def partial_fit(self, X, y=None):
"""Update k means estimate on a single mini-batch X.
Parameters
----------
X : array-like, shape = [n_samples, rows, cols, channels]
Coordinates of the data points to cluster.
y : Ignored
"""
X = check_image_array(self, X)
return self._partial_fit(X, y)
def fit(self, X, y=None):
"""Compute the centroids on X by chunking it into mini-batches.
Parameters
----------
X : array-like or sparse matrix, shape = [n_samples, rows, cols, channels]
Training instances to cluster.
y : Ignored
"""
X = check_image_array(self, X)
return self._fit(X, y)
def predict(self, X):
"""Predict the closest cluster each sample in X belongs to.
In the vector quantization literature, `cluster_centers_` is called
the code book and each value returned by `predict` is the index of
the closest code in the code book.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, rows, cols, channels]
New data to predict.
Returns
-------
labels : array, shape [n_samples,]
Index of the cluster each sample belongs to.
"""
check_is_fitted(self, "cluster_centers_")
X = check_image_array(self, X)
return self._predict(X)