def mfm(sampler=hybrid, offset=0, gamma=1):
return {
"sampler":
sampler,
"mixture":
lambda nc: bmcc.MFM(gamma=gamma,
prior=lambda k: poisson.logpmf(k, nc + offset))
}
d=3,
r=0.7,
alpha=40,
df=3,
symmetric=False,
shuffle=False)
def hybrid(*args, **kwargs):
for _ in range(5):
bmcc.gibbs(*args, **kwargs)
bmcc.split_merge(*args, **kwargs)
mm = bmcc.MFM(gamma=1, prior=lambda k: poisson.logpmf(k, 3))
# mm = bmcc.DPM(alpha=1, use_eb=False)
cm = bmcc.NormalWishart(df=3)
# Create mixture model
model = bmcc.BayesianMixture(data=dataset.data,
sampler=hybrid,
component_model=cm,
mixture_model=mm,
assignments=np.zeros(POINTS).astype(np.uint16),
thinning=THINNING)
# Run Iterations
start = time.time()
for i in tqdm(range(ITERATIONS)):
model.iter()
# Create dataset
dataset = bmcc.GaussianMixture(n=1000,
k=4,
d=3,
r=0.7,
alpha=40,
df=3,
symmetric=False,
shuffle=False)
# Create mixture models
model_mfm = bmcc.GibbsMixtureModel(
data=dataset.data,
component_model=bmcc.NormalWishart(df=3),
mixture_model=bmcc.MFM(gamma=1, prior=lambda k: poisson.logpmf(k, 4)),
assignments=np.zeros(1000).astype(np.uint16),
thinning=5)
model_dpm = bmcc.GibbsMixtureModel(
data=dataset.data,
component_model=bmcc.NormalWishart(df=3),
mixture_model=bmcc.DPM(alpha=1, use_eb=True),
assignments=np.zeros(1000).astype(np.uint16),
thinning=5)
# Run Iterations
print("MFM:")
for i in tqdm(range(5000)):
model_mfm.iter()
print("DPM:")
for i in tqdm(range(5000)):