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