parser: fix `for i, mut val in vals {; examples: vfmt flappybird
Alexander Medvednikov
parent
bcdf3ca0cf
commit
0d43ff2453
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@ -1,4 +1,3 @@
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module main
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import gg
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@ -7,7 +6,6 @@ import os
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import time
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import math
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import rand
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import neuroevolution
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const (
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@ -22,7 +20,6 @@ mut:
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y f64 = 250
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width f64 = 40
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height f64 = 30
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alive bool = true
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gravity f64
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velocity f64 = 0.3
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@ -43,12 +40,8 @@ fn (b Bird) is_dead(height f64, pipes []Pipe) bool {
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return true
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}
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for pipe in pipes {
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if !(
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b.x > pipe.x + pipe.width ||
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b.x + b.width < pipe.x ||
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b.y > pipe.y + pipe.height ||
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b.y + b.height < pipe.y
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) {
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if !(b.x > pipe.x + pipe.width ||
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b.x + b.width < pipe.x || b.y > pipe.y + pipe.height || b.y + b.height < pipe.y) {
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return true
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}
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}
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@ -79,7 +72,6 @@ mut:
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bird gg.Image
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pipetop gg.Image
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pipebottom gg.Image
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pipes []Pipe
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birds []Bird
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score int
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@ -88,12 +80,10 @@ mut:
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height f64 = win_height
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spawn_interval f64 = 90
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interval f64
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nv neuroevolution.Generations
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gen []neuroevolution.Network
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alives int
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generation int
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background_speed f64 = 0.5
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background_x f64
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}
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@ -104,7 +94,6 @@ fn (mut app App) start() {
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app.pipes = []
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app.birds = []
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app.gen = app.nv.generate()
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for _ in 0 .. app.gen.len {
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app.birds << Bird{}
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}
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@ -118,14 +107,12 @@ fn (app &App) is_it_end() bool {
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return false
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}
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}
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return true
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}
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fn (mut app App) update() {
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app.background_x += app.background_speed
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mut next_holl := f64(0)
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if app.birds.len > 0 {
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for i := 0; i < app.pipes.len; i += 2 {
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if app.pipes[i].x + app.pipes[i].width > app.birds[0].x {
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@ -134,8 +121,7 @@ fn (mut app App) update() {
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}
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}
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}
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for mut j, bird in app.birds {
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for j, mut bird in app.birds {
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if bird.alive {
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inputs := [
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bird.y / app.height,
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@ -145,9 +131,7 @@ fn (mut app App) update() {
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if res[0] > 0.5 {
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bird.flap()
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}
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bird.update()
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if bird.is_dead(app.height, app.pipes) {
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bird.alive = false
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app.alives--
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@ -156,10 +140,8 @@ fn (mut app App) update() {
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app.start()
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}
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}
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}
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}
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for k := 0; k < app.pipes.len; k++ {
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app.pipes[k].update()
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if app.pipes[k].is_out() {
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@ -167,44 +149,35 @@ fn (mut app App) update() {
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k--
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}
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}
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if app.interval == 0 {
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delta_bord := f64(50)
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pipe_holl := f64(120)
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holl_position := math.round(rand.f64() * (app.height - delta_bord * 2.0 - pipe_holl)) + delta_bord
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holl_position := math.round(rand.f64() *
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(app.height - delta_bord * 2.0 - pipe_holl)) + delta_bord
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app.pipes << Pipe{
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x: app.width
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y: 0
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height: holl_position
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}
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app.pipes << Pipe{
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x: app.width
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y: holl_position + pipe_holl
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height: app.height
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}
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}
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app.interval++
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if app.interval == app.spawn_interval {
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app.interval = 0
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}
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app.score++
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app.max_score = if app.score > app.max_score {
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app.score
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} else {
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app.max_score
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}
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app.max_score = if app.score > app.max_score { app.score } else { app.max_score }
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}
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fn main() {
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mut app := &App{
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gg: 0
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}
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app.gg = gg.new_context({
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app.gg = gg.new_context(
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bg_color: gx.white
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width: win_width
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height: win_height
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@ -215,7 +188,7 @@ fn main() {
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user_data: app
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init_fn: init_images
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font_path: os.resource_abs_path('../assets/fonts/RobotoMono-Regular.ttf')
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})
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)
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app.nv = neuroevolution.Generations{
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population: 50
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network: [2, 2, 1]
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@ -248,20 +221,22 @@ fn frame(app &App) {
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fn (app &App) display() {
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for i := 0; i < int(math.ceil(app.width / app.background.width) + 1.0); i++ {
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background_x := i * app.background.width - math.floor(int(app.background_x) % int(app.background.width))
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app.gg.draw_image(f32(background_x), 0, app.background.width, app.background.height, app.background)
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app.gg.draw_image(f32(background_x), 0, app.background.width, app.background.height,
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app.background)
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}
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for i, pipe in app.pipes {
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if i % 2 == 0 {
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app.gg.draw_image(f32(pipe.x), f32(pipe.y + pipe.height - app.pipetop.height), app.pipetop.width, app.pipetop.height, app.pipetop)
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app.gg.draw_image(f32(pipe.x), f32(pipe.y + pipe.height - app.pipetop.height),
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app.pipetop.width, app.pipetop.height, app.pipetop)
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} else {
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app.gg.draw_image(f32(pipe.x), f32(pipe.y), app.pipebottom.width, app.pipebottom.height, app.pipebottom)
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app.gg.draw_image(f32(pipe.x), f32(pipe.y), app.pipebottom.width, app.pipebottom.height,
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app.pipebottom)
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}
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}
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for bird in app.birds {
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if bird.alive {
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app.gg.draw_image(f32(bird.x), f32(bird.y), app.bird.width, app.bird.height, app.bird)
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app.gg.draw_image(f32(bird.x), f32(bird.y), app.bird.width, app.bird.height,
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app.bird)
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}
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}
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app.gg.draw_text_def(10, 25, 'Score: $app.score')
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@ -1,4 +1,3 @@
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module neuroevolution
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import rand
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@ -49,12 +48,10 @@ mut:
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}
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fn (mut n Network) populate(network []int) {
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assert network.len >= 2
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input := network[0]
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hiddens := network.slice(1, network.len - 1)
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output := network[network.len - 1]
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mut index := 0
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mut previous_neurons := 0
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mut input_layer := Layer{
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@ -62,7 +59,6 @@ fn (mut n Network) populate(network []int) {
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}
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input_layer.populate(input, previous_neurons)
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n.layers << input_layer
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previous_neurons = input
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index++
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for hidden in hiddens {
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@ -74,7 +70,6 @@ fn (mut n Network) populate(network []int) {
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n.layers << hidden_layer
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index++
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}
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mut output_layer := Layer{
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id: index
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}
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@ -83,7 +78,6 @@ fn (mut n Network) populate(network []int) {
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}
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fn (n Network) get_save() Save {
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mut save := Save{}
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for layer in n.layers {
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save.neurons << layer.neurons.len
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@ -97,11 +91,9 @@ fn (n Network) get_save() Save {
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}
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fn (mut n Network) set_save(save Save) {
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mut previous_neurons := 0
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mut index := 0
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mut index_weights := 0
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n.layers = []
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for save_neuron in save.neurons {
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mut layer := Layer{
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@ -123,13 +115,10 @@ fn (mut n Network) set_save(save Save) {
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pub fn (mut n Network) compute(inputs []f64) []f64 {
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assert n.layers.len > 0
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assert inputs.len == n.layers[0].neurons.len
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for i, input in inputs {
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n.layers[0].neurons[i].value = input
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}
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mut prev_layer := n.layers[0]
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for i in 1 .. n.layers.len {
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for j, neuron in n.layers[i].neurons {
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mut sum := f64(0)
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@ -140,13 +129,11 @@ pub fn (mut n Network) compute(inputs []f64) []f64 {
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}
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prev_layer = n.layers[i]
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}
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mut outputs := []f64{}
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mut last_layer := n.layers[n.layers.len - 1]
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for neuron in last_layer.neurons {
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outputs << neuron.value
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}
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return outputs
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}
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@ -174,65 +161,48 @@ mut:
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}
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fn (mut g Generation) add_genome(genome Genome) {
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mut i := 0
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for gg in g.genomes {
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if genome.score > gg.score {
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break
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}
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i++
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}
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g.genomes.insert(i, genome)
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}
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fn (g1 Genome) breed(g2 Genome, nb_child int) []Save {
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mut datas := []Save{}
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for _ in 0 .. nb_child {
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mut data := g1.network.clone()
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for i, weight in g2.network.weights {
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if rand.f64() <= 0.5 {
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data.weights[i] = weight
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}
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}
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for i, _ in data.weights {
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if rand.f64() <= 0.1 {
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data.weights[i] += (rand.f64() * 2 - 1) * 0.5
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}
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}
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datas << data
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}
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return datas
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}
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fn (g Generation) next(population int) []Save {
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mut nexts := []Save{}
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if population == 0 {
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return nexts
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}
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keep := round(population, 0.2)
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for i in 0 .. keep {
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if nexts.len < population {
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nexts << g.genomes[i].network.clone()
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}
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}
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random := round(population, 0.2)
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for _ in 0 .. random {
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if nexts.len < population {
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mut n := g.genomes[0].network.clone()
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for k, _ in n.weights {
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@ -241,7 +211,6 @@ fn (g Generation) next(population int) []Save {
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nexts << n
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}
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}
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mut max := 0
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out: for {
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for i in 0 .. max {
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@ -258,7 +227,6 @@ fn (g Generation) next(population int) []Save {
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max = 0
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}
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}
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return nexts
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}
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@ -277,7 +245,6 @@ fn (mut gs Generations) first() []Save {
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nn.populate(gs.network)
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out << nn.get_save()
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}
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gs.generations << Generation{}
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return out
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}
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@ -299,24 +266,16 @@ fn (mut gs Generations) restart() {
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}
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pub fn (mut gs Generations) generate() []Network {
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saves := if gs.generations.len == 0 {
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gs.first()
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} else {
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gs.next()
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}
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saves := if gs.generations.len == 0 { gs.first() } else { gs.next() }
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mut nns := []Network{}
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for save in saves {
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mut nn := Network{}
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nn.set_save(save)
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nns << nn
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}
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if gs.generations.len >= 2 {
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gs.generations.delete(0)
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}
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return nns
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}
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@ -326,4 +285,3 @@ pub fn (mut gs Generations) network_score(network Network, score int) {
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network: network.get_save()
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})
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}
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@ -51,6 +51,31 @@ pub fn (mut ctx Context) create_image(file string) Image {
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return img
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}
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// TODO copypasta
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pub fn (mut ctx Context) create_image_with_size(file string, width int, height int) Image {
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if !C.sg_isvalid() {
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// Sokol is not initialized yet, add stbi object to a queue/cache
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// ctx.image_queue << file
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stb_img := stbi.load(file) or { return Image{} }
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img := Image{
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width: width
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height: height
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nr_channels: stb_img.nr_channels
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ok: false
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data: stb_img.data
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ext: stb_img.ext
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path: file
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id: ctx.image_cache.len
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}
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ctx.image_cache << img
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return img
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}
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mut img := create_image(file)
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img.id = ctx.image_cache.len
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ctx.image_cache << img
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return img
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}
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// TODO remove this
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fn create_image(file string) Image {
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if !os.exists(file) {
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@ -127,7 +152,11 @@ pub fn (ctx &Context) draw_image(x f32, y f32, width f32, height f32, img_ &Imag
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x0 := f32(x) * ctx.scale
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y0 := f32(y) * ctx.scale
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x1 := f32(x + width) * ctx.scale
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y1 := f32(y + height) * ctx.scale
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mut y1 := f32(y + height) * ctx.scale
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if height == 0 {
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scale := f32(img.width) / f32(width)
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y1 = f32(y + int(f32(img.height) / scale)) * ctx.scale
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}
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//
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sgl.load_pipeline(ctx.timage_pip)
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sgl.enable_texture()
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@ -80,8 +80,8 @@ fn (mut p Parser) for_stmt() ast.Stmt {
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return for_c_stmt
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} else if p.peek_tok.kind in [.key_in, .comma] ||
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(p.tok.kind == .key_mut && p.peek_tok2.kind in [.key_in, .comma]) {
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// `for i in vals`, `for i in start .. end`
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val_is_mut := p.tok.kind == .key_mut
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// `for i in vals`, `for i in start .. end`, `for mut user in users`, `for i, mut user in users`
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mut val_is_mut := p.tok.kind == .key_mut
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if val_is_mut {
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p.next()
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}
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@ -91,6 +91,11 @@ fn (mut p Parser) for_stmt() ast.Stmt {
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mut val_var_name := p.check_name()
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if p.tok.kind == .comma {
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p.next()
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if p.tok.kind == .key_mut {
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// `for i, mut user in users {`
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p.next()
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val_is_mut = true
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}
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key_var_name = val_var_name
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val_var_pos = p.tok.position()
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val_var_name = p.check_name()
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