def main():
# Data parameters
D = 1 # dimensions
N = 100 # number of points to generate
K_true = 4 # the true number of components
# Model parameters
alpha = 1.
K = 3 # initial number of components
n_iter = 40
# Generate data
mu_scale = 4.0
covar_scale = 0.7
z_true = np.random.randint(0, K_true, N)
logging.info("true clustering: {}".format(collections.Counter(z_true)))
mu = np.random.randn(D, K_true) * mu_scale
X = mu[:, z_true] + np.random.randn(D, N) * covar_scale
X = X.T
# Intialize prior
m_0 = np.zeros(D)
k_0 = covar_scale**2 / mu_scale**2
v_0 = D + 3
S_0 = covar_scale**2 * v_0 * np.eye(D)
prior = NIW(m_0, k_0, v_0, S_0)
# Setup PCRPMM
pcrpmm = PCRPMM(X, prior, alpha, save_path=None, assignments="rand", K=K)
# pcrpmm = PCRPMM(X, prior, alpha, save_path=None, assignments="one-by-one", K=K)
# Perform collapsed Gibbs sampling
record_dict, distribution_dict = pcrpmm.collapsed_gibbs_sampler(
n_iter, z_true, n_power=1.01, num_saved=K_true)
def main():
# Data parameters
D = 2 # dimensions
N = 100 # number of points to generate
K_true = 4 # the true number of components
# Model parameters
alpha = 1.
K = 3 # initial number of components
n_iter = 40
# Generate data
mu_scale = 4.0
covar_scale = 0.7
z_true = np.random.randint(0, K_true, N)
logging.info("true clustering: {}".format(collections.Counter(z_true)))
mu = np.random.randn(D, K_true) * mu_scale
X = mu[:, z_true] + np.random.randn(D, N) * covar_scale
X = X.T
# Intialize prior
m_0 = np.zeros(D)
k_0 = covar_scale**2 / mu_scale**2
v_0 = D + 3
S_0 = covar_scale**2 * v_0 * np.eye(D)
prior = NIW(m_0, k_0, v_0, S_0)
## setup save path
save_path = os.path.dirname(__file__) + '/tmp_res/'
try:
os.stat(save_path)
except:
os.mkdir(save_path)
# Setup CRPMM
crpmm = CRPMM(X,
prior,
alpha,
save_path=save_path,
assignments="rand",
K=K)
# crpmmmm = CRPMM(X, prior, alpha, save_path=save_path, assignments="one-by-one", K=K)
# Perform collapsed Gibbs sampling
record_dict = crpmm.collapsed_gibbs_sampler(n_iter, z_true)
# Plot results
fig = plt.figure()
ax = fig.add_subplot(111)
plot_mixture_model(ax, crpmm)
for k in xrange(crpmm.components.K):
mu, sigma = crpmm.components.rand_k(k)
plot_ellipse(ax, mu, sigma)
plt.show()
def main():
# Data parameters
D = 1 # dimensions
N = 100 # number of points to generate
K_true = 4 # the true number of components
# Model parameters
alpha = 1.
K = 4 # initial number of components
n_iter = 40 ## number of iteration, change it to larger value
# Generate data
mu_scale = 4.0
covar_scale = 0.7
z_true = np.random.randint(0, K_true, N)
logging.info("true clustering: {}".format(collections.Counter(z_true)))
mu = np.random.randn(D, K_true) * mu_scale
X = mu[:, z_true] + np.random.randn(D, N) * covar_scale
X = X.T
# Intialize prior
m_0 = np.zeros(D)
k_0 = covar_scale**2 / mu_scale**2
v_0 = D + 3
S_0 = covar_scale**2 * v_0 * np.eye(D)
prior = NIW(m_0, k_0, v_0, S_0)
# Setup CRPMM
igmm = CSCRPMM(X, prior, alpha, save_path=None, assignments="rand", K=K)
# igmm = CRPMM(X, prior, alpha, save_path=None, assignments="one-by-one", K=K)
# Perform collapsed Gibbs sampling
#igmm.collapsed_gibbs_sampler(n_iter, z_true)
igmm.constrained_gibbs_sample(n_iter,
z_true,
flag_constrain=True,
n_constrain=2,
thres=0.04,
num_saved=4)