compression - Sequitur算法的Python实现？

Question

我需要将 Sequitur 算法用于生物信息学项目，但到目前为止我还没有找到该算法的任何 Python 实现。这是我到目前为止所得到的:

    def new_rule(sequence,rules,x,y,z):
    y = list(y)
    if max(y) > 1:
        ind = y.index(max(y))

        locations = np.zeros(max(y))

        counter = 0
        for i, j in enumerate(z):
            if j == x[ind]:
                locations[counter] = i
                counter+=1

    new_rule = 'A'+str(len(rules)+1)
    rules.append([new_rule,x[ind]])

    return sequence.replace(x[ind],new_rule), rules

def word_count(sequence):
    return len(sequence)-sequence.count(' ')

def recursive_sequitur(sequence,rules):
    count_down = word_count(sequence)-1

x,y,z = n_grams(sequence,count_down)

while count_down>1:
    if len(y) > 1 and max(y) > 1:
        sequence, rules = new_rule(sequence,rules,x,y,z)
        x,y,z = n_grams(sequence,count_down)

    else:
        count_down-=1
        x,y,z = n_grams(sequence,count_down)

return sequence, rules

Answer 1

你见过Parallel Sequitur吗？对于 Python？

We implemented serial and parallel versions of the Sequitur compression algorithm. The Sequitur algorithm uses hierarchical structure and sequences of discrete symbols to compress files by exploiting repetative structures found in strings.

concat_parallel.py :Sequitur 算法的简单并行实现。在主进程按顺序合并所有结果之前，文本在运行串行算法版本的工作人员之间拆分。