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_spectral(k, X, G, run_times=5, init="random"):
"""Run few times and pick the best objective function value.
Choose the initializatio for k-means, which can be k-means++ or random.
"""
best_score = -np.inf
for rt in range(run_times):
zh = initialization.topeigen(k, G, run_times=run_times, init="random")
Zh = eclust.ztoZ(zh)
score = eclust.objective(Zh, G)
if score > best_score:
best_score = score
best_z = zh
return best_z
def energy_spectral(k, X, G, run_times=10, init="random"):
"""Run few times and pick the best objective function value.
Choose the initializatio for k-means, which can be k-means++ or random.
"""
best_score = -np.inf
for rt in range(run_times):
zh = initialization.topeigen(k, G, run_times=run_times, init="random")
Zh = eclust.ztoZ(zh)
score = eclust.objective(Zh, G)
if score > best_score:
best_score = score
best_z = zh
return best_z
