(self, row, attr=None, values=None)
return zip(values, map(float, pred_Y))
predict_pp generates posterior probabilities for the domain values
        corresponding to the cell/random variable row['_vid_'].

        That is: :param`attr` and :param`values` are ignored.

        predict_pp_batch is much faster for Logistic since it simply does
        a one-pass of the batch feature tensor.

        :return: (list[2-tuple]) 2-tuples corresponding to (value, proba)
predict_pp generates posterior probabilities for the domain values
        corresponding to the cell/random variable row['_vid_'].
predict_pp generates posterior probabilities for the domain values corresponding to the cell / random variable row [ _vid_ ] .
def predict_pp(self, row, attr=None, values=None):
        """
        predict_pp generates posterior probabilities for the domain values
        corresponding to the cell/random variable row['_vid_'].

        That is: :param`attr` and :param`values` are ignored.

        predict_pp_batch is much faster for Logistic since it simply does
        a one-pass of the batch feature tensor.

        :return: (list[2-tuple]) 2-tuples corresponding to (value, proba)
        """
        start_idx, end_idx = self.vid_to_idxs[row['_vid_']]
        pred_X = self._X[start_idx:end_idx]
        pred_Y = self.forward(pred_X)
        values = self.domain_records[row['_vid_']]['domain'].split('|||')
        return zip(values, map(float, pred_Y))
def predict_pp ( self , row , attr = None , values = None ) : start_idx , end_idx = self . vid_to_idxs [ row [ '_vid_' ] ] pred_X = self . _X [ start_idx : end_idx ] pred_Y = self . forward ( pred_X ) values = self . domain_records [ row [ '_vid_' ] ] [ 'domain' ] . split ( '|||' ) return zip ( values , map ( float , pred_Y ) )