allenwhale

reviewerID-itemID,prediction
U938994110-I529819131,1
U181459539-I863471064,0
U941668816-I684585522,0
U768449391-I782253949,0
U640450168-I232683472,1
U087574132-I014281144,1
U885457860-I600866492,0
U319023404-I050733439,0
U535965656-I929867818,1

HugsLibRecordKeeper

Log uploaded on Tuesday, November 13, 2018, 6:15:36 PM
Loaded mods:
Core: (no assemblies)
Animal Tab: 0Harmony(1.2.0.1), AnimalTab(av:3.0.0.0,fv:3.4.123)
Blueprints: Blueprints(av:2.0.0.0,fv:2.2.39)
ED-Embrasures: 0Harmony(1.2.0.1), ED-Embrasures(1.0.0.0)
Expanded Prosthetics and Organ Engineering: (no assemblies)
Extended Turrets: 0Harmony(1.1.0.0), LaserBeam(1.0.0.0)
Hospitality: $HugsLibChecker(0.5.0.0)
HugsLib[ov:6.1.1]: 0Harmony(1.2.0.1), HugsLib(av:1.0.0.0,fv:6.1.1)

marceloreichert

class HelloWorld:

    def __init__(self, name):
        self.name = name.capitalize()
       
    def sayHi(self):
        print "Hello " + self.name + "!"

hello = HelloWorld("world")
hello.sayHi()

Ajax12345

{
  "$schema": "https://vega.github.io/schema/vega-lite/v3.json",
  "description": "The total number of votes each candidate got in the 2016 presidential primary.",
  "width": 600,
  "height": 600,
  "padding": 5,
  "autosize": "none",
  "data": {
    "values": [
      {"color": "red", "Candidate": "Ben Carson", "Total Votes": 564553},  

danielchiaradia

/build

DevTarakanova

// Callback function to insert 'styleselect' into the $buttons array
function my_mce_buttons_2( $buttons ) {
    array_unshift( $buttons, 'styleselect' );
    return $buttons;
}
// Register our callback to the appropriate filter
add_filter( 'mce_buttons_2', 'my_mce_buttons_2' );

zafar-saleem

class Todo {

  constructor() {
    let self = this;

    this.list = document.querySelector('.list-items');
    this.render();

    document.querySelector('.btn-add-item').addEventListener('click', this.create.bind(this));
    document.querySelector('.btn-update').addEventListener('click', this.update.bind(this));

MatthewTurk

mcbarlowe

#Here we draw a 10000 sample from the poisson distributions for home and away
#teams using the lambdas calculated earlier for regulation goals scored
    home_reg_goals = np.random.poisson(home_lambda, 10000)
    away_reg_goals = np.random.poisson(away_lambda, 10000)

#now I looop through both results and compare them. If the home team goals are
#larger than the away then then I append a one else a zero. If they are tied
#then it goes to an OT and if needed Shootout scenario that are binomial
#distributions decided by home OT and Shootout probability determined by a
#Bradley-Terry Model

marceloreichert

class HelloWorld:

    def __init__(self, name):
        self.name = name.capitalize()
       
    def sayHi(self):
        print "Hello " + self.name + "!"

hello = HelloWorld("world")
hello.sayHi()