Dict.py 文件源码

def prune(self, size):
        if size >= self.size():
            return self

        # Only keep the `size` most frequent entries.
        freq = torch.Tensor(
                [self.frequencies[i] for i in range(len(self.frequencies))])
        _, idx = torch.sort(freq, 0, True)

        newDict = Dict()

        # Add special entries in all cases.
        for i in self.special:
            newDict.addSpecial(self.idxToLabel[i])

        for i in idx[:size]:
            newDict.add(self.idxToLabel[i])

        return newDict

    # Convert `labels` to indices. Use `unkWord` if not found.
    # Optionally insert `bosWord` at the beginning and `eosWord` at the .