splearn/naive_bayes.py
def fit(self, Z, classes=None):
        """
        TODO fulibacsi fix docstring
        Fit Multinomial Naive Bayes according to (X,y) pair
        which is zipped into TupleRDD Z.

        Parameters
        ----------
        Z : TupleRDD containing X [array-like, shape (m_samples, n_features)]
            and y [array-like, shape (m_samples,)] tuple
            Training vectors, where ,_samples is the number of samples in the
            block and n_features is the number of features, and y contains
            the target values.

        Returns
        -------
        self : object
            Returns self.
        """
        check_rdd(Z, {'X': (sp.spmatrix, np.ndarray), 'y': (sp.spmatrix, np.ndarray)})
        if 'w' in Z.columns:
            models = Z[:, ['X', 'y', 'w']].map(
                lambda X_y_w: self.partial_fit(
                    X_y_w[0], X_y_w[1], classes, X_y_w[2]
                )
            )
        else:
            models = Z[:, ['X', 'y']].map(
                lambda X_y: self.partial_fit(X_y[0], X_y[1], classes))
        avg = models.sum()
        self.__dict__.update(avg.__dict__)
        return self
def fit(self, Z, classes=None):
        """
        TODO fulibacsi fix docstring
        Fit Multinomial Naive Bayes according to (X,y) pair
        which is zipped into TupleRDD Z.

        Parameters
        ----------
        Z : TupleRDD containing X [array-like, shape (m_samples, n_features)]
            and y [array-like, shape (m_samples,)] tuple
            Training vectors, where ,_samples is the number of samples in the
            block and n_features is the number of features, and y contains
            the target values.

        Returns
        -------
        self : object
            Returns self.
        """
        check_rdd(Z, {'X': (sp.spmatrix, np.ndarray), 'y': (sp.spmatrix, np.ndarray)})
        if 'w' in Z.columns:
            models = Z[:, ['X', 'y', 'w']].map(
                lambda X_y_w: self.partial_fit(
                    X_y_w[0], X_y_w[1], classes, X_y_w[2]
                )
            )
        else:
            models = Z[:, ['X', 'y']].map(
                lambda X_y: self.partial_fit(X_y[0], X_y[1], classes))
        avg = models.sum()
        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 ) } ) if 'w' in Z . columns : models = Z [ : , [ 'X' , 'y' , 'w' ] ] . map ( lambda X_y_w : self . partial_fit ( X_y_w [ 0 ] , X_y_w [ 1 ] , classes , X_y_w [ 2 ] ) ) else : models = Z [ : , [ 'X' , 'y' ] ] . map ( lambda X_y : self . partial_fit ( X_y [ 0 ] , X_y [ 1 ] , classes ) ) avg = models . sum ( ) self . __dict__ . update ( avg . __dict__ ) return self
TODO fulibacsi fix docstring
        Fit Multinomial Naive Bayes according to (X,y) pair
        which is zipped into TupleRDD Z.

        Parameters
        ----------
        Z : TupleRDD containing X [array-like, shape (m_samples, n_features)]
            and y [array-like, shape (m_samples,)] tuple
            Training vectors, where ,_samples is the number of samples in the
            block and n_features is the number of features, and y contains
            the target values.

        Returns
        -------
        self : object
            Returns self.
TODO fulibacsi fix docstring Fit Multinomial Naive Bayes according to ( X y ) pair which is zipped into TupleRDD Z .