splearn/linear_model/stochastic_gradient.py
def fit(self, Z, classes=None):
        """Fit the model according to the given training data.

        Parameters
        ----------
        Z : DictRDD containing (X, y) pairs
            X - Training vector
            y - Target labels
        classes : iterable
            The set of available classes

        Returns
        -------
        self : object
            Returns self.
        """
        check_rdd(Z, {'X': (sp.spmatrix, np.ndarray)})
        self._classes_ = np.unique(classes)
        return self._spark_fit(SparkSGDClassifier, Z)
def fit(self, Z, classes=None):
        """Fit the model according to the given training data.

        Parameters
        ----------
        Z : DictRDD containing (X, y) pairs
            X - Training vector
            y - Target labels
        classes : iterable
            The set of available classes

        Returns
        -------
        self : object
            Returns self.
        """
        check_rdd(Z, {'X': (sp.spmatrix, np.ndarray)})
        self._classes_ = np.unique(classes)
        return self._spark_fit(SparkSGDClassifier, Z)
def fit ( self , Z , classes = None ) : check_rdd ( Z , { 'X' : ( sp . spmatrix , np . ndarray ) } ) self . _classes_ = np . unique ( classes ) return self . _spark_fit ( SparkSGDClassifier , Z )
Fit the model according to the given training data.

        Parameters
        ----------
        Z : DictRDD containing (X, y) pairs
            X - Training vector
            y - Target labels
        classes : iterable
            The set of available classes

        Returns
        -------
        self : object
            Returns self.
Fit the model according to the given training data .