1 files changed, 287 insertions, 0 deletions
diff --git a/v_windows/v/old/examples/flappylearning/modules/neuroevolution/neuronevolution.v b/v_windows/v/old/examples/flappylearning/modules/neuroevolution/neuronevolution.v
new file mode 100644
index 0000000..f3839ee
--- /dev/null
+++ b/v_windows/v/old/examples/flappylearning/modules/neuroevolution/neuronevolution.v
@@ -0,0 +1,287 @@
+module neuroevolution
+
+import rand
+import math
+
+fn random_clamped() f64 {
+	return rand.f64() * 2 - 1
+}
+
+pub fn activation(a f64) f64 {
+	ap := (-a) / 1
+	return (1 / (1 + math.exp(ap)))
+}
+
+fn round(a int, b f64) int {
+	return int(math.round(f64(a) * b))
+}
+
+struct Neuron {
+mut:
+	value   f64
+	weights []f64
+}
+
+fn (mut n Neuron) populate(nb int) {
+	for _ in 0 .. nb {
+		n.weights << random_clamped()
+	}
+}
+
+struct Layer {
+	id int
+mut:
+	neurons []Neuron
+}
+
+fn (mut l Layer) populate(nb_neurons int, nb_inputs int) {
+	for _ in 0 .. nb_neurons {
+		mut n := Neuron{}
+		n.populate(nb_inputs)
+		l.neurons << n
+	}
+}
+
+struct Network {
+mut:
+	layers []Layer
+}
+
+fn (mut n Network) populate(network []int) {
+	assert network.len >= 2
+	input := network[0]
+	hiddens := network[1..network.len - 1]
+	output := network[network.len - 1]
+	mut index := 0
+	mut previous_neurons := 0
+	mut input_layer := Layer{
+		id: index
+	}
+	input_layer.populate(input, previous_neurons)
+	n.layers << input_layer
+	previous_neurons = input
+	index++
+	for hidden in hiddens {
+		mut hidden_layer := Layer{
+			id: index
+		}
+		hidden_layer.populate(hidden, previous_neurons)
+		previous_neurons = hidden
+		n.layers << hidden_layer
+		index++
+	}
+	mut output_layer := Layer{
+		id: index
+	}
+	output_layer.populate(output, previous_neurons)
+	n.layers << output_layer
+}
+
+fn (n Network) get_save() Save {
+	mut save := Save{}
+	for layer in n.layers {
+		save.neurons << layer.neurons.len
+		for neuron in layer.neurons {
+			for weight in neuron.weights {
+				save.weights << weight
+			}
+		}
+	}
+	return save
+}
+
+fn (mut n Network) set_save(save Save) {
+	mut previous_neurons := 0
+	mut index := 0
+	mut index_weights := 0
+	n.layers = []
+	for save_neuron in save.neurons {
+		mut layer := Layer{
+			id: index
+		}
+		layer.populate(save_neuron, previous_neurons)
+		for mut neuron in layer.neurons {
+			for i in 0 .. neuron.weights.len {
+				neuron.weights[i] = save.weights[index_weights]
+				index_weights++
+			}
+		}
+		previous_neurons = save_neuron
+		index++
+		n.layers << layer
+	}
+}
+
+pub fn (mut n Network) compute(inputs []f64) []f64 {
+	assert n.layers.len > 0
+	assert inputs.len == n.layers[0].neurons.len
+	for i, input in inputs {
+		n.layers[0].neurons[i].value = input
+	}
+	mut prev_layer := n.layers[0]
+	for i in 1 .. n.layers.len {
+		for j, neuron in n.layers[i].neurons {
+			mut sum := f64(0)
+			for k, prev_layer_neuron in prev_layer.neurons {
+				sum += prev_layer_neuron.value * neuron.weights[k]
+			}
+			n.layers[i].neurons[j].value = activation(sum)
+		}
+		prev_layer = n.layers[i]
+	}
+	mut outputs := []f64{}
+	mut last_layer := n.layers[n.layers.len - 1]
+	for neuron in last_layer.neurons {
+		outputs << neuron.value
+	}
+	return outputs
+}
+
+struct Save {
+mut:
+	neurons []int
+	weights []f64
+}
+
+fn (s Save) clone() Save {
+	mut save := Save{}
+	save.neurons << s.neurons
+	save.weights << s.weights
+	return save
+}
+
+struct Genome {
+	score   int
+	network Save
+}
+
+struct Generation {
+mut:
+	genomes []Genome
+}
+
+fn (mut g Generation) add_genome(genome Genome) {
+	mut i := 0
+	for gg in g.genomes {
+		if genome.score > gg.score {
+			break
+		}
+		i++
+	}
+	g.genomes.insert(i, genome)
+}
+
+fn (g1 Genome) breed(g2 Genome, nb_child int) []Save {
+	mut datas := []Save{}
+	for _ in 0 .. nb_child {
+		mut data := g1.network.clone()
+		for i, weight in g2.network.weights {
+			if rand.f64() <= 0.5 {
+				data.weights[i] = weight
+			}
+		}
+		for i, _ in data.weights {
+			if rand.f64() <= 0.1 {
+				data.weights[i] += (rand.f64() * 2 - 1) * 0.5
+			}
+		}
+		datas << data
+	}
+	return datas
+}
+
+fn (g Generation) next(population int) []Save {
+	mut nexts := []Save{}
+	if population == 0 {
+		return nexts
+	}
+	keep := round(population, 0.2)
+	for i in 0 .. keep {
+		if nexts.len < population {
+			nexts << g.genomes[i].network.clone()
+		}
+	}
+	random := round(population, 0.2)
+	for _ in 0 .. random {
+		if nexts.len < population {
+			mut n := g.genomes[0].network.clone()
+			for k, _ in n.weights {
+				n.weights[k] = random_clamped()
+			}
+			nexts << n
+		}
+	}
+	mut max := 0
+	out: for {
+		for i in 0 .. max {
+			mut childs := g.genomes[i].breed(g.genomes[max], 1)
+			for c in childs {
+				nexts << c
+				if nexts.len >= population {
+					break out
+				}
+			}
+		}
+		max++
+		if max >= g.genomes.len - 1 {
+			max = 0
+		}
+	}
+	return nexts
+}
+
+pub struct Generations {
+pub:
+	population int
+	network    []int
+mut:
+	generations []Generation
+}
+
+fn (mut gs Generations) first() []Save {
+	mut out := []Save{}
+	for _ in 0 .. gs.population {
+		mut nn := Network{}
+		nn.populate(gs.network)
+		out << nn.get_save()
+	}
+	gs.generations << Generation{}
+	return out
+}
+
+fn (mut gs Generations) next() []Save {
+	assert gs.generations.len > 0
+	gen := gs.generations[gs.generations.len - 1].next(gs.population)
+	gs.generations << Generation{}
+	return gen
+}
+
+fn (mut gs Generations) add_genome(genome Genome) {
+	assert gs.generations.len > 0
+	gs.generations[gs.generations.len - 1].add_genome(genome)
+}
+
+fn (mut gs Generations) restart() {
+	gs.generations = []
+}
+
+pub fn (mut gs Generations) generate() []Network {
+	saves := if gs.generations.len == 0 { gs.first() } else { gs.next() }
+	mut nns := []Network{}
+	for save in saves {
+		mut nn := Network{}
+		nn.set_save(save)
+		nns << nn
+	}
+	if gs.generations.len >= 2 {
+		gs.generations.delete(0)
+	}
+	return nns
+}
+
+pub fn (mut gs Generations) network_score(network Network, score int) {
+	gs.add_genome(Genome{
+		score: score
+		network: network.get_save()
+	})
+}