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Introduction

Markov Jr. is an open source C# application that creates procedural content primarily via applying Markov rewrite rules to a 2D or 3D grid. A rewrite rule has an input and output pattern, which essentially specifies what pattern to look for in the existing grid, and what to replace it with.

For example, given a 2D grid, this would replace any white dot with a white cross:

***/*W*/*** :: *W*/WWW/*W*

The left hand side is the rule input, and the right hand side is the output. The / character is used to delimit rows, and space is used to delimit Z-layers (in 3D grids). The input rule above translates to the 2D pattern:

깃 리모트 변경 하기

기존 리포지토리 깔끔하게 pull / push

git pull
git add .
git commit -m "clean push"
git push

NAVER DEVIEW 2017 Slides

키노트 영상

DAY 1

Generating Procedural Game Worlds with Wave Function Collapse

Wave Function Collapse (WFC) by @exutumno is a new algorithm that can generate procedural patterns from a sample image. It's especially exciting for game designers, letting us draw our ideas instead of hand coding them. We'll take a look at the kinds of output WFC can produce and the meaning of the algorithm's parameters. Then we'll walk through setting up WFC in javascript and the Unity game engine.

The traditional approach to this sort of output is to hand code algorithms that generate features, and combine them to alter your game map. For example you could sprinkle some trees at random coordinates, draw roads with a brownian motion, and add rooms with a Binary Space Partition. This is powerful but time consuming, and your original vision can someti

	/*
	Copy this into the console of any web page that is interactive and doesn't
	do hard reloads. You will hear your DOM changes as different pitches of
	audio.

	I have found this interesting for debugging, but also fun to hear web pages
	render like UIs do in movies.
	*/

	const audioCtx = new (window.AudioContext \|\| window.webkitAudioContext)()

	from IPython.display import HTML, JSON
	from google.colab import output

	# first, register the python function to use
	def array():
	return JSON([1,2,3]) # can send any JSON
	output.register_callback('array', array)

	# then invoke it later in an %%html block
	%%html

	import torch
	from torchvision import datasets

	class ImageFolderWithPaths(datasets.ImageFolder):
	"""Custom dataset that includes image file paths. Extends
	torchvision.datasets.ImageFolder
	"""

	# override the __getitem__ method. this is the method that dataloader calls
	def __getitem__(self, index):

	import numpy as np
	import tensorflow as tf
	from tensorflow.python.framework import dtypes

	# 1. Setting up initial values

	x = np.zeros((7, 7, 3))
	x[:, :, 0] = np.mat(
	"0 0 0 0 0 0 0;0 0 1 0 1 0 0;0 2 1 0 1 2 0;0 0 2 0 0 1 0;0 2 0 1 0 0 0;0 0 0 1 2 2 0;0 0 0 0 0 0 0"
	).A

	/**
	* Retrieve the array key corresponding to the largest element in the array.
	*
	* @param {Array.<number>} array Input array
	* @return {number} Index of array element with largest value
	*/
	function argMax(array) {
	return array.map((x, i) => [x, i]).reduce((r, a) => (a[0] > r[0] ? a : r))[1];
	}