end
 
 function nodeMutate(genome)
 	if #genome.genes == 0 then
 		return
 	end
 
 	genome.maxneuron = genome.maxneuron + 1
 
 	local gene = genome.genes[math.random(1,#genome.genes)]
 	if not gene.enabled then
 		return
 	end
 	gene.enabled = false
 
 	local gene1 = copyGene(gene)
 	gene1.out = genome.maxneuron
 	gene1.weight = 1.0
 	gene1.innovation = newInnovation()
 	gene1.enabled = true
 	table.insert(genome.genes, gene1)
 
 	local gene2 = copyGene(gene)
 	gene2.into = genome.maxneuron
 	gene2.innovation = newInnovation()
 	gene2.enabled = true
 	table.insert(genome.genes, gene2)
 end
 
 function enableDisableMutate(genome, enable)
 	local candidates = {}
 	for _,gene in pairs(genome.genes) do
 		if gene.enabled == not enable then
 			table.insert(candidates, gene)
 		end
 	end
 
 	if #candidates == 0 then
 		return
 	end
 
 	local gene = candidates[math.random(1,#candidates)]
 	gene.enabled = not gene.enabled
 end
 
 function mutate(genome)
 	for mutation,rate in pairs(genome.mutationRates) do
 		if math.random(1,2) == 1 then
 			genome.mutationRates[mutation] = 0.95*rate
 		else
 			genome.mutationRates[mutation] = 1.05263*rate
 		end
 	end
 
 	if math.random() < genome.mutationRates["connections"] then
 		pointMutate(genome)
 	end
 
 	local p = genome.mutationRates["link"]
 	while p > 0 do
 		if math.random() < p then
 			linkMutate(genome, false)
 		end
 		p = p - 1
 	end
 
 	p = genome.mutationRates["bias"]
 	while p > 0 do
 		if math.random() < p then
 			linkMutate(genome, true)
 		end
 		p = p - 1
 	end
 
 	p = genome.mutationRates["node"]
 	while p > 0 do
 		if math.random() < p then
 			nodeMutate(genome)
 		end
 		p = p - 1
 	end
 
 	p = genome.mutationRates["enable"]
 	while p > 0 do
 		if math.random() < p then
 			enableDisableMutate(genome, true)
 		end
 		p = p - 1
 	end
 
 	p = genome.mutationRates["disable"]
 	while p > 0 do
 		if math.random() < p then
 			enableDisableMutate(genome, false)
 		end
 		p = p - 1
 	end
 end
 
 function disjoint(genes1, genes2)
 	local i1 = {}
 	for i = 1,#genes1 do
 		local gene = genes1[i]
 		i1[gene.innovation] = true
 	end
 
 	local i2 = {}
 	for i = 1,#genes2 do
 		local gene = genes2[i]
 		i2[gene.innovation] = true
 	end
 
 	local disjointGenes = 0
 	for i = 1,#genes1 do
 		local gene = genes1[i]
 		if not i2[gene.innovation] then
 			disjointGenes = disjointGenes+1
 		end
 	end
 
 	for i = 1,#genes2 do
 		local gene = genes2[i]
 		if not i1[gene.innovation] then
 			disjointGenes = disjointGenes+1
 		end
 	end
 
 	local n = math.max(#genes1, #genes2)
 
 	return disjointGenes / n
 end
 
 function weights(genes1, genes2)
 	local i2 = {}
 	for i = 1,#genes2 do
 		local gene = genes2[i]
 		i2[gene.innovation] = gene
 	end
 
 	local sum = 0
 	local coincident = 0
 	for i = 1,#genes1 do
 		local gene = genes1[i]
 		if i2[gene.innovation] ~= nil then
 			local gene2 = i2[gene.innovation]
 			sum = sum + math.abs(gene.weight - gene2.weight)
 			coincident = coincident + 1
 		end
 	end
 
 	return sum / coincident
 end
 
 function sameSpecies(genome1, genome2)
 	local dd = DeltaDisjoint*disjoint(genome1.genes, genome2.genes)
 	local dw = DeltaWeights*weights(genome1.genes, genome2.genes)
 	return dd + dw < DeltaThreshold
 end
 
 function rankGlobally()
 	local global = {}
 	for s = 1,#pool.species do
 		local species = pool.species[s]
 		for g = 1,#species.genomes do
 			table.insert(global, species.genomes[g])
 		end
 	end
 	table.sort(global, function (a,b)
 		return (a.fitness < b.fitness)
 	end)
 
 	for g=1,#global do
 		global[g].globalRank = g
 	end
 end
 
 function calculateAverageFitness(species)
 	local total = 0
 
 	for g=1,#species.genomes do
 		local genome = species.genomes[g]
 		total = total + genome.globalRank
 	end
 
 	species.averageFitness = total / #species.genomes
 end
 
 function totalAverageFitness()
 	local total = 0
 	for s = 1,#pool.species do
 		local species = pool.species[s]
 		total = total + species.averageFitness
 	end
 
 	return total
 end
 
 function cullSpecies(cutToOne)
 	for s = 1,#pool.species do
 		local species = pool.species[s]
 
 		table.sort(species.genomes, function (a,b)
 			return (a.fitness > b.fitness)
 		end)
 
 		local remaining = math.ceil(#species.genomes/2)
 		if cutToOne then
 			remaining = 1
 		end
 		while #species.genomes > remaining do
 			table.remove(species.genomes)
 		end
 	end
 end
 
 function breedChild(species)
 	local child = {}
 	if math.random() < CrossoverChance then
 		g1 = species.genomes[math.random(1, #species.genomes)]
 		g2 = species.genomes[math.random(1, #species.genomes)]
 		child = crossover(g1, g2)
 	else
 		g = species.genomes[math.random(1, #species.genomes)]
 		child = copyGenome(g)
 	end
 
 	mutate(child)
 
 	return child
 end
 
 function removeStaleSpecies()
 	local survived = {}
 
 	for s = 1,#pool.species do
 		local species = pool.species[s]
 
 		table.sort(species.genomes, function (a,b)
 			return (a.fitness > b.fitness)
 		end)
 
 		if species.genomes[1].fitness > species.topFitness then
 			species.topFitness = species.genomes[1].fitness
 			species.staleness = 0
 		else
 			species.staleness = species.staleness + 1
 		end
 		if species.staleness < StaleSpecies or species.topFitness >= pool.maxFitness then
 			table.insert(survived, species)
 		end
 	end
 
 	pool.species = survived
 end
 
 function removeWeakSpecies()
 	local survived = {}
 
 	local sum = totalAverageFitness()
 	for s = 1,#pool.species do
 		local species = pool.species[s]
 		breed = math.floor(species.averageFitness / sum * Population)
 		if breed >= 1 then
 			table.insert(survived, species)
 		end
 	end
 
 	pool.species = survived
 end
 
 
 function addToSpecies(child)
 	local foundSpecies = false
 	for s=1,#pool.species do
 		local species = pool.species[s]
 		if not foundSpecies and sameSpecies(child, species.genomes[1]) then
 			table.insert(species.genomes, child)
 			foundSpecies = true
 		end
 	end
 
 	if not foundSpecies then
 		local childSpecies = newSpecies()
 		table.insert(childSpecies.genomes, child)
 		table.insert(pool.species, childSpecies)
 	end
 end
 
 function newGeneration()
 	cullSpecies(false) -- Cull the bottom half of each species
 	rankGlobally()
 	removeStaleSpecies()
 	rankGlobally()
 	for s = 1,#pool.species do
 		local species = pool.species[s]
 		calculateAverageFitness(species)
 	end
 	removeWeakSpecies()
 	local sum = totalAverageFitness()
 	local children = {}
 	for s = 1,#pool.species do
 		local species = pool.species[s]
 		breed = math.floor(species.averageFitness / sum * Population) - 1
 		for i=1,breed do
 			table.insert(children, breedChild(species))
 		end
 	end
 	cullSpecies(true) -- Cull all but the top member of each species
 	while #children + #pool.species < Population do
 		local species = pool.species[math.random(1, #pool.species)]
 		table.insert(children, breedChild(species))
 	end
 	for c=1,#children do
 		local child = children[c]
 		addToSpecies(child)
 	end
 
 	pool.generation = pool.generation + 1
 
 	writeFile("backup." .. pool.generation .. "." .. forms.gettext(saveLoadFile))
 end
 
 function initializePool()
 	pool = newPool()
 
 	for i=1,Population do
 		basic = basicGenome()
 		addToSpecies(basic)
 	end
 
 	initializeRun()
 end
 
 function clearJoypad()
 	controller = {}
 	for b = 1,#ButtonNames do
 		controller["P1 " .. ButtonNames[b]] = false
 	end
 	joypad.set(controller)
 end
 
 function initializeRun()
 	savestate.load(Filename);
 	rightmost = 0
 	pool.currentFrame = 0
 	timeout = TimeoutConstant
 	clearJoypad()
 
 	local species = pool.species[pool.currentSpecies]
 	local genome = species.genomes[pool.currentGenome]
 	generateNetwork(genome)
 	evaluateCurrent()
 end
 
 function evaluateCurrent()
 	local species = pool.species[pool.currentSpecies]
 	local genome = species.genomes[pool.currentGenome]
 
 	inputs = getInputs()
 	controller = evaluateNetwork(genome.network, inputs)
 
 	if controller["P1 Left"] and controller["P1 Right"] then
 		controller["P1 Left"] = false
 		controller["P1 Right"] = false
 	end
 	if controller["P1 Up"] and controller["P1 Down"] then
 		controller["P1 Up"] = false
 		controller["P1 Down"] = false
 	end
 
 	joypad.set(controller)
 end
 
 if pool == nil then
 	initializePool()
 end
 
 
 function nextGenome()
 	pool.currentGenome = pool.currentGenome + 1
 	if pool.currentGenome > #pool.species[pool.currentSpecies].genomes then
 		pool.currentGenome = 1
 		pool.currentSpecies = pool.currentSpecies+1
 		if pool.currentSpecies > #pool.species then
 			newGeneration()
 			pool.currentSpecies = 1
 		end
 	end
 end
 
 function fitnessAlreadyMeasured()
 	local species = pool.species[pool.currentSpecies]
 	local genome = species.genomes[pool.currentGenome]
 
 	return genome.fitness ~= 0
 end
 
 function displayGenome(genome)
 	local network = genome.network
 	local cells = {}
 	local i = 1
 	local cell = {}
 	for dy=-BoxRadius,BoxRadius do
 		for dx=-BoxRadius,BoxRadius do
 			cell = {}
 			cell.x = 50+5*dx
 			cell.y = 70+5*dy
 			cell.value = network.neurons[i].value
 			cells[i] = cell
 			i = i + 1
 		end
 	end
 	local biasCell = {}
 	biasCell.x = 80
 	biasCell.y = 110
 	biasCell.value = network.neurons[Inputs].value
 	cells[Inputs] = biasCell
 
 	for o = 1,Outputs do
 		cell = {}
 		cell.x = 220
 		cell.y = 30 + 8 * o
 		cell.value = network.neurons[MaxNodes + o].value
 		cells[MaxNodes+o] = cell
 		local color
 		if cell.value > 0 then
 			color = 0xFF0000FF
 		else
 			color = 0xFF000000
 		end
 		gui.drawText(223, 24+8*o, ButtonNames[o], color, 9)
 	end
 
 	for n,neuron in pairs(network.neurons) do
 		cell = {}
 		if n > Inputs and n <= MaxNodes then
 			cell.x = 140
 			cell.y = 40
 			cell.value = neuron.value
 			cells[n] = cell
 		end
 	end
 
 	for n=1,4 do
 		for _,gene in pairs(genome.genes) do
 			if gene.enabled then
 				local c1 = cells[gene.into]
 				local c2 = cells[gene.out]
 				if gene.into > Inputs and gene.into <= MaxNodes then
 					c1.x = 0.75*c1.x + 0.25*c2.x
 					if c1.x >= c2.x then
 						c1.x = c1.x - 40
 					end
 					if c1.x < 90 then
 						c1.x = 90
 					end
 
 					if c1.x > 220 then
 						c1.x = 220
 					end
 					c1.y = 0.75*c1.y + 0.25*c2.y
 
 				end
 				if gene.out > Inputs and gene.out <= MaxNodes then
 					c2.x = 0.25*c1.x + 0.75*c2.x
 					if c1.x >= c2.x then
 						c2.x = c2.x + 40
 					end
 					if c2.x < 90 then
 						c2.x = 90
 					end
 					if c2.x > 220 then
 						c2.x = 220
 					end
 					c2.y = 0.25*c1.y + 0.75*c2.y
 				end
 			end
 		end
 	end
 
 	gui.drawBox(50-BoxRadius*5-3,70-BoxRadius*5-3,50+BoxRadius*5+2,70+BoxRadius*5+2,0xFF000000, 0x80808080)
 	for n,cell in pairs(cells) do
 		if n > Inputs or cell.value ~= 0 then
 			local color = math.floor((cell.value+1)/2*256)
 			if color > 255 then color = 255 end
 			if color < 0 then color = 0 end
 			local opacity = 0xFF000000
 			if cell.value == 0 then
 				opacity = 0x50000000
 			end
 			color = opacity + color*0x10000 + color*0x100 + color
 			gui.drawBox(cell.x-2,cell.y-2,cell.x+2,cell.y+2,opacity,color)
 		end
 	end
 	for _,gene in pairs(genome.genes) do
 		if gene.enabled then
 			local c1 = cells[gene.into]
 			local c2 = cells[gene.out]
 			local opacity = 0xA0000000
 			if c1.value == 0 then
 				opacity = 0x20000000
 			end
 
 			local color = 0x80-math.floor(math.abs(sigmoid(gene.weight))*0x80)
 			if gene.weight > 0 then
 				color = opacity + 0x8000 + 0x10000*color
 			else
 				color = opacity + 0x800000 + 0x100*color
 			end
 			gui.drawLine(c1.x+1, c1.y, c2.x-3, c2.y, color)
 		end
 	end
 
 	gui.drawBox(49,71,51,78,0x00000000,0x80FF0000)
 
 	if forms.ischecked(showMutationRates) then
 		local pos = 100
 		for mutation,rate in pairs(genome.mutationRates) do
 			gui.drawText(100, pos, mutation .. ": " .. rate, 0xFF000000, 10)
 			pos = pos + 8
 		end
 	end
 end
 
 function writeFile(filename)
         local file = io.open(filename, "w")
 	file:write(pool.generation .. "\n")
 	file:write(pool.maxFitness .. "\n")
 	file:write(#pool.species .. "\n")
         for n,species in pairs(pool.species) do
 		file:write(species.topFitness .. "\n")
 		file:write(species.staleness .. "\n")
 		file:write(#species.genomes .. "\n")
 		for m,genome in pairs(species.genomes) do
 			file:write(genome.fitness .. "\n")
 			file:write(genome.maxneuron .. "\n")
 			for mutation,rate in pairs(genome.mutationRates) do
 				file:write(mutation .. "\n")
 				file:write(rate .. "\n")
 			end
 			file:write("done\n")
 
 			file:write(#genome.genes .. "\n")
 			for l,gene in pairs(genome.genes) do
 				file:write(gene.into .. " ")
 				file:write(gene.out .. " ")
 				file:write(gene.weight .. " ")
 				file:write(gene.innovation .. " ")
 				if(gene.enabled) then
 					file:write("1\n")
 				else
 					file:write("0\n")
 				end
 			end
 		end
         end
         file:close()
 end
 
 function savePool()
 	local filename = forms.gettext(saveLoadFile)
 	writeFile(filename)
 end
 
 function loadFile(filename)
         local file = io.open(filename, "r")
 	pool = newPool()
 	pool.generation = file:read("*number")
 	pool.maxFitness = file:read("*number")
 	forms.settext(maxFitnessLabel, "Max Fitness: " .. math.floor(pool.maxFitness))
         local numSpecies = file:read("*number")
         for s=1,numSpecies do
 		local species = newSpecies()
 		table.insert(pool.species, species)
 		species.topFitness = file:read("*number")
 		species.staleness = file:read("*number")
 		local numGenomes = file:read("*number")
 		for g=1,numGenomes do
 			local genome = newGenome()
 			table.insert(species.genomes, genome)
 			genome.fitness = file:read("*number")
 			genome.maxneuron = file:read("*number")
 			local line = file:read("*line")
 			while line ~= "done" do
 				genome.mutationRates[line] = file:read("*number")
 				line = file:read("*line")
 			end
 			local numGenes = file:read("*number")
 			for n=1,numGenes do
 				local gene = newGene()
 				table.insert(genome.genes, gene)
 				local enabled
 				gene.into, gene.out, gene.weight, gene.innovation, enabled = file:read("*number", "*number", "*number", "*number", "*number")
 				if enabled == 0 then
 					gene.enabled = false
 				else
 					gene.enabled = true
 				end
 
 			end
 		end
 	end
         file:close()
 
 	while fitnessAlreadyMeasured() do
 		nextGenome()
 	end
 	initializeRun()
 	pool.currentFrame = pool.currentFrame + 1
 end
 
 function loadPool()
 	local filename = forms.gettext(saveLoadFile)
 	loadFile(filename)
 end
 
 function playTop()
 	local maxfitness = 0
 	local maxs, maxg
 	for s,species in pairs(pool.species) do
 		for g,genome in pairs(species.genomes) do
 			if genome.fitness > maxfitness then
 				maxfitness = genome.fitness
 				maxs = s
 				maxg = g
 			end
 		end
 	end
 
 	pool.currentSpecies = maxs
 	pool.currentGenome = maxg
 	pool.maxFitness = maxfitness
 	forms.settext(maxFitnessLabel, "Max Fitness: " .. math.floor(pool.maxFitness))
 	initializeRun()
 	pool.currentFrame = pool.currentFrame + 1
 	return
 end
 
 function onExit()
 	forms.destroy(form)
 end
 
 writeFile("temp.pool")
 
 event.onexit(onExit)
 
 form = forms.newform(200, 260, "Fitness")
 maxFitnessLabel = forms.label(form, "Max Fitness: " .. math.floor(pool.maxFitness), 5, 8)
 showNetwork = forms.checkbox(form, "Show Map", 5, 30)
 showMutationRates = forms.checkbox(form, "Show M-Rates", 5, 52)
 restartButton = forms.button(form, "Restart", initializePool, 5, 77)
 saveButton = forms.button(form, "Save", savePool, 5, 102)
 loadButton = forms.button(form, "Load", loadPool, 80, 102)
 saveLoadFile = forms.textbox(form, Filename .. ".pool", 170, 25, nil, 5, 148)
 saveLoadLabel = forms.label(form, "Save/Load:", 5, 129)
 playTopButton = forms.button(form, "Play Top", playTop, 5, 170)
 hideBanner = forms.checkbox(form, "Hide Banner", 5, 190)
 
 
 while true do
 	local backgroundColor = 0xD0FFFFFF
 	if not forms.ischecked(hideBanner) then
 		gui.drawBox(0, 0, 300, 26, backgroundColor, backgroundColor)
 	end
 
 	local species = pool.species[pool.currentSpecies]
 	local genome = species.genomes[pool.currentGenome]
 
 	if forms.ischecked(showNetwork) then
 		displayGenome(genome)
 	end
 
 	if pool.currentFrame%5 == 0 then
 		evaluateCurrent()
 	end
 
 	joypad.set(controller)
 
 	getPositions()
 	if marioX > rightmost then
 		rightmost = marioX
 		timeout = TimeoutConstant
 	end
 
 	timeout = timeout - 1
 
 
 	local timeoutBonus = pool.currentFrame / 4
 	if timeout + timeoutBonus <= 0 then
 		local fitness = rightmost - pool.currentFrame / 2
 		if gameinfo.getromname() == "Super Mario World (USA)" and rightmost > 4816 then
 			fitness = fitness + 1000
 		end
 		if gameinfo.getromname() == "Super Mario Bros." and rightmost > 3186 then
 			fitness = fitness + 1000
 		end
 		if fitness == 0 then
 			fitness = -1
 		end
 		genome.fitness = fitness
 
 		if fitness > pool.maxFitness then
 			pool.maxFitness = fitness
 			forms.settext(maxFitnessLabel, "Max Fitness: " .. math.floor(pool.maxFitness))
 			writeFile("backup." .. pool.generation .. "." .. forms.gettext(saveLoadFile))
 		end
 
 		console.writeline("Gen " .. pool.generation .. " species " .. pool.currentSpecies .. " genome " .. pool.currentGenome .. " fitness: " .. fitness)
 		pool.currentSpecies = 1
 		pool.currentGenome = 1
 		while fitnessAlreadyMeasured() do
 			nextGenome()
 		end
 		initializeRun()
 	end
 
 	local measured = 0
 	local total = 0
 	for _,species in pairs(pool.species) do
 		for _,genome in pairs(species.genomes) do
 			total = total + 1
 			if genome.fitness ~= 0 then
 				measured = measured + 1
 			end
 		end
 	end
 	if not forms.ischecked(hideBanner) then
 		gui.drawText(0, 0, "Gen " .. pool.generation .. " species " .. pool.currentSpecies .. " genome " .. pool.currentGenome .. " (" .. math.floor(measured/total*100) .. "%)", 0xFF000000, 11)
 		gui.drawText(0, 12, "Fitness: " .. math.floor(rightmost - (pool.currentFrame) / 2 - (timeout + timeoutBonus)*2/3), 0xFF000000, 11)
 		gui.drawText(100, 12, "Max Fitness: " .. math.floor(pool.maxFitness), 0xFF000000, 11)
 	end
 
 	pool.currentFrame = pool.currentFrame + 1
 
 	emu.frameadvance();
 end