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'