如何将共生矩阵转换为稀疏矩阵(How to convert co-occurrence matrix to sparse matrix)

 我开始处理稀疏矩阵，所以我对这个主题并不十分精通。 我的问题是，我从单词列表中得到一个简单的合并矩阵，只是一个二维共生矩阵，逐字逐句计算一个单词在同一个上下文中出现的次数。 由于语料库不是那么大，因此矩阵非常稀疏。 我想将它转换为稀疏矩阵，以便能够更好地处理它，最终在事后做一些矩阵乘法运算。 这里我到目前为止所做的（只有第一部分，其余部分只是输出格式和清理数据）： 
 
def matrix(from_corpus):    
d = defaultdict(lambda : defaultdict(int))
        heads = set() 
        trans = set()
        for text in corpus:
            d[text[0]][text[1]] += 1
            heads.add(text[0])
            trans.add(text[1])

        return d,heads,trans
 
 我的想法是创建一个新功能： 
 
def matrix_to_sparse(d):
    A = sparse.lil_matrix(d)
 
 这有意义吗？ 然而，这是行不通的，我不知道如何得到一个稀疏矩阵。 我应该更好地使用numpy数组吗？ 什么是最好的方式来做到这一点。 我想比较许多处理矩阵的方法。 
 
 如果有人能让我朝着这个方向发展，那将会很好。 

I am starting dealing with sparse matrices so I'm not really proficient on this topic. My problem is, I have a simple coo-occurrences matrix from a word list, just a 2-dimensional co-occurrence matrix word by word counting how many times a word occurs in same context. The matrix is quite sparse since the corpus is not that big. I want to convert it to a sparse matrix to be able to deal better with it, eventually do some matrix multiplication afterwards. Here what I have done until now (only the first part, the rest is just output format and cleaning data):
 
def matrix(from_corpus):    
d = defaultdict(lambda : defaultdict(int))
        heads = set() 
        trans = set()
        for text in corpus:
            d[text[0]][text[1]] += 1
            heads.add(text[0])
            trans.add(text[1])

        return d,heads,trans
 
My idea would be to make a new function:
 
def matrix_to_sparse(d):
    A = sparse.lil_matrix(d)
 
Does this make any sense? This is however not working and somehow I don't the way how get a sparse matrix. Should I better work with numpy arrays? What would be the best way to do this. I want to compare many ways to deal with matrices.
 
It would be nice if some could put me in the direction.

原文：https://stackoverflow.com/questions/15030047