(self, x, who=None, metadata=None)
:param x: tuple
            (x, [y[, w]) floats for single sample. Include
:param x: tuple
            (x, [y[, w]) floats for single sample. Include
: param x : tuple ( x [ y [ w ] ) floats for single sample . Include
def update(self, x, who=None, metadata=None):
        """
        :param x: tuple
            (x, [y[, w]) floats for single sample. Include
        """
        self.model.learn_one(*x)
        if self.metric:
            yp = self.model.predict_one(x[0])
            weights = x[2] if len(x) > 1 else 1.0
            return self._emit(self.metric.update(x[1], yp, weights).get(), metadata=metadata)
        if self.pass_model:
            return self._emit(self.model, metadata=metadata)
def update ( self , x , who = None , metadata = None ) : self . model . learn_one ( * x ) if self . metric : yp = self . model . predict_one ( x [ 0 ] ) weights = x [ 2 ] if len ( x ) > 1 else 1.0 return self . _emit ( self . metric . update ( x [ 1 ] , yp , weights ) . get ( ) , metadata = metadata ) if self . pass_model : return self . _emit ( self . model , metadata = metadata )