adamnemecek

Adjointness

Adjoint

Adjointness expresses a condition that is essentially universal in mathematics, category theory, probability, logic, optimization, machine learning.

What is adjointness? Depends on the context.

This page is really illuminating.

adamnemecek

For background and further references see: Entity Component Systems on Wikipedia

ECS by Scott Bilas (GDC 2002)

Entity->Components->Update

• entity = class: no logic + no data OR at most small set of frequently used data (ie position)
• component = class: logic + data

foreach entity in allEntities do
    foreach component in entity.components do

adamnemecek

-- credit goes to sigfpe
{-# LANGUAGE MultiParamTypeClasses
    ,TemplateHaskell
    ,GeneralizedNewtypeDeriving
    ,DeriveFunctor
    ,FunctionalDependencies
    ,FlexibleInstances
    ,UndecidableInstances
    ,FlexibleContexts
    ,Rank2Types #-}

adamnemecek

//
// MIT License
// Copyright (c) 2017 Lachlan Hurst
// 
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:

adamnemecek

Keybase proof

I hereby claim:

• I am adamnemecek on github.
• I am adamnemecek (https://keybase.io/adamnemecek) on keybase.
• I have a public key ASAYuMz0BsFIrC7WD0_ZdesQuR0yrAOmRH6ZJlp_7CF-1wo

To claim this, I am signing this object:

adamnemecek

Courses

• Learning from data, course by Professor Yaser Abu-Mostafa

• http://courses.cs.washington.edu/courses/cse446/13sp/

• http://cs.nyu.edu/~dsontag/courses/ml13/

• http://holehouse.org/mlclass/

• ML, level 1 by Prof. Andrew Ng: Machine Learning, Website

• ML, level 1 by Prof. Andrew Ng: Machine Learning, Website, Lecture Notes, Videos

• ML, level 1 by Prof. Nando de Freitas: Machine Learning & Data Mining, Website, Lecture Notes, Videos

• ML, level 2 by Prof. Nando de Freitas: Machine Learning, Website, Lecture Notes, [Videos](https://www.youtube.com/playlist?list=PLE6Wd9FR--EdyJ5lb

adamnemecek

enum Sharp: Int { case C = 0, G, D, A, E, B, F }

func sKeyForNote(n: Sharp) -> [Sharp] {
  return (0..<n.rawValue)
    .map{ n in (n + 6) % 7 }
    .flatMap(Sharp.init)
}

enum Flat: Int { case F = 1, B, E, A, D, G }

adamnemecek

enum Expr {
  case Lit(Int)
  indirect case Neg(Expr)
  indirect case Add(Expr,Expr)
  indirect case Mul(Expr,Expr)
  indirect case Sub(Expr,Expr)
}

let spaces = many(oneof(" \n\r"))
func token<P: ParserType>(p: P) -> Parser<P.A> {

adamnemecek

func encode<T>(var value: T) -> NSData {
    return withUnsafePointer(&value) { p in
        NSData(bytes: p, length: sizeofValue(value))
    }
}

func decode<T>(data: NSData) -> T {
    let pointer = UnsafeMutablePointer<T>.alloc(sizeof(T.Type))
    data.getBytes(pointer)
    

adamnemecek

"""
Exercises for the Markov Chain Monte-Carlo (MCMC) course available at

http://users.aims.ac.za/~ioana/
"""

import numpy as np
import numpy.linalg as la
import pylab
from scipy import stats