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#Our batch generator - the bias allows us to control how much of the context is from genre and how much is from score
def gen_batch(genres, scores, size, bias):
    #Initialise the numpy arrays - the xs are the target words and the ys the inidividual contexts.
    xs = np.ndarray(shape=(size), dtype=np.int32)
    ys = np.ndarray(shape=(size, 1), dtype=np.int32)
    for idx in range(size):
        b = np.random.randint(10)
        if b < bias:
            genre = np.random.randint(len(genres))
            g1 = np.random.randint(len(genres[genre_lookup[genre]]))

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#Before we dive in we need to declare some variables
vocabulary_size = len(artist_lookup)
#How big we want our final vectors to be
embedding_size = 64
#The number of training samples passed per epoch
batch_size = 64
#Number of negative samples to use in NCE [see below]
num_sampled = 16

#BAND2VEC - Tensorflow Time!

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#Running our Neural Network!
#First we init the session
iterations = 100000

with tf.Session(graph=graph) as sess:
    #We run the initialize all global variables operation
    sess.run(tf.global_variables_initializer())
    average_loss = 0.0
    #For all of the iterations
    for index in range(iterations):

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try:
    from sklearn.manifold import TSNE
    import matplotlib.pyplot as plt

    tsne = TSNE(perplexity=30, n_components=2, verbose=1, init='pca', n_iter=500, method='exact')
    plot_only = len(artist_lookup)
    low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])

except ImportError as ex:
    print('Please install sklearn, matplotlib, and scipy to show embeddings.')

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function setup() {
  createCanvas(640, 640);
}

function draw() {
  //Updates, renders etc.
}

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class Planet {
  //The 'build' method
  constructor(position, mass, radius, color) {
    this.position = position;
    //A p5 Vector 
    this.velocity = createVector(0, 0);
    this.mass = mass;
    this.radius = radius;
    this.color = color;
  }

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class Satellite extends Planet {
  constructor(position, mass, radius, color, vel) {
    //Telling Planet to build a base planet for us
    super(position, mass, radius, color);
    //Redefining the velocity to be something we pass it (shadowing)
    this.velocity = vel;
    //Our new variable acceleration
    this.acceleration = createVector(0, 0);
  }
  

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function centripetalForce(satellite, centre) {
  //Get the direction of the force
  let force = p5.Vector.sub(centre, satellite.position).normalize();
  //Get the distance to centre of rotation from our satellite 
  let radius = p5.Vector.sub(centre, satellite.position).mag();
  //The velocity squared
  let velocitySquared = p5.Vector.dot(satellite.velocity, satellite.velocity);
  //Pull it all together with the f = (m * v^2) / r equation
  force.mult(velocitySquared);
  force.mult(satellite.mass);

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//Our global variables
let earth;
let moon;
let r = 225;

function setup() {
  createCanvas(640, 640);
  //Creating our Earth and Moon
  earth = new Planet(createVector(width/2, height/2), 1000, 100, 'blue');
  //Notice we're starting the moon directly 'east' to our Earth, so the initial velocity is straight upwards.

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import requests
from bs4 import BeautifulSoup
import json

#Accessing the Webpage
result = requests.get('https://github.com/factbook/factbook.json')

#Getting the content of the webpage
content = result.content