class_docstr: Gaussian Naive Bayes (GaussianNB)
  
  Can perform online updates to model parameters via `partial_fit` method.
  For details on algorithm used to update feature means and variance online,
  see Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque:
  
      http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf
  
  Read more in the :ref:`User Guide <gaussian_naive_bayes>`.
  
  Parameters
  ----------
  priors : array-like, shape (n_classes,)
      Prior probabilities of the classes. If specified the priors are not
      adjusted according to the data.
  
  Attributes
  ----------
  class_prior_ : array, shape (n_classes,)
      probability of each class.
  
  class_count_ : array, shape (n_classes,)
      number of training samples observed in each class.
  
  theta_ : array, shape (n_classes, n_features)
      mean of each feature per class
  
  sigma_ : array, shape (n_classes, n_features)
      variance of each feature per class
  
  Examples
  --------
  >>> import numpy as np
  >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
  >>> Y = np.array([1, 1, 1, 2, 2, 2])
  >>> from sklearn.naive_bayes import GaussianNB
  >>> clf = GaussianNB()
  >>> clf.fit(X, Y)
  GaussianNB(priors=None)
  >>> print(clf.predict([[-0.8, -1]]))
  [1]
  >>> clf_pf = GaussianNB()
  >>> clf_pf.partial_fit(X, Y, np.unique(Y))
  GaussianNB(priors=None)
  >>> print(clf_pf.predict([[-0.8, -1]]))
  [1]
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docstr: 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.
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code: def partial_fit(self, X, y, classes=None, sample_weight=None):
      """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.
      """
      return self._partial_fit(
          X, y, classes, _refit=False, sample_weight=sample_weight
      )
  
imports: from abc import ABCMeta, abstractmethod
  import numpy
  from scipy.sparse import issparse
  from .base import BaseEstimator, ClassifierMixin
  from .preprocessing import binarize
  from .preprocessing import LabelBinarizer
  from .preprocessing import label_binarize
  from .utils import check_X_y, check_array
  from .utils.extmath import safe_sparse_dot, logsumexp
  from .utils.multiclass import _check_partial_fit_first_call
  from .utils.fixes import in1d
  from .utils.validation import check_is_fitted
  from .externals import six
label_desc: Use image node_id 4 for calling the GaussianNB obj's underlying member method code with example usage: obj.partial_fit(X, y, classes, sample_weight) and returns: self