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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 |