在稀疏lil_matrix中找到最大值及其索引(Scipy/Python) [英] Finding maximum value and their indices in a sparse lil_matrix (Scipy/Python)

问题描述

在Scipy稀疏使用itertools.izip 遍历非零条目，但是还有什么更好的办法吗?我觉得我这里缺少明显的东西.

What's the best way to find the maximum value and their corresponding row and column indices in a Scipy sparse lil_matrix object ? I can loop through the nonzero entries using itertools.izip, but is there anything better ? I feel like I'm missing something obvious here ..

推荐答案

您可以转换为COO格式，然后使用data，row和col属性.

You could convert to COO format, and then use the data, row and col attributes.

例如，假设LIL矩阵为x.这是一种获取最大值及其行和列的方法:

For example, suppose the LIL matrix is x. Here's one way to get the maximum value along with its row and column:

In [41]: x
Out[41]: 
<1000x1000 sparse matrix of type '<type 'numpy.float64'>'
    with 1999 stored elements in LInked List format>

In [42]: y = x.tocoo()

In [43]: k = y.data.argmax()

In [44]: maxval = y.data[k]

In [45]: maxrow = y.row[k]

In [46]: maxcol = y.col[k]

注意:上面的代码中有两个错误:

Note: There are two bugs in the above code:

• 如果所有非零值均为负，它将找到最大的负值.但是在这种情况下，正确答案应该为0.
• 如果没有 no 个非零值，则k = y.data.argmax()行将引发异常，因为y.data是一个空数组.

• If all the nonzero values are negative, it will find the largest negative value. But the correct answer should be 0 in that case.
• If there are no nonzero values, then the line k = y.data.argmax() will raise an exception, because y.data is an empty array.

如果在您的应用程序中不会发生这些情况，那么可以忽略这些错误.

If those cases can't happen in your application, then those bugs can be ignored.

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