Tiger Shen tigershen23

Thor::Util#snake_case: well-isolated test cases. Clear and thorough about expected behavior with potentially ambiguous input
HTTParty.normalize_base_uri: describes behavior well for a method that might be confusing at first
Virtus::Attribute#==: good use of intermediate variable for clarity. tests are collectively exhaustive within the scope of the method
AASM::Core::Event#fire: good isolation of complex behavior; good use of mocks and doubles. when/then/expect division is clear (BDD). good use of context
[Money.from_amount](https://gi

	import pymc3 as pm

	def posterior_upvote_ratio(upvotes, downvotes, samples = 20000):
	“””
	This function accepts the number of upvotes and downvotes a particular submission recieved,
	and the number of posterior samples to return to the user. Assumes a uniform prior.
	“””
	N = upvotes + downvotes
	with pm.Model() as model:
	upvote_ratio = pm.Uniform(“upvote_ratio”, 0, 1) # (1)

	import torch
	import pyro
	import pyro.distributions as dist
	from pyro.infer import SVI, Trace_ELBO
	from pyro.optim import Adam

	from __future__ import print_function
	import math
	import os
	import torch
	import torch.distributions.constraints as constraints
	import pyro
	from pyro.optim import Adam
	from pyro.infer import SVI, Trace_ELBO
	import pyro.distributions as dist

	def model(data):
	alpha = torch.tensor(6.0)
	beta = torch.tensor(10.0)
	pay_probs = pyro.sample('pay_probs', dist.Beta(alpha, beta).expand(3).independent(1))
	normalized_pay_probs = pay_probs / torch.sum(pay_probs)

	with pyro.iarange('data_loop', len(data)):
	pyro.sample('obs', dist.Categorical(probs=normalized_pay_probs), obs=data)

	def guide(data):
	alphas = pyro.param('alphas', torch.tensor(6.).expand(3), constraint=constraints.positive)
	betas = pyro.param('betas', torch.tensor(10.).expand(3), constraint=constraints.positive)

	pyro.sample('pay_probs', dist.Beta(alphas, betas).independent(1))

	# Tiger Jason James
	data = torch.cat((torch.zeros(9), torch.ones(7), torch.empty(4).fill_(2.)))

	def print_progress():
	alphas = pyro.param("alphas")
	betas = pyro.param("betas")

	if torch.cuda.is_available():
	alphas.cuda()
	betas.cuda()

	means = alphas / (alphas + betas)
	normalized_means = means / torch.sum(means)

	adam_params = {"lr": 0.0005}
	optimizer = Adam(adam_params)
	svi = SVI(model, guide, optimizer, loss=Trace_ELBO())

	n_steps = 2501
	for step in range(n_steps):
	svi.step(data)
	if step % 100 == 0:
	print_progress()

	pyro.enable_validation(True)
	pyro.clear_param_store()