impute.py 文件源码

def _impute2D(self, X_2D):
        r"""Mean impute a rank 2 tensor."""
        # Fill zeros in for missing data initially
        data_zeroed_missing_tf = X_2D * self.real_val_mask

        # Sum the real values in each column
        col_tot = tf.reduce_sum(data_zeroed_missing_tf, 0)

        # Divide column totals by the number of non-nan values
        num_values_col = tf.reduce_sum(self.real_val_mask, 0)
        num_values_col = tf.maximum(num_values_col,
                                    tf.ones(tf.shape(num_values_col)))
        col_nan_means = tf.div(col_tot, num_values_col)

        # Make an vector of the impute values for each missing point
        imputed_vals = tf.gather(col_nan_means, self.missing_ind[:, 1])

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

        X_with_impute = data_zeroed_missing_tf + missing_imputed

        return X_with_impute