Shuhei Iitsuka tushuhei

Google に入ったときの話 (Kinuko)

祭っぽいので私も書いてみることにした！お手軽に gist で。

と書き出したはいいが、私が受けたときは本も情報もあまりなく、かつ競プロ的なものの存在も知らなかったので、とりあえず家にあったアルゴリズムの本を２回くらい読み直した。そして受かった。最初っから情報があまりない方のパターンで申し訳ない 😄

Please consider using http://lygia.xyz instead of copy/pasting this functions. It expand suport for voronoi, voronoise, fbm, noise, worley, noise, derivatives and much more, through simple file dependencies. Take a look to https://github.com/patriciogonzalezvivo/lygia/tree/main/generative

float rand(float n){return fract(sin(n) * 43758.5453123);}

float noise(float p){
	float fl = floor(p);
  float fc = fract(p);


	function migrateObject(obj) {
	const clipPath = obj?.clipPath;
	if (clipPath && clipPath.type === 'group' && clipPath.eraser === true) {
	clipPath.type = 'eraser';
	delete clipPath.eraser;
	const rect = clipPath.objects.shift();
	obj.clipPath = rect.clipPath;
	obj.eraser = clipPath;
	}

	{
	"name": "tf-js",
	"version": "1.0.0",
	"main": "script.js",
	"license": "MIT",
	"dependencies": {
	"@tensorflow-models/mobilenet": "^0.2.2",
	"@tensorflow/tfjs": "^0.12.3",
	"@tensorflow/tfjs-node": "^0.1.9",
	"jpeg-js": "^0.3.4"

	import types
	import tensorflow as tf
	import numpy as np

	# Expressions are represented as lists of lists,
	# in lisp style -- the symbol name is the head (first element)
	# of the list, and the arguments follow.

	# add an expression to an expression list, recursively if necessary.
	def add_expr_to_list(exprlist, expr):

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

	"""Perlin noise implementation."""
	# Licensed under ISC
	from itertools import product
	import math
	import random


	def smoothstep(t):
	"""Smooth curve with a zero derivative at 0 and 1, making it useful for
	interpolating.