loretoparisi

const spinner = [ '⠋', '⠙', '⠹', '⠸', '⠼', '⠴', '⠦', '⠧', '⠇', '⠏' ];
for(var count=0;count<100000;count++) process.stdout.write(" "+spinner[  (count+1)% spinner.length ]+"\033[0G" ); // \033[0G | \r | \x1B[0G

loretoparisi

function deepClone(obj) {
  var copy;
  var i;
  var len;

  if (!obj || typeof obj !== 'object') {
      return obj;
  }

  if (obj instanceof Array) {

jovianlin

from tensorflow.python.client import device_lib

def get_available_gpus():
    local_device_protos = device_lib.list_local_devices()
    return [x.name for x in local_device_protos if x.device_type == 'GPU']

get_available_gpus()

romcaname

function standardDeviation(values, avg) {
    var squareDiffs = values.map(value => Math.pow(value - avg, 2));
    return Math.sqrt(average(squareDiffs));
}

function average(data) {
    return data.reduce((sum, value)=>sum + value) / data.length;
}

if (responseCode.code === 200 || responseCode.code === 201) {

loretoparisi

/**
 * Phantomjs example wait For Page Document condition
 * @author Loreto Parisi (loretoparisi at gmail dot com )
 */
(function() {

/**
 * Delayed wait on a condition
 */
var Delay = function() {

hagerty

#Generator
with tf.device(gpu):
    x8 = tf.placeholder(tf.float32, shape=[None, FLAGS.ws, FLAGS.ws, 8]) # 8-band input
    x3 = tf.placeholder(tf.float32, shape=[None, scale * FLAGS.ws, scale * FLAGS.ws, 3]) # 3-band ipnput
    label_distance = tf.placeholder(tf.float32, shape=[None, FLAGS.ws, FLAGS.ws, 1]) # distance transform as a label

    for i in range(layers):
        alpha[i] = tf.Variable(0.9, name='alpha_' + str(i))
        beta[i] = tf.maximum( 0.0 , tf.minimum ( 1.0 , alpha[i] ), name='beta_'+str(i))
        bi[i] = tf.Variable(tf.constant(0.0,shape=[FLAGS.filters]), name='bi_'+str(i))

loretoparisi

 /********************
   * Object modifiers
   ********************/

  /**
   * Object.mapObject(value,key)
   * Map Object properties
   * @return object Copy of this object
   */
  if( typeof( Object.mapObject ) == 'undefined') {

quadrismegistus

def smart_procrustes_align_gensim(base_embed, other_embed, words=None):
	"""Procrustes align two gensim word2vec models (to allow for comparison between same word across models).
	Code ported from HistWords <https://github.com/williamleif/histwords> by William Hamilton <[email protected]>.
		(With help from William. Thank you!)

	First, intersect the vocabularies (see `intersection_align_gensim` documentation).
	Then do the alignment on the other_embed model.
	Replace the other_embed model's syn0 and syn0norm numpy matrices with the aligned version.
	Return other_embed.

loretoparisi

#!/bin/bash
netstat -atn | grep LISTEN  | grep "127.0.0.1"

loretoparisi

function(n, ps) {
  const DUMMY = -1;
   var rs = [];                  // rs = round array
    if (!ps) {
      ps = [];
      for (var k = 1; k <= n; k += 1) {
        ps.push(k);
      }
    } else {
      ps = ps.slice();