SpreadingActivation.py 文件源码

def _score(self, freq, scores, row, col, memoization=None):
        mem = memoization if memoization is not None else [False] * scores.shape[1]

        # memoization hit
        if mem[col]: return scores[row, col]

        children = self.hierarchy.successors(self.feature_names[col] if self.feature_names else col)
        if len(children) == 0:
            # Base case for leaves
            scores[row, col] = freq[row, col]
            mem[col] = True
            return scores[row, col]

        # recursively compute children score
        score = float(0)
        for child in children:
            child_idx = self.vocabulary[child] if self.vocabulary else child
            score += self._score(freq, scores, row, child_idx, memoization=mem)

        # scale them with some method specific factor
        if self.method in ["bell", "belllog"]:
            k = nx.shortest_path_length(self.hierarchy, self.root, self.feature_names[col] if self.feature_names else col)
            print(k+1, self.levels[k+1])
            print("Count of children:", len(children))
            denom = self.levels[k+1]
            if self.method == "belllog": denom = log(denom, 10) #TODO problem when zero
            score *= 1.0 / denom
        elif self.method == "children":
            score *= 1.0 / len(children)
        elif self.method == "basic":
            score *= self.decay 

        # add the freq of the concept just now since it should not be scaled
        score += freq[row, col]


        scores[row, col] = score
        mem[col] = True

        return scores[row, col]