def predict(self, X, exposure=None):
        """
        preduct expected value of target given model and input X
        often this is done via expected value of GAM given input X

        Parameters
        ---------
        X : array-like of shape (n_samples, m_features), default: None
            containing the input dataset

        exposure : array-like shape (n_samples,) or None, default: None
            containing exposures
            if None, defaults to array of ones

        Returns
        -------
        y : np.array of shape (n_samples,)
            containing predicted values under the model
        """
        if not self._is_fitted:
            raise AttributeError('GAM has not been fitted. Call fit first.')

        X = check_X(X, n_feats=self.statistics_['m_features'],
                    edge_knots=self.edge_knots_, dtypes=self.dtype,
                    features=self.feature, verbose=self.verbose)

        if exposure is not None:
            exposure = np.array(exposure).astype('f')
        else:
            exposure = np.ones(X.shape[0]).astype('f')
        check_lengths(X, exposure)

        return self.predict_mu(X) * exposure
def predict ( self , X , exposure = None ) : if not self . _is_fitted : raise AttributeError ( 'GAM has not been fitted. Call fit first.' ) X = check_X ( X , n_feats = self . statistics_ [ 'm_features' ] , edge_knots = self . edge_knots_ , dtypes = self . dtype , features = self . feature , verbose = self . verbose ) if exposure is not None : exposure = np . array ( exposure ) . astype ( 'f' ) else : exposure = np . ones ( X . shape [ 0 ] ) . astype ( 'f' ) check_lengths ( X , exposure ) return self . predict_mu ( X ) * exposure
preduct expected value of target given model and input X
        often this is done via expected value of GAM given input X

        Parameters
        ---------
        X : array-like of shape (n_samples, m_features), default: None
            containing the input dataset

        exposure : array-like shape (n_samples,) or None, default: None
            containing exposures
            if None, defaults to array of ones

        Returns
        -------
        y : np.array of shape (n_samples,)
            containing predicted values under the model