Rich Wandell richwandell

Function definition

Function types are encoded by the byte 0x60 followed by the respective vectors of parameter and result types.

Example:

(module
    (func $add (param $lhs i32) (result i32)
 get_local $lhs)

	#[macro_export]
	macro_rules! num_enum {
	($name:ident $type:ident [$($keys:tt)*] $impl:ident) => {
	impl From<$type> for $name {
	fn from(v: $type) -> Self {
	match v {
	$(x if x == $name::$keys as $type => $name::$keys),*,
	_ => panic!("Cannot convert {}", v)
	}
	}

	use std::cell::RefCell;
	use std::collections::HashMap;
	use std::rc::Rc;
	use glutin_window::GlutinWindow as Window;
	use graphics::{clear, Transformed};
	use graphics::Context;
	use image::{ImageBuffer, Rgba};
	use opengl_graphics::{GlGraphics, GlyphCache, OpenGL, Texture, TextureSettings};
	use piston::{Button, PressEvent};
	use piston::event_loop::{Events, EventSettings};

	use std::thread;
	use std::time::Duration;
	use std::collections::LinkedList;
	extern crate num_cpus;

	fn main() {

	let num = num_cpus::get();

	let mut handles = LinkedList::new();

	buildHuffmanTree(charData) {
	//Sort by lowest frequency
	let cmp = (a, b) => {
	if(a.fre < b.fre) {
	return -1;
	}
	return 1;
	};
	//initially sorted list of nodes
	let data = charData

	epoch(d){
	//keep track of how many times we go through the dataset
	this.ni++;
	//Shuffle our data first
	let data = this.shuffle(d);
	const N = data.length;

	for(let i = 0; i < N; i ++){
	const x = data[i][0];
	const y = data[i][1];

	epoch(data){
	//keep track of how many times we go through the dataset
	this.ni++;
	const N = data.length;
	let bGrad = 0;
	let mGrad = 0;
	for(let i = 0; i < N; i ++){
	const x = data[i][0];
	const y = data[i][1];
	const diff = (y - this.line(x));

	function NeuralNetwork(){
	this.inputLayerSize = 2;
	this.outputLayerSize = 1;
	this.hiddenLayerSize = 3;
	this.W1 = math.randomInt([this.inputLayerSize, this.hiddenLayerSize])
	.map(function(row){
	return row.map(function(el){ return Math.random(); });
	});
	this.W2 = math.randomInt([this.hiddenLayerSize, this.outputLayerSize])
	.map(function(row){