josejimenezluna

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)

rygorous

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)).

shagunsodhani

Key-Value Memory Networks for Directly Reading Documents

Introduction

• 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.

Related Work

karpathy

""" 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

kylemcdonald

alertedsnake

# 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

yomotsu

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 );

patriciogonzalezvivo

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);

adaugherity

#!/usr/bin/ruby
# Create display override file to force Mac OS X to use RGB mode for Display
# see http://embdev.net/topic/284710

require 'base64'

data=`ioreg -l -d0 -w 0 -r -c AppleDisplay`

edids=data.scan(/IODisplayEDID.*?<([a-z0-9]+)>/i).flatten
vendorids=data.scan(/DisplayVendorID.*?([0-9]+)/i).flatten

JackNova

//BASIC PIECES, 3 functions: unit, bind and the bind argument
//function unit(value)
//function bind(monad, function(value))
//all three functions return a monad

/* The unit function is a constructor (returns a monad object) 
* The magic is in the bind function
* 
* There are AXIOMS:
* bind(unit(value)), f) === f(value)