def kernel(rid):
start0 = time.time()
assign(bound_s, r)
sec0 = time.time() - start0
start1 = time.time()
hp(bound_s, r, hparams=hparams)
sec1 = time.time() - start1
print 'rid=', rid, 'nclusters=', s.ngroups(), \
'iter0=', sec0, 'sec', 'iter1=', sec1, 'sec'
sec_per_post_pred = sec0 / (float(view.size()) * (float(s.ngroups())))
print ' time_per_post_pred=', sec_per_post_pred, 'sec'
# print group size breakdown
sizes = [(gid, s.groupsize(gid)) for gid in s.groups()]
sizes = sorted(sizes, key=lambda x: x[1], reverse=True)
print ' group_sizes=', sizes
print_prediction_results()
# save state
mkdirp("mnist-states")
fname = os.path.join("mnist-states", "state-iter{}.ser".format(rid))
with open(fname, "w") as fp:
fp.write(s.serialize())
def run(self, r, niters=10000):
"""Run the specified mixturemodel kernel for `niters`, in a single
thread.
Parameters
----------
r : random state
niters : int
"""
validator.validate_type(r, rng, param_name='r')
validator.validate_positive(niters, param_name='niters')
inds = xrange(len(self._defn.domains()))
models = [bind(self._latent, i, self._views) for i in inds]
for _ in xrange(niters):
for name, config in self._kernel_config:
if name == 'assign':
for idx in config.keys():
gibbs.assign(models[idx], r)
elif name == 'assign_resample':
for idx, v in config.iteritems():
gibbs.assign_resample(models[idx], v['m'], r)
elif name == 'slice_cluster_hp':
for idx, v in config.iteritems():
slice.hp(models[idx], r, cparam=v['cparam'])
elif name == 'grid_relation_hp':
gibbs.hp(models[0], config, r)
elif name == 'slice_relation_hp':
slice.hp(models[0], r, hparams=config['hparams'])
elif name == 'theta':
slice.theta(models[0], r, tparams=config['tparams'])
else:
assert False, "should not be reached"
def run(self, r, niters=10000):
"""Run the specified mixturemodel kernel for `niters`, in a single
thread.
Parameters
----------
r : random state
niters : int
"""
validator.validate_type(r, rng, param_name='r')
validator.validate_positive(niters, param_name='niters')
model = bind(self._latent, self._view)
for _ in xrange(niters):
for name, config in self._kernel_config:
if name == 'assign':
gibbs.assign(model, r)
elif name == 'assign_resample':
gibbs.assign_resample(model, config['m'], r)
elif name == 'grid_feature_hp':
gibbs.hp(model, config, r)
elif name == 'slice_feature_hp':
slice.hp(model, r, hparams=config['hparams'])
elif name == 'slice_cluster_hp':
slice.hp(model, r, cparam=config['cparam'])
elif name == 'theta':
slice.theta(model, r, tparams=config['tparams'])
else:
assert False, "should not be reach"
def kernel(rid):
start0 = time.time()
assign(bound_s, r)
sec0 = time.time() - start0
start1 = time.time()
hp(bound_s, r, hparams=hparams)
sec1 = time.time() - start1
print 'rid=', rid, 'nclusters=', s.ngroups(), \
'iter0=', sec0, 'sec', 'iter1=', sec1, 'sec'
sec_per_post_pred = sec0 / (float(view.size()) * (float(s.ngroups())))
print ' time_per_post_pred=', sec_per_post_pred, 'sec'
# print group size breakdown
sizes = [(gid, s.groupsize(gid)) for gid in s.groups()]
sizes = sorted(sizes, key=lambda x: x[1], reverse=True)
print ' group_sizes=', sizes
print_prediction_results()
# save state
mkdirp("mnist-states")
fname = os.path.join("mnist-states", "state-iter{}.ser".format(rid))
with open(fname, "w") as fp:
fp.write(s.serialize())
def kernel(rid):
start0 = time.time()
assign(bound_s, r)
sec0 = time.time() - start0
start1 = time.time()
hp(bound_s, r, hparams=hparams)
sec1 = time.time() - start1
print 'rid=', rid, 'nclusters=', s.ngroups(), \
'iter0=', sec0, 'sec', 'iter1=', sec1, 'sec'
sec_per_post_pred = sec0 / (float(view.size()) * (float(s.ngroups())))
print ' time_per_post_pred=', sec_per_post_pred, 'sec'
def kernel(rid):
start0 = time.time()
assign(bound_s, r)
sec0 = time.time() - start0
start1 = time.time()
hp(bound_s, r, hparams=hparams)
sec1 = time.time() - start1
print 'rid=', rid, 'nclusters=', s.ngroups(), \
'iter0=', sec0, 'sec', 'iter1=', sec1, 'sec'
sec_per_post_pred = sec0 / (float(view.size()) * (float(s.ngroups())))
print ' time_per_post_pred=', sec_per_post_pred, 'sec'