'Incremental fit on a batch of samples.
This method is expected to be called several times consecutively
on different chunks of a dataset so as to implement out-of-core
or online learning.
This is especially useful when the whole dataset is too big to fit in
memory at once.
This method has some performance and numerical stability overhead,
hence it is better to call partial_fit on chunks of data that are
as large as possible (as long as fitting in the memory budget) to
hide the overhead.
Parameters
X : array-like, shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape (n_samples,)
Target values.
classes : array-like, shape (n_classes,), optional (default=None)
List of all the classes that can possibly appear in the y vector.
Must be provided at the first call to partial_fit, can be omitted
in subsequent calls.
sample_weight : array-like, shape (n_samples,), optional (default=None)
Weights applied to individual samples (1. for unweighted).
.. versionadded:: 0.17
Returns
self : object
Returns self.'

def partial_fit(self, X, y, classes=None, sample_weight=None):

    return self._partial_fit(X, y, classes, _refit=False, sample_weight=sample_weight)
