splearn/naive_bayes.py
def fit(self, Z, classes=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.

        Returns
        -------
        self : object
            Returns self.
        """
        check_rdd(Z, {'X': (sp.spmatrix, np.ndarray), 'y': (sp.spmatrix, np.ndarray)})
        models = Z[:, ['X', 'y']].map(
            lambda X_y: self.partial_fit(X_y[0], X_y[1], classes))
        avg = models.reduce(operator.add)
        self.__dict__.update(avg.__dict__)
        return self
def fit(self, Z, classes=None):
        check_rdd(Z, {'X': (sp.spmatrix, np.ndarray), 'y': (sp.spmatrix, np.ndarray)})
        models = Z[:, ['X', 'y']].map(
            lambda X_y: self.partial_fit(X_y[0], X_y[1], classes))
        avg = models.reduce(operator.add)
        self.__dict__.update(avg.__dict__)
        return self
def fit ( self , Z , classes = None ) : check_rdd ( Z , { 'X' : ( sp . spmatrix , np . ndarray ) , 'y' : ( sp . spmatrix , np . ndarray ) } ) models = Z [ : , [ 'X' , 'y' ] ] . map ( lambda X_y : self . partial_fit ( X_y [ 0 ] , X_y [ 1 ] , classes ) ) avg = models . reduce ( operator . add ) self . __dict__ . update ( avg . __dict__ ) return self
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.

        Returns
        -------
        self : object
            Returns self.
Fit Gaussian Naive Bayes according to X y