Advertisement
Not a member of Pastebin yet?
Sign Up,
it unlocks many cool features!
- def run_gibbs(nsweeps=1):
- xs = get_dataset()
- xs, ys = xs[:1,:], xs[2,:]
- # xs is Shape([1000,2])
- # ys is Shape([1000,1]) of cluster labels
- prior = [Normal(-1, 0.5), Normal(1, 0.5)]
- for sweep in range(0, nsweeps):
- postieror = gibbs(prior, xs)
- prior = posterior # do something with that posterior?
- def gibbs(prior, data):
- chain = [] # used for updating later?
- assert len(prior) == 2, "just assume 2 features in the data with 2 variables that correspond."
- # ...do a more general case later
- for xy in data:
- old_x, old_y = xy
- x_prior = prior[0]
- new_x = x_prior.sample()
- new_cond_old = None # find conditional of x|new_x?
- chain.append(("x", old_x, new_x, new_cond_old)) # for an update later?
- # repeat in y
- posterior = prior
- return posterior
Advertisement
Add Comment
Please, Sign In to add comment
Advertisement