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|
||||
0 2533274791006126 1760177390000000000 82547a8dd7f3efb3f077622e34876868 1 compiler_rt\fmodq_test.zig
|
||||
0 2533274791006127 1760177390000000000 82547a8dd7f3efb3f077622e34876868 1 compiler_rt\fmodx_test.zig
|
||||
0 2533274791006240 1760177390000000000 82547a8dd7f3efb3f077622e34876868 1 compiler_rt\udivmodti4_test.zig
|
||||
13531 4503599629259800 1760147602000000000 479ed8dfe695d0a2eea06b674b3ed0da 1 std\compress\flate\Token.zig
|
||||
20508 4503599629259795 1760147602000000000 453a65b5b55858b073099208c4e07ca8 1 std\compress\flate\BlockWriter.zig
|
||||
3588 4222124652549143 1760147602000000000 5a3d7acaca811bf6b6e62a58645eddae 1 std\compress\flate\Lookup.zig
|
||||
5945 4503599629259815 1760147602000000000 937d6b84c08ac71922db69ef4603ee39 1 std\compress\lzma\decode\lzbuffer.zig
|
||||
4994 4785074605970472 1760147602000000000 159872c0de3e30f43567e5ed7666125d 1 std\compress\lzma\decode\rangecoder.zig
|
||||
12650 3940649675838813 1760147602000000000 56befc361ef070a7bd0a2d3c1dc46994 1 std\crypto\pcurves\common.zig
|
||||
67958 5348024559392097 1760147602000000000 0f2daafefad01026d6796eec68d65d2e 1 std\crypto\pcurves\p256\p256_64.zig
|
||||
76136 9851624186762722 1760147602000000000 8ec5f177ef28f7a2a0ec8d103665db00 1 std\crypto\pcurves\p256\p256_scalar_64.zig
|
||||
134511 5629499536102968 1760147602000000000 2e0dda7c40794e981dd2d2471c4776a5 1 std\crypto\pcurves\p384\p384_64.zig
|
||||
137291 3940649675839033 1760147602000000000 81eb087d46e6c49907ae0c02d3230828 1 std\crypto\pcurves\p384\p384_scalar_64.zig
|
||||
73280 3940649675839106 1760147602000000000 c871f98dad15c7a8c29be9ccefa4b181 1 std\crypto\pcurves\secp256k1\secp256k1_64.zig
|
||||
75859 3940649675839116 1760147602000000000 e29275bdb0eb931fc383e7f2f5ded944 1 std\crypto\pcurves\secp256k1\secp256k1_scalar_64.zig
|
||||
1807 6473924466234599 1760147602000000000 f47429307ac0920ff18758ce86074549 1 std\crypto\codecs\asn1\der.zig
|
||||
7178 4503599629260001 1760147602000000000 6d4dab023a981a670d308b0b120c9077 1 std\crypto\codecs\asn1\Oid.zig
|
||||
0 9288674233341191 1760177394000000000 82547a8dd7f3efb3f077622e34876868 1 std\crypto\codecs\asn1\test.zig
|
||||
3892 9570149210051734 1760147602000000000 b6e0d691e62b1e9830666c4f8c67fdf4 1 std\crypto\Certificate\Bundle\macos.zig
|
||||
3081 7881299349789821 1760147602000000000 2aeda0b8b6036bb4d980778abb5a928a 1 std\fmt\parse_float\common.zig
|
||||
3073 3659174699129969 1760147602000000000 3950e4fa1fdd11d50db0b4abfc254022 1 std\fmt\parse_float\FloatStream.zig
|
||||
6036 3940649675840619 1760147602000000000 68169ffe43d55f0eb5e26b984ef98670 1 std\fmt\parse_float\FloatInfo.zig
|
||||
29140 8162774326500495 1760147602000000000 04115d79320f402803a56bd43cc34cf9 1 std\fmt\parse_float\decimal.zig
|
||||
2726 7881299349790279 1760147602000000000 7f318d60fafbfa10754d5644fd131ffe 1 std\math\complex\ldexp.zig
|
||||
0 14918173767556621 1760177394000000000 82547a8dd7f3efb3f077622e34876868 1 std\math\big\int_test.zig
|
||||
4082 7599824373079736 1760147602000000000 11a08913a0ec64b8325b0d29601479a7 1 std\os\linux\bpf\btf.zig
|
||||
1543 10977524093607611 1760147602000000000 95995c37b42f8d7a12578170850af6ee 1 std\os\linux\bpf\kern.zig
|
||||
2001 3659174699130895 1760147602000000000 539322409dfd2d684638b127038a6e33 1 std\os\uefi\protocol\service_binding.zig
|
||||
1743 26177172835983355 1760147602000000000 e4e18b6e0741218763e2f0eefe57c63c 1 std\os\uefi\protocol\loaded_image.zig
|
||||
4860 8162774326501355 1760147602000000000 db6a8b32ce1366281968b6a3dbffb173 1 std\os\uefi\protocol\device_path.zig
|
||||
3924 3659174699130886 1760147602000000000 636ee102096b7ee8d15380af87b55c99 1 std\os\uefi\protocol\rng.zig
|
||||
544 3377699722420241 1760147602000000000 a0f63cfe62d021c13659600cea4aaa1a 1 std\os\uefi\protocol\shell_parameters.zig
|
||||
1524 3096224745709588 1760147602000000000 7748a7cf094118fb46a4fa8cb0e326fb 1 std\os\uefi\protocol\simple_file_system.zig
|
||||
13432 7318349396369389 1760147602000000000 995882ac23c2af4019a5dd716a5f09f3 1 std\os\uefi\protocol\file.zig
|
||||
5171 9288674233343977 1760147602000000000 9136b751bd537d37181af6f96f23bc0d 1 std\os\uefi\protocol\block_io.zig
|
||||
1827 3940649675841564 1760147602000000000 dc816c9ebf174e0f2191f721ebf627fd 1 std\os\uefi\protocol\simple_text_input.zig
|
||||
5002 4222124652552224 1760147602000000000 33197652ebd5358fbd566ba1598a738f 1 std\os\uefi\protocol\simple_text_input_ex.zig
|
||||
9857 4222124652552225 1760147602000000000 ac75efaf87e83f490e1b05ca64f3d221 1 std\os\uefi\protocol\simple_text_output.zig
|
||||
2028 3377699722420246 1760147602000000000 2b300415efacd2dec40e6f1d67488799 1 std\os\uefi\protocol\simple_pointer.zig
|
||||
2398 15481123720978408 1760147602000000000 2c87d630cff25fb47ddfd2241990d8f4 1 std\os\uefi\protocol\absolute_pointer.zig
|
||||
4876 3377699722420232 1760147602000000000 1955a5a309db3628c52bf415af05b0f8 1 std\os\uefi\protocol\serial_io.zig
|
||||
4296 8444249303212014 1760147602000000000 702a798c1a856afdd0ade5a1ee67b3cd 1 std\os\uefi\protocol\graphics_output.zig
|
||||
2479 8444249303212012 1760147602000000000 0fab22ebe8c02b50099313679d4e2eb5 1 std\os\uefi\protocol\edid.zig
|
||||
15978 3377699722420245 1760147602000000000 95a49124c8237f8d2ab01672bf2a69b1 1 std\os\uefi\protocol\simple_network.zig
|
||||
9851 3377699722420221 1760147602000000000 fc89ee17ffacbefb54626ee378473525 1 std\os\uefi\protocol\managed_network.zig
|
||||
13201 4785074605973489 1760147602000000000 427b83b08791055f001b217a1bd39a8b 1 std\os\uefi\protocol\ip6.zig
|
||||
5373 7881299349790706 1760147602000000000 911ecc11b1404b1bef6afcda6cd7868e 1 std\os\uefi\protocol\ip6_config.zig
|
||||
8567 3940649675841570 1760147602000000000 47adbb9c98a53124032c9ef80ce5889f 1 std\os\uefi\protocol\udp6.zig
|
||||
4199 8162774326501359 1760147602000000000 b821152f4c9abddf1c1fb114333c66dd 1 std\os\uefi\protocol\hii_database.zig
|
||||
1712 8162774326501360 1760147602000000000 1a882e3749f4ef9b5c5827cbb346f6a3 1 std\os\uefi\protocol\hii_popup.zig
|
||||
47589 4222124652552231 1760147602000000000 da7566eb78da06f1636e8eb3a693eb3e 1 std\os\uefi\tables\boot_services.zig
|
||||
18947 4503599629262889 1760147602000000000 cb1eb30776a43459956ea4c0675dee88 1 std\os\uefi\tables\runtime_services.zig
|
||||
2796 4222124652552232 1760147602000000000 f0a08fa361dffa5eadb351a471801946 1 std\os\uefi\tables\configuration_table.zig
|
||||
2295 4222124652552234 1760147602000000000 25bf31dd5f33af51b4b9da897fa1e3d5 1 std\os\uefi\tables\system_table.zig
|
||||
214 4503599629262891 1760147602000000000 cdb95d6c52cd4654ef26be0bd9f114d4 1 std\os\uefi\tables\table_header.zig
|
||||
0 7599824373082018 1760177394000000000 82547a8dd7f3efb3f077622e34876868 1 std\zig\parser_test.zig
|
||||
15091 3377699722422420 1760147602000000000 e1ff2fae360a9d1df9777ae4f90bda6a 1 std\zig\system\arm.zig
|
||||
16495 3659174699133111 1760147602000000000 753462fa54f971ae23b566336ad73030 1 std\zig\system\darwin\macos.zig
|
||||
50924 9007199256635047 1760147602000000000 5c60970433f1092302f7ef4ff90d3f62 1 std\zig\llvm\ir.zig
|
||||
20581 2533274791006196 1760147598000000000 e8eaf68a4ffa3364b8f352326a575189 1 compiler_rt\rem_pio2_large.zig
|
||||
5806 7599824373077243 1760147602000000000 92f1dd53520d8191f93c177825b7845c 1 std\crypto\codecs\asn1\der\Decoder.zig
|
||||
5861 9288674233341185 1760147602000000000 650695830257d32d9ea72d767d918703 1 std\crypto\codecs\asn1\der\Encoder.zig
|
||||
419 6755399442947769 1760147602000000000 ed7dfc04a5d0c4f0853edb5414ce981e 1 std\os\linux\bpf\btf_ext.zig
|
||||
24293 9570149210054330 1760147602000000000 0c7d3ee9ea8e698a843ee6039fd161c4 1 std\os\linux\bpf\helpers.zig
|
||||
3221 11258999070315769 1760147602000000000 fda67b74062c7f535bb0a6d0f1fe74fb 1 std\crypto\codecs\asn1\der\ArrayListReverse.zig
|
||||
1317 67835469387425288 1770850771569010600 728d082d124116c0d5cdb17903db8cf3 0 C:\Users\Fores\hive_mind_zig\Hive_Mind-Zig\build.zig
|
||||
5476 7318349396371882 1770222202368985100 308097aac97271847d72e43226a0c168 0 C:\Users\Fores\AppData\Local\Temp\zls\build_runner\cf46548b062a7e79e448e80c05616097\shared.zig
|
||||
0
.zig-cache/h/timestamp
Normal file
0
.zig-cache/h/timestamp
Normal file
BIN
.zig-cache/o/46c2631951ca28b3efafd89ac0a7015f/build.exe
Normal file
BIN
.zig-cache/o/46c2631951ca28b3efafd89ac0a7015f/build.exe
Normal file
Binary file not shown.
BIN
.zig-cache/o/46c2631951ca28b3efafd89ac0a7015f/build.pdb
Normal file
BIN
.zig-cache/o/46c2631951ca28b3efafd89ac0a7015f/build.pdb
Normal file
Binary file not shown.
BIN
.zig-cache/o/46c2631951ca28b3efafd89ac0a7015f/build_zcu.obj
Normal file
BIN
.zig-cache/o/46c2631951ca28b3efafd89ac0a7015f/build_zcu.obj
Normal file
Binary file not shown.
BIN
.zig-cache/o/c09c1fd803e316cd07b89608fff955b9/build.exe
Normal file
BIN
.zig-cache/o/c09c1fd803e316cd07b89608fff955b9/build.exe
Normal file
Binary file not shown.
BIN
.zig-cache/o/c09c1fd803e316cd07b89608fff955b9/build.pdb
Normal file
BIN
.zig-cache/o/c09c1fd803e316cd07b89608fff955b9/build.pdb
Normal file
Binary file not shown.
BIN
.zig-cache/o/c09c1fd803e316cd07b89608fff955b9/build_zcu.obj
Normal file
BIN
.zig-cache/o/c09c1fd803e316cd07b89608fff955b9/build_zcu.obj
Normal file
Binary file not shown.
|
|
@ -0,0 +1,2 @@
|
|||
pub const packages = struct {};
|
||||
pub const root_deps: []const struct { []const u8, []const u8 } = &.{};
|
||||
BIN
.zig-cache/z/0b5053f4811f98c9784b84f6ea95ddaa
Normal file
BIN
.zig-cache/z/0b5053f4811f98c9784b84f6ea95ddaa
Normal file
Binary file not shown.
BIN
.zig-cache/z/6922dc9ebe38cc69a4be835a54f44a12
Normal file
BIN
.zig-cache/z/6922dc9ebe38cc69a4be835a54f44a12
Normal file
Binary file not shown.
BIN
.zig-cache/z/780d6e436e2fae933ca812aa89faecb4
Normal file
BIN
.zig-cache/z/780d6e436e2fae933ca812aa89faecb4
Normal file
Binary file not shown.
BIN
.zig-cache/z/929e554acdc6d5acde01af64a6c97d00
Normal file
BIN
.zig-cache/z/929e554acdc6d5acde01af64a6c97d00
Normal file
Binary file not shown.
156
ant_viewer.html
Normal file
156
ant_viewer.html
Normal file
|
|
@ -0,0 +1,156 @@
|
|||
<!DOCTYPE html>
|
||||
<html lang="it">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<title>Hive Mind Viewer</title>
|
||||
<style>
|
||||
body {
|
||||
margin: 0;
|
||||
background-color: #111;
|
||||
color: #eee;
|
||||
font-family: monospace;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
height: 100vh;
|
||||
overflow: hidden;
|
||||
}
|
||||
#header {
|
||||
width: 100%;
|
||||
padding: 10px;
|
||||
background: #222;
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
border-bottom: 1px solid #444;
|
||||
box-sizing: border-box;
|
||||
z-index: 10;
|
||||
}
|
||||
#container {
|
||||
flex-grow: 1;
|
||||
width: 100%;
|
||||
display: flex;
|
||||
justify-content: center;
|
||||
align-items: center;
|
||||
position: relative;
|
||||
}
|
||||
canvas {
|
||||
box-shadow: 0 0 50px rgba(0,0,0,0.8);
|
||||
image-rendering: pixelated;
|
||||
}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
|
||||
<div id="header">
|
||||
<span><b>HIVE MIND AI</b></span>
|
||||
<span>Episodio: <span id="episode">0</span></span>
|
||||
<span>Epsilon: <span id="epsilon">0.00</span></span>
|
||||
<span>Cibo: <span id="food-coords">?</span></span>
|
||||
</div>
|
||||
|
||||
<div id="container">
|
||||
<canvas id="gridCanvas"></canvas>
|
||||
</div>
|
||||
|
||||
<script>
|
||||
const canvas = document.getElementById('gridCanvas');
|
||||
const ctx = canvas.getContext('2d');
|
||||
|
||||
const epEl = document.getElementById('episode');
|
||||
const epsEl = document.getElementById('epsilon');
|
||||
const foodEl = document.getElementById('food-coords');
|
||||
|
||||
let gridSize = 100;
|
||||
|
||||
function drawWorld(data) {
|
||||
gridSize = data.grid_size;
|
||||
|
||||
const margin = 40;
|
||||
const availableW = document.getElementById('container').clientWidth - margin;
|
||||
const availableH = document.getElementById('container').clientHeight - margin;
|
||||
|
||||
const canvasSize = Math.min(availableW, availableH);
|
||||
|
||||
canvas.width = canvasSize;
|
||||
canvas.height = canvasSize;
|
||||
|
||||
const cellSize = canvasSize / gridSize;
|
||||
|
||||
ctx.fillStyle = "#1a1a1a";
|
||||
ctx.fillRect(0, 0, canvas.width, canvas.height);
|
||||
|
||||
if (cellSize > 4) {
|
||||
ctx.strokeStyle = "#2a2a2a";
|
||||
ctx.lineWidth = 1;
|
||||
ctx.beginPath();
|
||||
for(let i=0; i<=gridSize; i++) {
|
||||
const pos = i * cellSize;
|
||||
ctx.moveTo(pos, 0); ctx.lineTo(pos, canvas.height);
|
||||
ctx.moveTo(0, pos); ctx.lineTo(canvas.width, pos);
|
||||
}
|
||||
ctx.stroke();
|
||||
}
|
||||
|
||||
if (data.food) {
|
||||
const fx = data.food[0] * cellSize;
|
||||
const fy = data.food[1] * cellSize;
|
||||
|
||||
ctx.shadowBlur = 15;
|
||||
ctx.shadowColor = "#0f0";
|
||||
|
||||
ctx.fillStyle = "#00ff00";
|
||||
ctx.beginPath();
|
||||
ctx.arc(fx + cellSize/2, fy + cellSize/2, cellSize/1.5, 0, Math.PI*2);
|
||||
ctx.fill();
|
||||
|
||||
ctx.shadowBlur = 0;
|
||||
}
|
||||
|
||||
if (data.ants) {
|
||||
data.ants.forEach(ant => {
|
||||
const ax = ant[0] * cellSize;
|
||||
const ay = ant[1] * cellSize;
|
||||
const speed = ant[2];
|
||||
|
||||
let color = "orange";
|
||||
if (speed > 1.3) color = `rgb(255, ${255 - (speed*80)}, 0)`;
|
||||
else if (speed < 0.9) color = `rgb(${speed*100}, ${speed*100}, 255)`;
|
||||
|
||||
ctx.fillStyle = color;
|
||||
|
||||
if (cellSize < 3) {
|
||||
ctx.fillRect(ax, ay, cellSize, cellSize);
|
||||
} else {
|
||||
ctx.beginPath();
|
||||
ctx.arc(ax + cellSize/2, ay + cellSize/2, cellSize/2, 0, Math.PI*2);
|
||||
ctx.fill();
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
async function update() {
|
||||
try {
|
||||
const response = await fetch('ant_state.json?t=' + Date.now());
|
||||
if (!response.ok) return;
|
||||
|
||||
const data = await response.json();
|
||||
|
||||
epEl.innerText = data.episode;
|
||||
epsEl.innerText = data.epsilon;
|
||||
if(data.food) foodEl.innerText = `[${data.food[0]}, ${data.food[1]}]`;
|
||||
|
||||
drawWorld(data);
|
||||
} catch (e) {
|
||||
console.error(e);
|
||||
}
|
||||
}
|
||||
|
||||
setInterval(update, 50);
|
||||
|
||||
window.addEventListener('resize', () => {
|
||||
|
||||
});
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
51
build.zig
Normal file
51
build.zig
Normal file
|
|
@ -0,0 +1,51 @@
|
|||
const std = @import("std");
|
||||
|
||||
pub fn build(b: *std.Build) void {
|
||||
const target = b.standardTargetOptions(.{});
|
||||
const optimize = b.standardOptimizeOption(.{});
|
||||
const mod = b.addModule("AI_Zig", .{
|
||||
.root_source_file = b.path("src/root.zig"),
|
||||
.target = target,
|
||||
});
|
||||
|
||||
const exe = b.addExecutable(.{
|
||||
.name = "AI_Zig",
|
||||
.root_module = b.createModule(.{
|
||||
.root_source_file = b.path("src/main.zig"),
|
||||
.target = target,
|
||||
.optimize = optimize,
|
||||
.imports = &.{
|
||||
.{ .name = "AI_Zig", .module = mod },
|
||||
},
|
||||
}),
|
||||
});
|
||||
|
||||
b.installArtifact(exe);
|
||||
|
||||
const run_step = b.step("run", "Run the app");
|
||||
|
||||
const run_cmd = b.addRunArtifact(exe);
|
||||
run_step.dependOn(&run_cmd.step);
|
||||
|
||||
run_cmd.step.dependOn(b.getInstallStep());
|
||||
|
||||
if (b.args) |args| {
|
||||
run_cmd.addArgs(args);
|
||||
}
|
||||
|
||||
const mod_tests = b.addTest(.{
|
||||
.root_module = mod,
|
||||
});
|
||||
|
||||
const run_mod_tests = b.addRunArtifact(mod_tests);
|
||||
|
||||
const exe_tests = b.addTest(.{
|
||||
.root_module = exe.root_module,
|
||||
});
|
||||
|
||||
const run_exe_tests = b.addRunArtifact(exe_tests);
|
||||
|
||||
const test_step = b.step("test", "Run tests");
|
||||
test_step.dependOn(&run_mod_tests.step);
|
||||
test_step.dependOn(&run_exe_tests.step);
|
||||
}
|
||||
81
build.zig.zon
Normal file
81
build.zig.zon
Normal file
|
|
@ -0,0 +1,81 @@
|
|||
.{
|
||||
// This is the default name used by packages depending on this one. For
|
||||
// example, when a user runs `zig fetch --save <url>`, this field is used
|
||||
// as the key in the `dependencies` table. Although the user can choose a
|
||||
// different name, most users will stick with this provided value.
|
||||
//
|
||||
// It is redundant to include "zig" in this name because it is already
|
||||
// within the Zig package namespace.
|
||||
.name = .Hive_Mind_Zig,
|
||||
// This is a [Semantic Version](https://semver.org/).
|
||||
// In a future version of Zig it will be used for package deduplication.
|
||||
.version = "0.0.0",
|
||||
// Together with name, this represents a globally unique package
|
||||
// identifier. This field is generated by the Zig toolchain when the
|
||||
// package is first created, and then *never changes*. This allows
|
||||
// unambiguous detection of one package being an updated version of
|
||||
// another.
|
||||
//
|
||||
// When forking a Zig project, this id should be regenerated (delete the
|
||||
// field and run `zig build`) if the upstream project is still maintained.
|
||||
// Otherwise, the fork is *hostile*, attempting to take control over the
|
||||
// original project's identity. Thus it is recommended to leave the comment
|
||||
// on the following line intact, so that it shows up in code reviews that
|
||||
// modify the field.
|
||||
.fingerprint = 0xb6e57ad4ffe6bc4d, // Changing this has security and trust implications.
|
||||
// Tracks the earliest Zig version that the package considers to be a
|
||||
// supported use case.
|
||||
.minimum_zig_version = "0.15.1",
|
||||
// This field is optional.
|
||||
// Each dependency must either provide a `url` and `hash`, or a `path`.
|
||||
// `zig build --fetch` can be used to fetch all dependencies of a package, recursively.
|
||||
// Once all dependencies are fetched, `zig build` no longer requires
|
||||
// internet connectivity.
|
||||
.dependencies = .{
|
||||
// See `zig fetch --save <url>` for a command-line interface for adding dependencies.
|
||||
//.example = .{
|
||||
// // When updating this field to a new URL, be sure to delete the corresponding
|
||||
// // `hash`, otherwise you are communicating that you expect to find the old hash at
|
||||
// // the new URL. If the contents of a URL change this will result in a hash mismatch
|
||||
// // which will prevent zig from using it.
|
||||
// .url = "https://example.com/foo.tar.gz",
|
||||
//
|
||||
// // This is computed from the file contents of the directory of files that is
|
||||
// // obtained after fetching `url` and applying the inclusion rules given by
|
||||
// // `paths`.
|
||||
// //
|
||||
// // This field is the source of truth; packages do not come from a `url`; they
|
||||
// // come from a `hash`. `url` is just one of many possible mirrors for how to
|
||||
// // obtain a package matching this `hash`.
|
||||
// //
|
||||
// // Uses the [multihash](https://multiformats.io/multihash/) format.
|
||||
// .hash = "...",
|
||||
//
|
||||
// // When this is provided, the package is found in a directory relative to the
|
||||
// // build root. In this case the package's hash is irrelevant and therefore not
|
||||
// // computed. This field and `url` are mutually exclusive.
|
||||
// .path = "foo",
|
||||
//
|
||||
// // When this is set to `true`, a package is declared to be lazily
|
||||
// // fetched. This makes the dependency only get fetched if it is
|
||||
// // actually used.
|
||||
// .lazy = false,
|
||||
//},
|
||||
},
|
||||
// Specifies the set of files and directories that are included in this package.
|
||||
// Only files and directories listed here are included in the `hash` that
|
||||
// is computed for this package. Only files listed here will remain on disk
|
||||
// when using the zig package manager. As a rule of thumb, one should list
|
||||
// files required for compilation plus any license(s).
|
||||
// Paths are relative to the build root. Use the empty string (`""`) to refer to
|
||||
// the build root itself.
|
||||
// A directory listed here means that all files within, recursively, are included.
|
||||
.paths = .{
|
||||
"build.zig",
|
||||
"build.zig.zon",
|
||||
"src",
|
||||
// For example...
|
||||
//"LICENSE",
|
||||
//"README.md",
|
||||
},
|
||||
}
|
||||
11
src/activations.zig
Normal file
11
src/activations.zig
Normal file
|
|
@ -0,0 +1,11 @@
|
|||
const std = @import("std");
|
||||
|
||||
pub const Sigmoid = struct {
|
||||
pub fn apply(x: f32) f32 {
|
||||
return 1.0 / (1.0 + std.math.exp(-x));
|
||||
}
|
||||
|
||||
pub fn derivative(y: f32) f32 {
|
||||
return y * (1.0 - y);
|
||||
}
|
||||
};
|
||||
263
src/env.zig
Normal file
263
src/env.zig
Normal file
|
|
@ -0,0 +1,263 @@
|
|||
const std = @import("std");
|
||||
const Allocator = std.mem.Allocator;
|
||||
|
||||
pub const GRID_SIZE = 100;
|
||||
pub const NUM_ANTS = 20;
|
||||
|
||||
pub const TILE_EMPTY = 0;
|
||||
pub const TILE_WALL = 1;
|
||||
pub const TILE_FOOD = 2;
|
||||
|
||||
pub const Ant = struct {
|
||||
x: usize,
|
||||
y: usize,
|
||||
alive: bool,
|
||||
energy: f32,
|
||||
|
||||
gene_speed: f32,
|
||||
gene_metabolism: f32,
|
||||
|
||||
generation: usize,
|
||||
score: usize,
|
||||
};
|
||||
|
||||
pub const World = struct {
|
||||
grid: [GRID_SIZE][GRID_SIZE]u8,
|
||||
pheromones: [GRID_SIZE][GRID_SIZE]f32,
|
||||
ants: [NUM_ANTS]Ant,
|
||||
food_x: usize,
|
||||
food_y: usize,
|
||||
|
||||
best_ant_idx: usize,
|
||||
prng: std.Random.DefaultPrng,
|
||||
|
||||
pub fn init(seed: u64) World {
|
||||
var w = World{
|
||||
.grid = undefined,
|
||||
.pheromones = undefined,
|
||||
.ants = undefined,
|
||||
.food_x = 0,
|
||||
.food_y = 0,
|
||||
.best_ant_idx = 0,
|
||||
.prng = std.Random.DefaultPrng.init(seed),
|
||||
};
|
||||
w.reset();
|
||||
return w;
|
||||
}
|
||||
|
||||
pub fn reset(self: *World) void {
|
||||
const random = self.prng.random();
|
||||
|
||||
for (0..GRID_SIZE) |y| {
|
||||
for (0..GRID_SIZE) |x| {
|
||||
self.pheromones[y][x] = 0.0;
|
||||
if (x == 0 or y == 0 or x == GRID_SIZE - 1 or y == GRID_SIZE - 1) {
|
||||
self.grid[y][x] = TILE_WALL;
|
||||
} else {
|
||||
self.grid[y][x] = TILE_EMPTY;
|
||||
}
|
||||
}
|
||||
}
|
||||
self.respawnFood();
|
||||
|
||||
const center = GRID_SIZE / 2;
|
||||
for (0..NUM_ANTS) |i| {
|
||||
self.ants[i] = Ant{
|
||||
.x = center,
|
||||
.y = center,
|
||||
.alive = true,
|
||||
.energy = 100.0,
|
||||
.gene_speed = 0.8 + random.float(f32) * 0.4,
|
||||
.gene_metabolism = 1.0,
|
||||
.generation = 0,
|
||||
.score = 0,
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
fn respawnFood(self: *World) void {
|
||||
const random = self.prng.random();
|
||||
while (true) {
|
||||
const rx = random.intRangeAtMost(usize, 1, GRID_SIZE - 2);
|
||||
const ry = random.intRangeAtMost(usize, 1, GRID_SIZE - 2);
|
||||
if (self.grid[ry][rx] != TILE_WALL) {
|
||||
self.food_x = rx;
|
||||
self.food_y = ry;
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fn getScent(self: *World, x: usize, y: usize) f32 {
|
||||
const dx = if (x > self.food_x) x - self.food_x else self.food_x - x;
|
||||
const dy = if (y > self.food_y) y - self.food_y else self.food_y - y;
|
||||
const dist = @as(f32, @floatFromInt(dx + dy));
|
||||
return 1.0 / (dist + 1.0);
|
||||
}
|
||||
|
||||
fn isOccupied(self: *World, x: usize, y: usize) bool {
|
||||
for (self.ants) |ant| {
|
||||
if (ant.alive and ant.x == x and ant.y == y) return true;
|
||||
}
|
||||
return false;
|
||||
}
|
||||
|
||||
pub fn spawnChild(self: *World, parent_idx: usize) Ant {
|
||||
const parent = self.ants[parent_idx];
|
||||
const random = self.prng.random();
|
||||
|
||||
var new_speed = parent.gene_speed + (random.float(f32) * 0.2 - 0.1);
|
||||
|
||||
if (new_speed < 0.5) new_speed = 0.5;
|
||||
if (new_speed > 3.0) new_speed = 3.0;
|
||||
|
||||
const new_metabolism = new_speed * new_speed;
|
||||
|
||||
return Ant{
|
||||
.x = GRID_SIZE / 2,
|
||||
.y = GRID_SIZE / 2,
|
||||
.alive = true,
|
||||
.energy = 100.0,
|
||||
.gene_speed = new_speed,
|
||||
.gene_metabolism = new_metabolism,
|
||||
.generation = parent.generation + 1,
|
||||
.score = 0,
|
||||
};
|
||||
}
|
||||
|
||||
pub fn updateBestAnt(self: *World) void {
|
||||
var max_score: usize = 0;
|
||||
var best_idx: usize = 0;
|
||||
|
||||
for (self.ants, 0..) |ant, i| {
|
||||
if (ant.alive and ant.score > max_score) {
|
||||
max_score = ant.score;
|
||||
best_idx = i;
|
||||
}
|
||||
}
|
||||
if (max_score > 0) {
|
||||
self.best_ant_idx = best_idx;
|
||||
}
|
||||
}
|
||||
|
||||
pub fn stepAnt(self: *World, ant_idx: usize, action: usize) struct { f32, bool } {
|
||||
var ant = &self.ants[ant_idx];
|
||||
if (!ant.alive) return .{ 0.0, true };
|
||||
|
||||
const random = self.prng.random();
|
||||
|
||||
var moves_to_make: usize = 0;
|
||||
var chance = ant.gene_speed;
|
||||
|
||||
while (chance > 0) {
|
||||
if (chance >= 1.0) {
|
||||
moves_to_make += 1;
|
||||
chance -= 1.0;
|
||||
} else {
|
||||
if (random.float(f32) < chance) moves_to_make += 1;
|
||||
chance = 0;
|
||||
}
|
||||
}
|
||||
|
||||
if (moves_to_make == 0) {
|
||||
ant.energy -= 0.1;
|
||||
if (ant.energy <= 0) return .{ -10.0, true };
|
||||
return .{ 0.0, false };
|
||||
}
|
||||
|
||||
var total_reward: f32 = 0.0;
|
||||
|
||||
for (0..moves_to_make) |_| {
|
||||
if (ant.energy <= 0) break;
|
||||
|
||||
ant.energy -= (0.2 * ant.gene_metabolism);
|
||||
|
||||
const old_dist_x = if (ant.x > self.food_x) ant.x - self.food_x else self.food_x - ant.x;
|
||||
const old_dist_y = if (ant.y > self.food_y) ant.y - self.food_y else self.food_y - ant.y;
|
||||
const old_dist = old_dist_x + old_dist_y;
|
||||
|
||||
var new_x = ant.x;
|
||||
var new_y = ant.y;
|
||||
if (action == 0) new_y -= 1;
|
||||
if (action == 1) new_y += 1;
|
||||
if (action == 2) new_x -= 1;
|
||||
if (action == 3) new_x += 1;
|
||||
|
||||
if (self.grid[new_y][new_x] == TILE_WALL or self.isOccupied(new_x, new_y)) {
|
||||
ant.energy -= 1.0;
|
||||
total_reward -= 0.5;
|
||||
break;
|
||||
}
|
||||
|
||||
ant.x = new_x;
|
||||
ant.y = new_y;
|
||||
self.pheromones[new_y][new_x] = 1.0;
|
||||
|
||||
const new_dist = if (new_x > self.food_x) new_x - self.food_x else self.food_x - new_x;
|
||||
const total_new_dist = new_dist + (if (new_y > self.food_y) new_y - self.food_y else self.food_y - new_y);
|
||||
|
||||
const scent = self.getScent(new_x, new_y);
|
||||
if (total_new_dist < old_dist) total_reward += 1.5 + scent;
|
||||
if (total_new_dist > old_dist) total_reward -= 1.0;
|
||||
|
||||
if (new_x == self.food_x and new_y == self.food_y) {
|
||||
self.respawnFood();
|
||||
ant.energy = 100.0;
|
||||
ant.score += 1;
|
||||
total_reward += 50.0;
|
||||
|
||||
self.updateBestAnt();
|
||||
}
|
||||
}
|
||||
|
||||
if (ant.energy <= 0) {
|
||||
self.ants[ant_idx] = self.spawnChild(self.best_ant_idx);
|
||||
return .{ -10.0, true };
|
||||
}
|
||||
|
||||
return .{ total_reward, false };
|
||||
}
|
||||
|
||||
pub fn evaporatePheromones(self: *World) void {
|
||||
for (0..GRID_SIZE) |y| {
|
||||
for (0..GRID_SIZE) |x| {
|
||||
if (self.pheromones[y][x] > 0) self.pheromones[y][x] *= 0.995;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub fn getAntObservation(self: *World, allocator: Allocator, ant_idx: usize) ![]f32 {
|
||||
var obs = try allocator.alloc(f32, 15);
|
||||
const ant = self.ants[ant_idx];
|
||||
var idx: usize = 0;
|
||||
|
||||
const ax = @as(i32, @intCast(ant.x));
|
||||
const ay = @as(i32, @intCast(ant.y));
|
||||
|
||||
var dy: i32 = -1;
|
||||
while (dy <= 1) : (dy += 1) {
|
||||
var dx: i32 = -1;
|
||||
while (dx <= 1) : (dx += 1) {
|
||||
const py = @as(usize, @intCast(ay + dy));
|
||||
const px = @as(usize, @intCast(ax + dx));
|
||||
var val: f32 = 0.0;
|
||||
if (self.grid[py][px] == TILE_WALL) {
|
||||
val = -1.0;
|
||||
} else if (px == self.food_x and py == self.food_y) {
|
||||
val = 1.0;
|
||||
} else if (self.isOccupied(px, py) and (dx != 0 or dy != 0)) {
|
||||
val = -0.5;
|
||||
}
|
||||
obs[idx] = val;
|
||||
idx += 1;
|
||||
}
|
||||
}
|
||||
obs[9] = self.getScent(ant.x, ant.y);
|
||||
obs[10] = self.pheromones[ant.y][ant.x];
|
||||
obs[11] = if (self.food_y < ant.y) 1.0 else 0.0;
|
||||
obs[12] = if (self.food_y > ant.y) 1.0 else 0.0;
|
||||
obs[13] = if (self.food_x < ant.x) 1.0 else 0.0;
|
||||
obs[14] = if (self.food_x > ant.x) 1.0 else 0.0;
|
||||
return obs;
|
||||
}
|
||||
};
|
||||
120
src/layer.zig
Normal file
120
src/layer.zig
Normal file
|
|
@ -0,0 +1,120 @@
|
|||
const std = @import("std");
|
||||
const Allocator = std.mem.Allocator;
|
||||
|
||||
const SimdWidth = 8;
|
||||
const Vec = @Vector(SimdWidth, f32);
|
||||
|
||||
pub const DenseLayer = struct {
|
||||
weights: []f32,
|
||||
biases: []f32,
|
||||
output: []f32,
|
||||
inputs_count: usize,
|
||||
neurons_count: usize,
|
||||
use_sigmoid: bool,
|
||||
allocator: Allocator,
|
||||
|
||||
fn sigmoid(x: f32) f32 {
|
||||
return 1.0 / (1.0 + std.math.exp(-x));
|
||||
}
|
||||
|
||||
fn sigmoidDerivative(x: f32) f32 {
|
||||
return x * (1.0 - x);
|
||||
}
|
||||
|
||||
pub fn init(allocator: Allocator, inputs: usize, neurons: usize, seed: u64, use_sigmoid: bool) !DenseLayer {
|
||||
const weights = try allocator.alloc(f32, inputs * neurons);
|
||||
const biases = try allocator.alloc(f32, neurons);
|
||||
const output = try allocator.alloc(f32, neurons);
|
||||
|
||||
var prng = std.Random.DefaultPrng.init(seed);
|
||||
const random = prng.random();
|
||||
|
||||
for (weights) |*w| w.* = (random.float(f32) * 2.0 - 1.0) * 0.1;
|
||||
for (biases) |*b| b.* = 0.0;
|
||||
|
||||
return DenseLayer{
|
||||
.weights = weights,
|
||||
.biases = biases,
|
||||
.output = output,
|
||||
.inputs_count = inputs,
|
||||
.neurons_count = neurons,
|
||||
.use_sigmoid = use_sigmoid,
|
||||
.allocator = allocator,
|
||||
};
|
||||
}
|
||||
|
||||
pub fn deinit(self: *DenseLayer) void {
|
||||
self.allocator.free(self.weights);
|
||||
self.allocator.free(self.biases);
|
||||
self.allocator.free(self.output);
|
||||
}
|
||||
|
||||
pub fn forward(self: *DenseLayer, input: []const f32) []const f32 {
|
||||
for (0..self.neurons_count) |n| {
|
||||
var sum: f32 = self.biases[n];
|
||||
const w_start = n * self.inputs_count;
|
||||
|
||||
var vec_sum: Vec = @splat(0.0);
|
||||
var i: usize = 0;
|
||||
while (i + SimdWidth <= self.inputs_count) : (i += SimdWidth) {
|
||||
const v_in: Vec = input[i..][0..SimdWidth].*;
|
||||
const v_w: Vec = self.weights[w_start + i ..][0..SimdWidth].*;
|
||||
vec_sum += v_in * v_w;
|
||||
}
|
||||
sum += @reduce(.Add, vec_sum);
|
||||
|
||||
while (i < self.inputs_count) : (i += 1) {
|
||||
sum += input[i] * self.weights[w_start + i];
|
||||
}
|
||||
|
||||
if (self.use_sigmoid) {
|
||||
self.output[n] = sigmoid(sum);
|
||||
} else {
|
||||
self.output[n] = sum;
|
||||
}
|
||||
}
|
||||
return self.output;
|
||||
}
|
||||
|
||||
pub fn backward(self: *DenseLayer, output_gradient: []const f32, input_vals: []const f32, learning_rate: f32) []f32 {
|
||||
const input_gradient = self.allocator.alloc(f32, self.inputs_count) catch @panic("OOM");
|
||||
@memset(input_gradient, 0.0);
|
||||
|
||||
for (0..self.neurons_count) |n| {
|
||||
var derivative: f32 = 1.0;
|
||||
if (self.use_sigmoid) {
|
||||
derivative = sigmoidDerivative(self.output[n]);
|
||||
}
|
||||
|
||||
const delta = output_gradient[n] * derivative;
|
||||
const w_start = n * self.inputs_count;
|
||||
|
||||
self.biases[n] -= learning_rate * delta;
|
||||
|
||||
const v_delta: Vec = @splat(delta);
|
||||
const v_lr: Vec = @splat(learning_rate);
|
||||
const v_change_factor = v_delta * v_lr;
|
||||
|
||||
var i: usize = 0;
|
||||
while (i + SimdWidth <= self.inputs_count) : (i += SimdWidth) {
|
||||
var v_w: Vec = self.weights[w_start + i ..][0..SimdWidth].*;
|
||||
const v_in: Vec = input_vals[i..][0..SimdWidth].*;
|
||||
|
||||
var v_in_grad: Vec = input_gradient[i..][0..SimdWidth].*;
|
||||
v_in_grad += v_w * v_delta;
|
||||
input_gradient[i..][0..SimdWidth].* = v_in_grad;
|
||||
|
||||
v_w -= v_in * v_change_factor;
|
||||
self.weights[w_start + i ..][0..SimdWidth].* = v_w;
|
||||
}
|
||||
|
||||
while (i < self.inputs_count) : (i += 1) {
|
||||
const w_idx = w_start + i;
|
||||
const old_weight = self.weights[w_idx];
|
||||
input_gradient[i] += old_weight * delta;
|
||||
self.weights[w_idx] -= input_vals[i] * delta * learning_rate;
|
||||
}
|
||||
}
|
||||
return input_gradient;
|
||||
}
|
||||
};
|
||||
125
src/main.zig
Normal file
125
src/main.zig
Normal file
|
|
@ -0,0 +1,125 @@
|
|||
const std = @import("std");
|
||||
const World = @import("env.zig").World;
|
||||
const env = @import("env.zig");
|
||||
const Network = @import("modular_network.zig").Network;
|
||||
|
||||
const GAMMA: f32 = 0.9;
|
||||
const LR: f32 = 0.005;
|
||||
const EPSILON_START: f32 = 1.0;
|
||||
const EPSILON_END: f32 = 0.05;
|
||||
const DECAY_RATE: f32 = 0.0001;
|
||||
|
||||
fn maxVal(slice: []const f32) f32 {
|
||||
var m: f32 = -1.0e20;
|
||||
for (slice) |v| if (v > m) {
|
||||
m = v;
|
||||
};
|
||||
return m;
|
||||
}
|
||||
|
||||
fn argmax(slice: []const f32) usize {
|
||||
var m: f32 = -1.0e20;
|
||||
var idx: usize = 0;
|
||||
for (slice, 0..) |v, i| {
|
||||
if (v > m) {
|
||||
m = v;
|
||||
idx = i;
|
||||
}
|
||||
}
|
||||
return idx;
|
||||
}
|
||||
|
||||
fn exportAntJSON(world: *World, file_path: []const u8, episode: usize, avg_epsilon: f32) !void {
|
||||
const file = try std.fs.cwd().createFile(file_path, .{});
|
||||
defer file.close();
|
||||
|
||||
var buffer: [65536]u8 = undefined;
|
||||
var fba = std.heap.FixedBufferAllocator.init(&buffer);
|
||||
const allocator = fba.allocator();
|
||||
|
||||
var ants_json = std.ArrayList(u8){};
|
||||
defer ants_json.deinit(allocator);
|
||||
try ants_json.appendSlice(allocator, "[");
|
||||
|
||||
for (world.ants, 0..) |ant, i| {
|
||||
if (i > 0) try ants_json.appendSlice(allocator, ",");
|
||||
try std.fmt.format(ants_json.writer(allocator), "[{d},{d},{d:.2}]", .{ ant.x, ant.y, ant.gene_speed });
|
||||
}
|
||||
try ants_json.appendSlice(allocator, "]");
|
||||
|
||||
const json = try std.fmt.allocPrint(allocator, "{{\n \"grid_size\": {d},\n \"food\": [{d}, {d}],\n \"ants\": {s},\n \"episode\": {d},\n \"epsilon\": {d:.3}\n}}", .{ env.GRID_SIZE, world.food_x, world.food_y, ants_json.items, episode, avg_epsilon });
|
||||
try file.writeAll(json);
|
||||
}
|
||||
|
||||
pub fn main() !void {
|
||||
var gpa = std.heap.GeneralPurposeAllocator(.{}){};
|
||||
const allocator = gpa.allocator();
|
||||
defer _ = gpa.deinit();
|
||||
|
||||
var world = World.init(12345);
|
||||
var net = Network.init(allocator);
|
||||
defer net.deinit();
|
||||
|
||||
try net.addLayer(15, 40, 111, true);
|
||||
try net.addLayer(40, 4, 222, false);
|
||||
|
||||
var prng = std.Random.DefaultPrng.init(999);
|
||||
const random = prng.random();
|
||||
|
||||
std.debug.print("--- HIVE MIND TRAINING START ---\n", .{});
|
||||
std.debug.print("Mappa: {d}x{d} | Formiche: {d}\n", .{ env.GRID_SIZE, env.GRID_SIZE, env.NUM_ANTS });
|
||||
|
||||
var global_step: usize = 0;
|
||||
var epsilon: f32 = EPSILON_START;
|
||||
|
||||
while (true) {
|
||||
world.evaporatePheromones();
|
||||
|
||||
for (0..env.NUM_ANTS) |i| {
|
||||
const current_obs = try world.getAntObservation(allocator, i);
|
||||
defer allocator.free(current_obs);
|
||||
|
||||
var action: usize = 0;
|
||||
const q_values = net.forward(current_obs);
|
||||
|
||||
if (random.float(f32) < epsilon) {
|
||||
action = random.intRangeAtMost(usize, 0, 3);
|
||||
} else {
|
||||
action = argmax(q_values);
|
||||
}
|
||||
|
||||
const result = world.stepAnt(i, action);
|
||||
const reward = result[0];
|
||||
|
||||
var target_val = reward;
|
||||
|
||||
const next_obs = try world.getAntObservation(allocator, i);
|
||||
defer allocator.free(next_obs);
|
||||
const next_q_values = net.forward(next_obs);
|
||||
target_val += GAMMA * maxVal(next_q_values);
|
||||
|
||||
var target_vector = try allocator.alloc(f32, 4);
|
||||
defer allocator.free(target_vector);
|
||||
for (0..4) |j| target_vector[j] = q_values[j];
|
||||
target_vector[action] = target_val;
|
||||
|
||||
_ = try net.train(current_obs, target_vector, LR);
|
||||
}
|
||||
|
||||
global_step += 1;
|
||||
|
||||
if (epsilon > EPSILON_END) {
|
||||
epsilon -= DECAY_RATE;
|
||||
}
|
||||
|
||||
if (global_step % 10 == 0) {
|
||||
try exportAntJSON(&world, "ant_state.json", global_step, epsilon);
|
||||
|
||||
if (global_step % 100 == 0) {
|
||||
std.debug.print("Step: {d} | Epsilon: {d:.3} | Cibo: [{d},{d}]\r", .{ global_step, epsilon, world.food_x, world.food_y });
|
||||
}
|
||||
|
||||
std.Thread.sleep(100 * 1_000_000);
|
||||
}
|
||||
}
|
||||
}
|
||||
189
src/modular_network.zig
Normal file
189
src/modular_network.zig
Normal file
|
|
@ -0,0 +1,189 @@
|
|||
const std = @import("std");
|
||||
const Allocator = std.mem.Allocator;
|
||||
const DenseLayer = @import("layer.zig").DenseLayer;
|
||||
|
||||
pub const Network = struct {
|
||||
layers: std.ArrayList(DenseLayer),
|
||||
allocator: Allocator,
|
||||
|
||||
pub fn init(allocator: Allocator) Network {
|
||||
return Network{
|
||||
.layers = std.ArrayList(DenseLayer){},
|
||||
.allocator = allocator,
|
||||
};
|
||||
}
|
||||
|
||||
pub fn deinit(self: *Network) void {
|
||||
for (self.layers.items) |*layer| {
|
||||
layer.deinit();
|
||||
}
|
||||
self.layers.deinit(self.allocator);
|
||||
}
|
||||
|
||||
pub fn addLayer(self: *Network, input_size: usize, output_size: usize, seed: u64, use_sigmoid: bool) !void {
|
||||
const layer = try DenseLayer.init(self.allocator, input_size, output_size, seed, use_sigmoid);
|
||||
try self.layers.append(self.allocator, layer);
|
||||
}
|
||||
|
||||
pub fn forward(self: *Network, input: []const f32) []const f32 {
|
||||
var current_input = input;
|
||||
for (self.layers.items) |*layer| {
|
||||
current_input = layer.forward(current_input);
|
||||
}
|
||||
return current_input;
|
||||
}
|
||||
|
||||
pub fn printTopology(self: *Network) void {
|
||||
std.debug.print("Architettura Rete: [Input]", .{});
|
||||
for (self.layers.items) |layer| {
|
||||
std.debug.print(" -> [Dense:{d}]", .{layer.neurons_count});
|
||||
}
|
||||
std.debug.print("\n", .{});
|
||||
}
|
||||
|
||||
pub fn train(self: *Network, input: []const f32, target: []const f32, lr: f32) !f32 {
|
||||
_ = self.forward(input);
|
||||
|
||||
const last_layer_idx = self.layers.items.len - 1;
|
||||
const last_layer = &self.layers.items[last_layer_idx];
|
||||
|
||||
var output_errors = try self.allocator.alloc(f32, last_layer.neurons_count);
|
||||
defer self.allocator.free(output_errors);
|
||||
|
||||
var total_loss: f32 = 0.0;
|
||||
for (0..last_layer.neurons_count) |i| {
|
||||
const err = last_layer.output[i] - target[i];
|
||||
output_errors[i] = err;
|
||||
total_loss += err * err;
|
||||
}
|
||||
|
||||
var next_gradients = output_errors;
|
||||
var i: usize = self.layers.items.len;
|
||||
|
||||
while (i > 0) {
|
||||
i -= 1;
|
||||
var layer = &self.layers.items[i];
|
||||
const prev_input = if (i == 0) input else self.layers.items[i - 1].output;
|
||||
|
||||
const new_gradients = layer.backward(next_gradients, prev_input, lr);
|
||||
|
||||
if (next_gradients.ptr != output_errors.ptr) {
|
||||
self.allocator.free(next_gradients);
|
||||
}
|
||||
|
||||
next_gradients = new_gradients;
|
||||
}
|
||||
|
||||
if (next_gradients.ptr != output_errors.ptr) {
|
||||
self.allocator.free(next_gradients);
|
||||
}
|
||||
|
||||
return total_loss;
|
||||
}
|
||||
|
||||
pub fn save(self: *Network, file_path: []const u8) !void {
|
||||
const file = try std.fs.cwd().createFile(file_path, .{});
|
||||
defer file.close();
|
||||
|
||||
const MagicNumber: u64 = 0xDEADBEEF;
|
||||
|
||||
try file.writeAll(std.mem.asBytes(&MagicNumber));
|
||||
|
||||
const layer_count = @as(u64, self.layers.items.len);
|
||||
try file.writeAll(std.mem.asBytes(&layer_count));
|
||||
|
||||
for (self.layers.items) |layer| {
|
||||
const inputs = @as(u64, layer.inputs_count);
|
||||
const neurons = @as(u64, layer.neurons_count);
|
||||
try file.writeAll(std.mem.asBytes(&inputs));
|
||||
try file.writeAll(std.mem.asBytes(&neurons));
|
||||
|
||||
const weights_bytes = std.mem.sliceAsBytes(layer.weights);
|
||||
try file.writeAll(weights_bytes);
|
||||
|
||||
const biases_bytes = std.mem.sliceAsBytes(layer.biases);
|
||||
try file.writeAll(biases_bytes);
|
||||
}
|
||||
}
|
||||
|
||||
pub fn load(allocator: Allocator, file_path: []const u8) !Network {
|
||||
const file = try std.fs.cwd().openFile(file_path, .{});
|
||||
defer file.close();
|
||||
|
||||
var net = Network.init(allocator);
|
||||
var success = false;
|
||||
|
||||
defer if (!success) net.deinit();
|
||||
|
||||
var magic: u64 = 0;
|
||||
_ = try file.readAll(std.mem.asBytes(&magic));
|
||||
if (magic != 0xDEADBEEF) return error.InvalidNetworkFile;
|
||||
|
||||
var layer_count: u64 = 0;
|
||||
_ = try file.readAll(std.mem.asBytes(&layer_count));
|
||||
|
||||
var i: u64 = 0;
|
||||
while (i < layer_count) : (i += 1) {
|
||||
var inputs: u64 = 0;
|
||||
var neurons: u64 = 0;
|
||||
|
||||
_ = try file.readAll(std.mem.asBytes(&inputs));
|
||||
_ = try file.readAll(std.mem.asBytes(&neurons));
|
||||
|
||||
const is_last = (i == layer_count - 1);
|
||||
try net.addLayer(@intCast(inputs), @intCast(neurons), 0, !is_last);
|
||||
|
||||
const layer = &net.layers.items[net.layers.items.len - 1];
|
||||
|
||||
const weights_bytes = std.mem.sliceAsBytes(layer.weights);
|
||||
_ = try file.readAll(weights_bytes);
|
||||
|
||||
const biases_bytes = std.mem.sliceAsBytes(layer.biases);
|
||||
_ = try file.readAll(biases_bytes);
|
||||
}
|
||||
|
||||
success = true;
|
||||
return net;
|
||||
}
|
||||
|
||||
pub fn exportJSON(self: *Network, file_path: []const u8, epoch: usize, loss: f32, input_snapshot: ?[]const f32) !void {
|
||||
const file = try std.fs.cwd().createFile(file_path, .{});
|
||||
defer file.close();
|
||||
|
||||
const Utils = struct {
|
||||
fn print(f: std.fs.File, comptime fmt: []const u8, args: anytype) !void {
|
||||
var buf: [256]u8 = undefined;
|
||||
const text = try std.fmt.bufPrint(&buf, fmt, args);
|
||||
try f.writeAll(text);
|
||||
}
|
||||
};
|
||||
|
||||
try Utils.print(file, "{{\n \"epoch\": {d},\n \"loss\": {d:.6},\n", .{ epoch, loss });
|
||||
|
||||
if (input_snapshot) |pixels| {
|
||||
try file.writeAll(" \"input_pixels\": [");
|
||||
for (pixels, 0..) |p, idx| {
|
||||
try Utils.print(file, "{d:.2}", .{p});
|
||||
if (idx < pixels.len - 1) try file.writeAll(",");
|
||||
}
|
||||
try file.writeAll("],\n");
|
||||
}
|
||||
|
||||
try file.writeAll(" \"layers\": [\n");
|
||||
|
||||
for (self.layers.items, 0..) |layer, i| {
|
||||
try Utils.print(file, " {{\n \"layer_index\": {d},\n \"neurons\": {d},\n \"inputs\": {d},\n \"weights\": [", .{ i, layer.neurons_count, layer.inputs_count });
|
||||
|
||||
const max_w = if (layer.weights.len > 1000) 100 else layer.weights.len;
|
||||
|
||||
for (layer.weights[0..max_w], 0..) |w, w_idx| {
|
||||
try Utils.print(file, "{d:.4}", .{w});
|
||||
if (w_idx < max_w - 1) try file.writeAll(", ");
|
||||
}
|
||||
|
||||
try file.writeAll("]\n }");
|
||||
if (i < self.layers.items.len - 1) try file.writeAll(",\n");
|
||||
}
|
||||
try file.writeAll("\n ]\n}\n");
|
||||
}
|
||||
};
|
||||
83
src/network.zig
Normal file
83
src/network.zig
Normal file
|
|
@ -0,0 +1,83 @@
|
|||
const std = @import("std");
|
||||
|
||||
pub const SimpleNetwork = struct {
|
||||
w_hidden: [2][2]f32,
|
||||
b_hidden: [2]f32,
|
||||
|
||||
w_output: [2]f32,
|
||||
b_output: f32,
|
||||
|
||||
hidden_outputs: [2]f32,
|
||||
|
||||
pub fn init(seed: u64) SimpleNetwork {
|
||||
var prng = std.Random.DefaultPrng.init(seed);
|
||||
const rand = prng.random();
|
||||
|
||||
var net = SimpleNetwork{
|
||||
.w_hidden = undefined,
|
||||
.b_hidden = undefined,
|
||||
.w_output = undefined,
|
||||
.b_output = 0.0,
|
||||
.hidden_outputs = undefined,
|
||||
};
|
||||
|
||||
for (&net.w_hidden) |*row| {
|
||||
row[0] = rand.float(f32) * 2.0 - 1.0;
|
||||
row[1] = rand.float(f32) * 2.0 - 1.0;
|
||||
}
|
||||
for (&net.b_hidden) |*b| b.* = 0.0;
|
||||
|
||||
for (&net.w_output) |*w| w.* = rand.float(f32) * 2.0 - 1.0;
|
||||
|
||||
return net;
|
||||
}
|
||||
|
||||
fn sigmoid(x: f32) f32 {
|
||||
return 1.0 / (1.0 + std.math.exp(-x));
|
||||
}
|
||||
|
||||
fn sigmoid_derivative(x: f32) f32 {
|
||||
return x * (1.0 - x);
|
||||
}
|
||||
|
||||
pub fn forward(self: *SimpleNetwork, input: [2]f32) f32 {
|
||||
for (0..2) |i| {
|
||||
const sum = (input[0] * self.w_hidden[i][0]) +
|
||||
(input[1] * self.w_hidden[i][1]) +
|
||||
self.b_hidden[i];
|
||||
self.hidden_outputs[i] = sigmoid(sum);
|
||||
}
|
||||
|
||||
const sum_out = (self.hidden_outputs[0] * self.w_output[0]) +
|
||||
(self.hidden_outputs[1] * self.w_output[1]) +
|
||||
self.b_output;
|
||||
|
||||
return sigmoid(sum_out);
|
||||
}
|
||||
|
||||
pub fn train(self: *SimpleNetwork, input: [2]f32, target: f32, lr: f32) f32 {
|
||||
const prediction = self.forward(input);
|
||||
|
||||
const output_error = target - prediction;
|
||||
const output_delta = output_error * sigmoid_derivative(prediction);
|
||||
|
||||
var hidden_deltas: [2]f32 = undefined;
|
||||
for (0..2) |i| {
|
||||
const error_contrib = output_delta * self.w_output[i];
|
||||
hidden_deltas[i] = error_contrib * sigmoid_derivative(self.hidden_outputs[i]);
|
||||
}
|
||||
|
||||
for (0..2) |i| {
|
||||
self.w_output[i] += lr * output_delta * self.hidden_outputs[i];
|
||||
}
|
||||
self.b_output += lr * output_delta;
|
||||
|
||||
for (0..2) |i| {
|
||||
self.w_hidden[i][0] += lr * hidden_deltas[i] * input[0];
|
||||
self.w_hidden[i][1] += lr * hidden_deltas[i] * input[1];
|
||||
self.b_hidden[i] += lr * hidden_deltas[i];
|
||||
}
|
||||
|
||||
return output_error * output_error;
|
||||
}
|
||||
};
|
||||
60
src/neuron.zig
Normal file
60
src/neuron.zig
Normal file
|
|
@ -0,0 +1,60 @@
|
|||
const std = @import("std");
|
||||
const Tensor = @import("tensor.zig").Tensor;
|
||||
|
||||
pub const Neuron = struct {
|
||||
weights: Tensor,
|
||||
bias: f32,
|
||||
|
||||
pub fn init(allocator: std.mem.Allocator, input_size: usize) !Neuron {
|
||||
const weights = try Tensor.init(allocator, &[_]usize{input_size});
|
||||
|
||||
var seed: u64 = undefined;
|
||||
try std.posix.getrandom(std.mem.asBytes(&seed));
|
||||
|
||||
var prng = std.Random.DefaultPrng.init(seed);
|
||||
const rand = prng.random();
|
||||
|
||||
for (weights.data) |*w| {
|
||||
w.* = rand.float(f32) * 2.0 - 1.0;
|
||||
}
|
||||
|
||||
return Neuron{
|
||||
.weights = weights,
|
||||
.bias = 0.0,
|
||||
};
|
||||
}
|
||||
|
||||
pub fn deinit(self: *Neuron) void {
|
||||
self.weights.deinit();
|
||||
}
|
||||
|
||||
pub fn forward(self: *Neuron, inputs: Tensor) f32 {
|
||||
std.debug.assert(inputs.data.len == self.weights.data.len);
|
||||
|
||||
var sum: f32 = 0.0;
|
||||
for (inputs.data, self.weights.data) |x, w| {
|
||||
sum += x * w;
|
||||
}
|
||||
|
||||
const z = sum + self.bias;
|
||||
|
||||
return if (z > 0) z else 0;
|
||||
}
|
||||
|
||||
pub fn train(self: *Neuron, inputs: Tensor, target: f32, learning_rate: f32) f32 {
|
||||
const prediction = self.forward(inputs);
|
||||
|
||||
const err = prediction - target;
|
||||
|
||||
const d_relu: f32 = if (prediction > 0) 1.0 else 0.0;
|
||||
|
||||
for (self.weights.data, inputs.data) |*w, x| {
|
||||
const gradient = err * d_relu * x;
|
||||
w.* -= learning_rate * gradient;
|
||||
}
|
||||
|
||||
self.bias -= learning_rate * err * d_relu;
|
||||
|
||||
return err * err;
|
||||
}
|
||||
};
|
||||
19
src/root.zig
Normal file
19
src/root.zig
Normal file
|
|
@ -0,0 +1,19 @@
|
|||
const std = @import("std");
|
||||
|
||||
pub fn bufferedPrint() !void {
|
||||
var stdout_buffer: [1024]u8 = undefined;
|
||||
var stdout_writer = std.fs.File.stdout().writer(&stdout_buffer);
|
||||
const stdout = &stdout_writer.interface;
|
||||
|
||||
try stdout.print("Run `zig build test` to run the tests.\n", .{});
|
||||
|
||||
try stdout.flush();
|
||||
}
|
||||
|
||||
pub fn add(a: i32, b: i32) i32 {
|
||||
return a + b;
|
||||
}
|
||||
|
||||
test "basic add functionality" {
|
||||
try std.testing.expect(add(3, 7) == 10);
|
||||
}
|
||||
28
src/tensor.zig
Normal file
28
src/tensor.zig
Normal file
|
|
@ -0,0 +1,28 @@
|
|||
const std = @import("std");
|
||||
const Allocator = std.mem.Allocator;
|
||||
|
||||
pub const Tensor = struct {
|
||||
data: []f32,
|
||||
shape: []const usize,
|
||||
allocator: Allocator,
|
||||
|
||||
pub fn init(allocator: Allocator, shape: []const usize) !Tensor {
|
||||
var size: usize = 1;
|
||||
for (shape) |dim| {
|
||||
size *= dim;
|
||||
}
|
||||
|
||||
const data = try allocator.alloc(f32, size);
|
||||
@memset(data, 0);
|
||||
|
||||
return Tensor{
|
||||
.data = data,
|
||||
.shape = shape,
|
||||
.allocator = allocator,
|
||||
};
|
||||
}
|
||||
|
||||
pub fn deinit(self: *Tensor) void {
|
||||
self.allocator.free(self.data);
|
||||
}
|
||||
};
|
||||
Loading…
Reference in a new issue