template = sampleTemplate(groundgraph, numtemplate)
if 'WORKHASH' in os.environ:
jobhash = os.environ['WORKHASH']
if not r.hexists('jobs:grounds', jobhash):
r.hset('jobs:grounds', jobhash,
zlib.compress(cPickle.dumps(groundgraph)))
random.seed()
np.random.seed()
datasizes = [4, 16, 32, 64, 128, 256]
temps = [1.0, 1.0, 2.0, 2.0, 5.0, 5.0]
for temperature, numdata in zip(temps, datasizes):
data = generateData(groundgraph, joint, numdata)
groundbnet = BayesNetCPD(states, data, limparent=3)
groundbnet.set_cpds(joint)
obj = BayesNetCPD(states, data, limparent=3)
b = BayesNetSampler(obj, template, groundbnet, priorweight)
s = SAMCRun(b, burn, stepscale, refden, thin)
s.sample(iters, temperature)
s.compute_means()
if 'WORKHASH' in os.environ:
r.lpush('jobs:done:' + jobhash, s.read_db())
r.lpush('custom:%s:samplesize=%d' % (jobhash, numdata),
s.db.root.computed.means._v_attrs['kld'])
s.db.close()
burn = 1
stepscale=10
temperature = 1.0
thin = 1
refden = 0.0
random.seed(123456)
np.random.seed(123456)
groundgraph = generateTree(N, comps)
data = generateData(groundgraph,numdata)
#template = sampleTemplate(groundgraph, numtemplate)
random.seed()
np.random.seed()
ground = TreeNet(N, graph=groundgraph)
b = TreeNet(N, data=data, ground=ground)
s = SAMCRun(b,burn,stepscale,refden,thin,verbose=True)
s.sample(iters, temperature)
s.compute_means()
# All to exercise cde deps
tmp = s.read_db()
import cPickle
txt = zlib.compress(cPickle.dumps([1,2,3]))
if 'WORKHASH' in os.environ:
r.lpush('jobs:done:'+os.environ['WORKHASH'], s.read_db())