Adrian Seeley adrianseeley

## LTTBD.cs
public float[] Downsample(float[] array, int Length)
{
    int insert = 0;
    float[] window = new float[Length];
    float[] window_x = new float[Length];
    int bucket_size_less_start_and_end = Length - 2;

    float bucket_size = (float)(array.Length - 2) / bucket_size_less_start_and_end;
    int a = 0;
    int next_a = 0;

## Ease.cs
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;

namespace YOUR_NAMESPACE_HERE_BRO
{
    static class Ease
    {
        public static float LINEAR(float CurrentStep, float TotalSteps, float StartValue, float ValueChange)

## glutil.js
window.onerror = function (msg, url, lineno) {
	alert(url + '(' + lineno + '): ' + msg);
}

function createShader (str, type) {
	var shader = gl.createShader(type);
	gl.shaderSource(shader, str);
	gl.compileShader(shader);
	if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS))
		throw gl.getShaderInfoLog(shader);

## Ease.cs
/// <summary>
	/// Use:
	/// 1. Create a new instance of the ease class where steps are time, and values are
	/// the value you want to ease over that time.
	/// For example moving 100 pixels in 3 seconds:
	///
	/// Ease myEase = new Ease
	/// (
	///		currentStep: 0f,
	///		totalSteps: 3f,

## HTML5Jumper.html
<html>
	<div align = 'center'>
		<canvas
			id     = 'c'
			width  = '800'
			height = '600'
			style  = 'border:1px solid #000000;'>
		</canvas>

		<script>

## encrypt.js
const args = process.argv.slice(2);
const crypto = require('crypto');
const algorithm = 'aes-256-ctr';
const password = 'seelflix';
const fs = require('fs');
const path = require('path');

function read_dir (dir) {
    return fs.statSync(dir).isDirectory() ? Array.prototype.concat(...fs.readdirSync(dir).map(f => read_dir(path.join(dir, f)))) : dir;
};

## Python Open AI Gym Ubuntu Setup
sudo apt-get update
sudo apt-get install python3.6
sudo apt install python3-pip
sudo apt-get -y install cmake
sudo apt install zlib1g-dev
sudo apt-get install p7zip-full
pip3 install numpy
pip3 install sklearn
pip3 install gym
pip3 install gym[Box2D]

## plate_mold.scad
positive_width = 185;
positive_length = 370;
positive_thick = 3;
lip_height = 3;
lip_width = 3;

negative_oversize = 3;
double_negative_oversize = 3;

module positive()

## pso.js
// based on http://msdn.microsoft.com/en-us/magazine/hh335067.aspx
// usage example at bottom

function pso (number_of_dimensions, function_to_optimize, number_of_particles, number_of_iterations, fitness_threshold, inertia_weight, cognitive_weight, social_weight) {
	var particles           = [];
	var swarm_best_position = [];
	var swarm_best_fitness  = null;
	for (var p = 0; p < number_of_particles; p++) {
		particles.push({
			particle_position:      [],

## compute.html
<!DOCTYPE html>
<html>
<canvas id="c" width="128" height="128"></canvas>
<script src="glutil.js"></script>
<script id="vshader" type="text/plain">
	attribute vec2 vtxpos;
    varying vec2 texpos;
    void main() {
        texpos = (vtxpos / 2.) + vec2(0.5, 0.5);
        gl_Position = vec4(vtxpos, 0, 1);
	public float[] Downsample(float[] array, int Length)
	{
	int insert = 0;
	float[] window = new float[Length];
	float[] window_x = new float[Length];
	int bucket_size_less_start_and_end = Length - 2;

	float bucket_size = (float)(array.Length - 2) / bucket_size_less_start_and_end;
	int a = 0;
	int next_a = 0;
	using System;
	using System.Collections.Generic;
	using System.Linq;
	using System.Text;

	namespace YOUR_NAMESPACE_HERE_BRO
	{
	static class Ease
	{
	public static float LINEAR(float CurrentStep, float TotalSteps, float StartValue, float ValueChange)
	window.onerror = function (msg, url, lineno) {
	alert(url + '(' + lineno + '): ' + msg);
	}

	function createShader (str, type) {
	var shader = gl.createShader(type);
	gl.shaderSource(shader, str);
	gl.compileShader(shader);
	if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS))
	throw gl.getShaderInfoLog(shader);
	/// <summary>
	/// Use:
	/// 1. Create a new instance of the ease class where steps are time, and values are
	/// the value you want to ease over that time.
	/// For example moving 100 pixels in 3 seconds:
	///
	/// Ease myEase = new Ease
	/// (
	/// currentStep: 0f,
	/// totalSteps: 3f,
	<html>
	<div align = 'center'>
	<canvas
	id = 'c'
	width = '800'
	height = '600'
	style = 'border:1px solid #000000;'>
	</canvas>

	<script>
	const args = process.argv.slice(2);
	const crypto = require('crypto');
	const algorithm = 'aes-256-ctr';
	const password = 'seelflix';
	const fs = require('fs');
	const path = require('path');

	function read_dir (dir) {
	return fs.statSync(dir).isDirectory() ? Array.prototype.concat(...fs.readdirSync(dir).map(f => read_dir(path.join(dir, f)))) : dir;
	};
	sudo apt-get update
	sudo apt-get install python3.6
	sudo apt install python3-pip
	sudo apt-get -y install cmake
	sudo apt install zlib1g-dev
	sudo apt-get install p7zip-full
	pip3 install numpy
	pip3 install sklearn
	pip3 install gym
	pip3 install gym[Box2D]
	positive_width = 185;
	positive_length = 370;
	positive_thick = 3;
	lip_height = 3;
	lip_width = 3;

	negative_oversize = 3;
	double_negative_oversize = 3;

	module positive()
	// based on http://msdn.microsoft.com/en-us/magazine/hh335067.aspx
	// usage example at bottom

	function pso (number_of_dimensions, function_to_optimize, number_of_particles, number_of_iterations, fitness_threshold, inertia_weight, cognitive_weight, social_weight) {
	var particles = [];
	var swarm_best_position = [];
	var swarm_best_fitness = null;
	for (var p = 0; p < number_of_particles; p++) {
	particles.push({
	particle_position: [],
	<!DOCTYPE html>
	<html>
	<canvas id="c" width="128" height="128"></canvas>
	<script src="glutil.js"></script>
	<script id="vshader" type="text/plain">
	attribute vec2 vtxpos;
	varying vec2 texpos;
	void main() {
	texpos = (vtxpos / 2.) + vec2(0.5, 0.5);
	gl_Position = vec4(vtxpos, 0, 1);