def initialize(init, k, G, X):
"""Initializaton function to use in energy Lloyd and Hartigan."""
if init == "spectral":
z0 = initialization.topeigen(k, G)
Z0 = eclust.ztoZ(z0)
elif init == "k-means++":
z0 = initialization.kmeanspp(k, X)
Z0 = eclust.ztoZ(z0)
elif init == "random":
z0 = np.random.randint(0, k, len(X))
Z0 = eclust.ztoZ(z0)
else:
raise ValueError("No initialization method provided")
return Z0
def initialize(init, k, G, X):
"""Initializaton function to use in energy Lloyd and Hartigan."""
if init == "spectral":
z0 = initialization.topeigen(k, G)
Z0 = eclust.ztoZ(z0)
elif init == "k-means++":
z0 = initialization.kmeanspp(k, X)
Z0 = eclust.ztoZ(z0)
elif init == "random":
z0 = np.random.randint(0, k, len(X))
Z0 = eclust.ztoZ(z0)
else:
raise ValueError("No initialization method provided")
return Z0
def energy_hartigan(k, X, G, run_times=5, return_labels=False):
best_score = -np.inf
for rt in range(run_times):
z0 = initialization.kmeanspp(k, X)
Z0 = eclust.ztoZ(z0)
zh = eclust.energy_hartigan(k, G, Z0, max_iter=300)
Zh = eclust.ztoZ(zh)
score = eclust.objective(Zh, G)
if score > best_score:
best_score = score
best_zh = zh
if return_labels:
return best_zh
else:
return best_score
def energy_hartigan(k, X, G, run_times=5, return_labels=False):
best_score = -np.inf
for rt in range(run_times):
z0 = initialization.kmeanspp(k, X)
Z0 = eclust.ztoZ(z0)
zh = eclust.energy_hartigan(k, G, Z0, max_iter=300)
Zh = eclust.ztoZ(zh)
score = eclust.objective(Zh, G)
if score > best_score:
best_score = score
best_zh = zh
if return_labels:
return best_zh
else:
return best_score