是否有类似coo_matrix的东西，但矢量稀疏? [英] is there something like coo_matrix but for sparse vectors?

问题描述

我正试图从存在一些重叠索引的一系列数组中创建一个稀疏向量.对于矩阵，有一个非常

I'm trying to create a sparse vector from a series of arrays where there are some overlapping indexes. For a matrix there's a very convenient object in scipy that does exactly this:

coo_matrix((data, (i, j)), [shape=(M, N)])

因此，如果数据恰好具有重复的元素(因为它们的i，j索引相同)，则将这些元素累加到最终的稀疏矩阵中.我想知道是否可以对稀疏向量做类似的事情，还是只需要使用这个对象并假装它是1列矩阵?

So if data happens to have repeated elements (because their i,j indexes are the same), those are summed up in the final sparse matrix. I was wondering if it would be possible to do something similar but for sparse vectors, or do I have just to use this object and pretend it's a 1-column matrix?

推荐答案

虽然您可以复制1d等效项，但仅使用1行(或1 col)稀疏矩阵可以节省很多工作.我不知道numpy的任何稀疏矢量包.

While you might be able to reproduce a 1d equivalent, it would save a lot of work to just work with a 1 row (or 1 col) sparse matrix. I am not aware of any sparse vector package for numpy.

coo格式完全按照您给定的输入数组进行存储，不进行求和.当求和显示或(或以其他方式)转换为csc或csr格式时，即完成求和.而且由于csr构造函数已编译，因此求和的速度将比您用Python编写的任何代码都要快.

The coo format stores the input arrays exactly as you given them, without the summing. The summing is done when it is displayed or (otherwise) converted to a csc or csr format. And since the csr constructor is compiled, it will to that summation faster than anything you could code in Python.

构造一个"1d"稀疏coo矩阵

Construct a '1d' sparse coo matrix

In [67]: data=[10,11,12,14,15,16]    
In [68]: col=[1,2,1,5,7,5]
In [70]: M=sparse.coo_matrix((data (np.zeros(len(col)),col)),shape=(1,10))

查看其数据表示形式(不求和)

Look at its data representation (no summation)

In [71]: M.data
Out[71]: array([10, 11, 12, 14, 15, 16])
In [72]: M.row
Out[72]: array([0, 0, 0, 0, 0, 0])
In [73]: M.col
Out[73]: array([1, 2, 1, 5, 7, 5])

查看数组表示形式(形状为(1,10))

look at the array representation (shape (1,10))

In [74]: M.A
Out[74]: array([[ 0, 22, 11,  0,  0, 30,  0, 15,  0,  0]])

和等效的csr.

In [75]: M1=M.tocsr()
In [76]: M1.data
Out[76]: array([22, 11, 30, 15])
In [77]: M1.indices
Out[77]: array([1, 2, 5, 7])
In [78]: M1.indptr
Out[78]: array([0, 4])

In [79]: np.nonzero(M.A)
Out[79]: (array([0, 0, 0, 0]), array([1, 2, 5, 7]))

nonzero显示相同的模式:

In [80]: M.nonzero()
Out[80]: (array([0, 0, 0, 0, 0, 0]), array([1, 2, 1, 5, 7, 5]))

In [81]: M.tocsr().nonzero()
Out[81]: (array([0, 0, 0, 0]), array([1, 2, 5, 7]))

In [82]: np.nonzero(M.A)
Out[82]: (array([0, 0, 0, 0]), array([1, 2, 5, 7]))

M.toarray().flatten()将为您提供(10,)一维数组.

M.toarray().flatten() will give you the (10,) 1d array.

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