Robert Dionne robertsdionne

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

Generic 1,2,3 Noise

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

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

Knowledge Bases (KBs) are effective tools for Question Answering (QA) but are often too restrictive (due to fixed schema) and too sparse (due to limitations of Information Extraction (IE) systems).
The paper proposes Key-Value Memory Networks, a neural network architecture based on Memory Networks that can leverage both KBs and raw data for QA.
The paper also introduces MOVIEQA, a new QA dataset that can be answered by a perfect KB, by Wikipedia pages and by an imperfect KB obtained using IE techniques thereby allowing a comparison between systems using any of the three sources.
Link to the paper.

	var collision = {};


	// aabb: <THREE.Box3>
	// Plane: <THREE.Plane>
	collision.isIntersectionAABBPlane = function ( aabb, Plane ) {

	var center = new THREE.Vector3().addVectors( aabb.max, aabb.min ).multiplyScalar( 0.5 ),
	extents = new THREE.Vector3().subVectors( aabb.max, center );

	# setup python virtualenv and install nodeenv
	virtualenv .python
	. .python/bin/activate
	pip install nodeenv

	# setup nodeenv with the node of your choice, and upgrade npm to latest
	nodeenv --node=0.10.37 --prebuilt .node
	. .node/bin/activate
	npm install -g npm

	""" 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

	Pick a 2n-element periodic sequence of complex numbers. This is isomorphic to a polynomial p in
	C[x] / (x^2n - 1)
	(x^2n = 1 = x^0, which encodes the 2n-periodicity).

	Computing the 2n-element DFT of the original sequence is just evaluating p at the roots of unity
	(\omega_{2n}^0, \omega_{2n}^1, ..., \omega_{2n}^{2n+1}).

	Now, (x^2n - 1) = (x^n - 1) (x^n + 1). This implies that the given p mod (x^2n - 1) is uniquely
	determined by the remainders p mod (x^n - 1) and p mod (x^n + 1) [via CRT]. That is, there is
	an isomorphism between C[x] / (x^2n - 1) and (C[x] / (x^n - 1)) x (C[x] / (x^n + 1)).

	import numpy as np
	from numpy.linalg import norm, solve
	from scipy.spatial.distance import cdist
	from sklearn.neighbors import kneighbors_graph


	def phi(l, mu):
	return (mu * (np.sqrt(l) - 1)**2)