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]
class_docstr_tok: ["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","]"]
docstr: Fit Gaussian Naive Bayes according to X, y
  
  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.
  
  sample_weight : array-like, shape (n_samples,), optional (default=None)
      Weights applied to individual samples (1. for unweighted).
  
      .. versionadded:: 0.17
         Gaussian Naive Bayes supports fitting with *sample_weight*.
  
  Returns
  -------
  self : object
      Returns self.
docstr_tok: ["Fit","Gaussian","Naive","Bayes","according","to","X",",","y","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",".","sample_weight",":","array-like",",","shape","(","n_samples",",",")",",","optional","(","default=None",")","Weights","applied","to","individual","samples","(","1.","for","unweighted",")",".","..","versionadded",":",":","0.17","Gaussian","Naive","Bayes","supports","fitting","with","*","sample_weight","*",".","Returns","--","--","--","-","self",":","object","Returns","self","."]
code: def fit(self, X, y, sample_weight=None):
      """Fit Gaussian Naive Bayes according to X, y
  
      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.
  
      sample_weight : array-like, shape (n_samples,), optional (default=None)
          Weights applied to individual samples (1. for unweighted).
  
          .. versionadded:: 0.17
             Gaussian Naive Bayes supports fitting with *sample_weight*.
  
      Returns
      -------
      self : object
          Returns self.
      """
      X, y = check_X_y(X, y)
      return self._partial_fit(
          X, y, np.unique(y), _refit=True, 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 2 for calling the GaussianNB obj's underlying member method code with example usage: obj.fit(X, y, sample_weight) and returns: self