Python 如何将“SciPy 稀疏矩阵”转换为“NumPy 矩阵”？
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How do I transform a "SciPy sparse matrix" to a "NumPy matrix"?
提问by Mr.Boy
I am using a python function called "incidence_matrix(G)", which returns the incident matrix of graph. It is from Networkx package. The problem that I am facing is the return type of this function is "Scipy Sparse Matrix". I need to have the Incident matrix in the format of numpy matrix or array. I was wondering if there is any easy way of doing that or not? Or is there any built-in function that can do this transformation for me or not?
我正在使用一个名为“incidence_matrix(G)”的python函数，它返回图形的事件矩阵。它来自 Networkx 包。我面临的问题是这个函数的返回类型是“Scipy Sparse Matrix”。我需要 numpy 矩阵或数组格式的事件矩阵。我想知道是否有任何简单的方法可以做到这一点？或者是否有任何内置函数可以为我进行这种转换？
Thanks
谢谢
采纳答案by sebix
The scipy.sparse.*_matrixhas several useful methods, for example, if ais e.g. scipy.sparse.csr_matrix:
该scipy.sparse.*_matrix有几个有用的方法，例如，如果a是例如scipy.sparse.csr_matrix：
a.toarray()ora.A- Return a dense ndarray representation of this matrix. (numpy.array, recommended)a.todense()ora.M- Return a dense matrix representation of this matrix. (numpy.matrix)
a.toarray()或a.A- 返回此矩阵的密集 ndarray 表示。(numpy.array, 推荐)a.todense()或a.M- 返回此矩阵的密集矩阵表示。(numpy.matrix)
回答by Aric
The simplest way is to call the todense() method on the data:
最简单的方法是对数据调用 todense() 方法：
In [1]: import networkx as nx
In [2]: G = nx.Graph([(1,2)])
In [3]: nx.incidence_matrix(G)
Out[3]:
<2x1 sparse matrix of type '<type 'numpy.float64'>'
with 2 stored elements in Compressed Sparse Column format>
In [4]: nx.incidence_matrix(G).todense()
Out[4]:
matrix([[ 1.],
[ 1.]])
In [5]: nx.incidence_matrix(G).todense().A
Out[5]:
array([[ 1.],
[ 1.]])
回答by user108569
I found that in the case of csr matrices, todense()and toarray()simply wrapped the tuples rather than producing a ndarray formatted version of the data in matrix form. This was unusable for the skmultilearn classifiers I'm training.
我发现，在CSR矩阵的情况下，todense()与toarray()简单地包裹所述元组，而不是以矩阵形式产生数据的ndarray格式化版本。这对于我正在训练的 skmultilearn 分类器是不可用的。
I translated it to a lil matrix- a format numpy can parse accurately, and then ran toarray()on that:
我将它转换为lil 矩阵- 一种格式 numpy 可以准确解析，然后运行toarray()：
sparse.lil_matrix(<my-sparse_matrix>).toarray()
