def energy_hartigan(k, X, G, run_times=10, init="spectral"):
"""Run few times and pick the best objective function value."""
best_score = -np.inf
for rt in range(run_times):
Z0 = initialize(init, k, G, X)
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_z = zh
return best_z
def energy_hartigan(k, X, G, run_times=5, init="spectral"):
"""Run few times and pick the best objective function value."""
best_score = -np.inf
for rt in range(run_times):
Z0 = initialize(init, k, G, X)
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_z = zh
return best_z
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
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
def energy_hartigan(k, X, G, z, run_times=10, init="spectral"):
"""Run few times and pick the best objective function value."""
best_score = -np.inf
for rt in range(run_times):
Z0 = initialize(init, k, G, X)
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_z = zh
a = metric.accuracy(z, best_z)
v = metric.variation_information(z, best_z)
return a, v
def energy_lloyd(k, X, G, z, run_times=10, init="spectral"):
"""Run few times and pick the best objective function value."""
best_score = -np.inf
for rt in range(run_times):
Z0 = initialize(init, k, G, X)
zh = eclust.energy_lloyd(k, G, Z0, max_iter=300)
Zh = eclust.ztoZ(zh)
score = eclust.objective(Zh, G)
if score > best_score:
best_score = score
best_z = zh
a = metric.accuracy(z, best_z)
v = metric.variation_information(z, best_z)
return a, v
