class MiniBatchKMeansImpl():
def __init__(self, n_clusters=8, init='k-means++', max_iter=100, batch_size=100, verbose=0, compute_labels=True, random_state=None, tol=0.0, max_no_improvement=10, init_size=None, n_init=3, reassignment_ratio=0.01):
self._hyperparams = {
'n_clusters': n_clusters,
'init': init,
'max_iter': max_iter,
'batch_size': batch_size,
'verbose': verbose,
'compute_labels': compute_labels,
'random_state': random_state,
'tol': tol,
'max_no_improvement': max_no_improvement,
'init_size': init_size,
'n_init': n_init,
'reassignment_ratio': reassignment_ratio}
self._wrapped_model = SKLModel(**self._hyperparams)
def fit(self, X, y=None):
if (y is not None):
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def transform(self, X):
return self._wrapped_model.transform(X)
def predict(self, X):
return self._wrapped_model.predict(X)
