serving.py 文件源码

def logprob(self, rows, evidence=None):
        """Compute non-normalized log probabilies of many rows of data.

        If evidence is specified, compute conditional log probability;
        otherwise compute unconditional log probability.

        Args:
            data: A list of rows of data, where each row is a sparse dict
                mapping feature name to feature value.
            evidence: An optional row of conditioning data, as a sparse dict
                mapping feature name to feature value.

        Returns:
            An [len(rows)]-shaped numpy array of log probabilities.
        """
        data = import_rows(self._schema, rows)
        if evidence is None:
            return self._server.logprob(data)
        else:
            ragged_evidence = import_rows(self._schema, [evidence])
            return (self._server.logprob(data + ragged_evidence) -
                    self._server.logprob(data + evidence))