impute.py 文件源码

def _impute2D(self, X_2D):
        r"""Impute a rank 2 tensor with draws from normal distributions.

        Parameters
        ----------
        X_2D : Tensor
            a rank 2 Tensor with missing data

        Returns
        -------
        X_imputed : Tensor
            a rank 2 Tensor with imputed data

        """
        # Fill zeros in for missing data initially
        data_zeroed_missing = X_2D * self.real_val_mask

        # Divide column totals by the number of non-nan values
        col_draws = tf.transpose(self.normal.sample(seed=next(seedgen)))
        # Make an vector of the impute values for each missing point
        imputed_vals = tf.gather(col_draws, self.missing_ind[:, 1])[:, 0]

        # Fill the imputed values into the data tensor of zeros
        shape = tf.cast(tf.shape(data_zeroed_missing), dtype=tf.int64)
        missing_imputed = tf.scatter_nd(self.missing_ind, imputed_vals, shape)

        X_with_impute = data_zeroed_missing + missing_imputed

        return X_with_impute